A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard

Remote Sensing of Environment 240 (2020) 111679

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A smart multiple spatial and temporal resolution system to support precision agriculture from satellite images: Proof of concept on Aglianico vineyard A. Brooka, V. De Miccob, G. Battipagliac, A. Erbaggiod, G. Ludenoe, I. Catapanoe, A. Bonfantef,

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a

Spectroscopy & Remote Sensing Laboratory, Department of Geography and Environmental Studies, University of Haifa, Mount Carmel 3498838, Israel Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, I-80055 Portici, (Naples), Italy c Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "L. Vanvitelli", Via Vivaldi 43, I-81100 Caserta, Italy d Freelance e Institute for the Electromagnetic Sensing of the Environment, National Research Council, (IREA-CNR), Naples, Italy f Institute for Mediterranean Agricultural and Forest Systems -CNR-ISAFOM, National Research Council, Via Patacca, 85, 80056 Ercolano, NA, Italy b

A R T I C LE I N FO

A B S T R A C T

Keywords: CNN image reconstruction Pan-sharpening Vineyard plant status Dendro-ecological analysis Plant hydraulics Precision agriculture Sentinel-2A UAV Wood anatomy And isotopes

In this century, one of the main objectives of agriculture is sustainability addressed to achieve food security, based on the improvement of use efficiency of farm resources, the increasing of crop yield and quality, under climate change conditions. The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized through crop monitoring in the field, aiming to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high-quality wines, the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture. Satellite multispectral images have been widely used for production management in large areas. However, their observation is limited by the pre-defined and fixed scale with relatively coarse spatial resolution, resulting in limitations in their application. In this paper, encouraged by recent achievements in convolutional neural network (CNN), a multiscale fullconnected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images. The reconstructed data are validated by independent multispectral UAV images and in-situ spectral measurements. The reconstructed Sentinel-2A images provide a temporal evaluation of plant responses using selected vegetation indices. The proposed methodology has been tested on plant measurements taken either in-vivo and through the retrospective reconstruction of the eco-physiological vine behavior, by the evaluation of water conductivity and water use efficiency indexes from anatomical and isotopic traits recorded in vine trunk wood. In this study, the use of such a methodology able to combine the pro and cons of space-borne and UAVs data to evaluate plant responses, with high spatial and temporal resolution, has been applied in a vineyard of southern Italy by analyzing the period from 2015 to 2018. The obtained results have shown a good correspondence between the vegetation indexes obtained from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based retrospective reconstruction of vine eco-physiological behavior.

1. Introduction Sustainable agriculture is one of the main objectives of this century. United Nations and FAO, through the Sustainable Development Goal 2 (SDG2 -Zero Hunger) and the Sustainable Crop Production Intensification (SCPI) Strategic Objective A of FAO STRATEGIC FRAMEWORK 2010–2019 (FAO, 2009), underline the need to improve farm

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resource use efficiency (i.e. water and nutrients) as the sole strategy able to increase crop production and quality, face climate change and achieve food security. The achievement of sustainable agriculture is grounded on the knowledge of the agricultural system (soil, climate, crop) and the use of tools able to support the farmer's management strategies (e.g., Decision Support System -DSS, Terribile et al., 2015; LCIS, Bonfante et al., 2019).

Corresponding author. E-mail address: [email protected] (A. Bonfante).

https://doi.org/10.1016/j.rse.2020.111679 Received 15 May 2019; Received in revised form 7 January 2020; Accepted 22 January 2020 0034-4257/ © 2020 Elsevier Inc. All rights reserved.

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tool, vegetation maps are obtained at low-cost and facilitating imagery interpretation for production and management decisions. However, up today most of the studies have been carried out over crops with a full canopy cover (no soil surface in the scene), as recently shown (Polinova et al., 2018), neglecting other cases, such as vineyard production with a constrained canopy cover and very limited spatial presence in any remotely sensed data collected above the plot (UAVs in particularly small drones, UAS, airborne, or space-born platforms). Since the drone's low-cost applications cannot support continuous temporal data collection at regular time intervals (preferably days apart), and provide SWIR spectral data (in most of currently commercial sensors), the applicable solution is restricted to VIs in VIS-NIR regions that might address spatial variability of red radiation absorption by chlorophyll within a plot. The vegetation/canopy water content cannot be accurately assessed without the SWIR absorption features. Since the canopy water content is a product of leaf area index (LAI) and leaf water content (e.g., Momen et al., 2017), it can be estimated by NIR-SWIR VIs that are sensitive to both LAI and leaf water content, e.g. the normalized difference infrared index (NDII) and the normalized difference water index (NDWI). As reported in previous studies (Hunt et al., 2018), the NDII is linearly correlated with canopy water content, yet the stem water content was not addressed in these studies. Many studies (Ezenne et al., 2019) showed that leaf water content (according to plant species or plant functional type) is highly empirically correlated to NIR-Red indices (e.g., Normalized Difference Vegetation Index NDVI). The NDVI is the index of plant greenness expressed as the normalized difference between the red and NIR regions. Differences in illumination corresponding might bias its results to topographical conditions (aspect and slope) or by the time at which images were obtained. A recent study (Fuentes-Peailillo et al., 2018), compared the level of performance and accuracy retrieved by multispectral and drone RGB sensors for vineyard production. It concluded that both the VIS (RGB) indices and VIS-NIR, in particular, NDVI, allowed to obtain similar spatial patterns, and thus, suggested using drone RGB VIs as a low-cost tool for identification of the spatial variability of the crop plants. Moreover, numerous studies (Deng et al., 2018) showed that VIs could retrieve coefficient of determination with stem water potential and leaf stomatal conductance reflecting cumulative water deficit and long-term response to water status. Yet, the majority of these studies presented plot-based measurements simultaneously acquired with drone remote sensing data, without considering variation and bias contributed by soil type and soil moisture in the relationship between canopy water content and VIs. Furthermore, recent studies (Romero et al., 2018) showed that plant water status is not accurately predicted with VIs in the VIS-NIR region due to their non-sensitivity to water content in crops. However, spectral regions > 800 nm retrieve more accurate results. Many studies noted that satisfactory estimation of leaf water potentials at canopy water content can be derived using VIs (e.g., Ali et al., 2017) based on the NIR-SWIR domain with specific spectral optimization. Considering that the water content of leaves and stems is proportional to dry mass, the stem water content is allometrically associated with canopy water content, which might be monitored by VIs (mainly established for monitoring canopy stresses). As the allometric relationships between canopy and stem water content are vegetation dependent, altered relationships between canopy water content and VIs are foreseen. Moreover, it is known that in woody species the stem holds larger water amount than leaves and also functions as water reservoir thus possibly buffering daily and seasonal variations in stem water content (Kramer, 1983). However, the contribution of stored water in the stems to water use of the various organs, as well as the patterns of water use dynamics, depend on various factors (including the species, plant age and phenological stage) but have not been completely unravelled yet (di Francescantonio et al., 2018). Therefore, any biases between stem and canopy water content might have large impacts on further estimations. A pioneering study (Romero et al., 2018) showed highly

The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized by means of field monitoring (proximal or remote sensing; e.g. tensiometers, flying devices collecting spectral imagery data) able to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high quality wines, the capability to manage and follow spatial behavior of vines during the season represents an opportunity to improve the harvesting and delivering to cellar, to guarantee uniform and stable production in terms of quality, pointing towards the identification of new products and increasing farmer incomes. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture. An opportunity for the agricultural sector is the use of spectral space-borne data collected via different satellite platforms (e.g., Sentinel via Copernicus, Proba, PlanetScope, Venμs) which vary in spatial resolution (1 km to 10 m, and less) and spectral configurations (bands across visible VIS, near-infrared NIR, shortwave infrared SWIR and thermal infrared TIR regions), with regular time intervals (monthly to daily). For many years, the occurrence and health analysis of vegetation have been determined with a low spectral resolution in the VIS-NIR range which is related to chlorophyll in green vegetation (Xue and Su, 2017). In satellite-based remote sensing, these parameters are assessed by vegetation indices (VIs) calculated from spectral information (based on spectral bands). However, many studies (Deng et al., 2018; FuentesPeailillo et al., 2018) draw attention to several limitations of the satellite-based remote sensing data in agriculture applications (e.g., Anderson et al., 2016). Meteorological conditions explain these limitations (e.g., clouds and fog that prevent or restrict the usability of the satellite-based remote sensing data at local scale for monitoring of crops in space and time) and by its spatial resolution. The spectral spaceborne data are limited to the predefined scale in which the instruments record the data, regardless of whether these scales are appropriate for the analysis (Dark and Bram, 2007). Since the scale of features greatly varies among different ground objects, multiscale methods for feature extraction from multiple sensors are needed (Riihimäki et al., 2019). Recently, numerous advanced technologies based on UAVs and UAS imagery data have been developed to provide an alternative source of information to estimate biophysical crops' parameters with higher spatial and spectral resolutions. The data might be collected in various spectral configurations, obtaining images across VIS, NIR, and TIR regions through true color cameras (RGB), multispectral and thermal sensors at a shorter distance from the crop. Nevertheless, a continuous observation and monitoring in practical-oriented applications might become a complex task; moreover, only the RGB sensor should be considered as a low-cost application. In precision agriculture, the spatial and temporal variability of the crop conditions would benefit from detailed spectra that commonly used sensor technologies (UAV and UAS) doesn't provide (spectrally limited survey, 4 to 16 bands across VIS-NIR region of electromagnetic spectrum). Considering the issues mentioned above regarding also payload limitation and abundance of prominent features, it is clear why there is need for the development of drone remote sensing (low-cost systems) mainly targeting agricultural and environmental monitoring applications in general and precision agriculture in particular (Aasen et al., 2018). The increased needs and demands for precision agriculture in reliable, accurate, and fast in-time interpretative data are fulfilled by drone remote sensing in the VIS-NIR range of electromagnetic spectrum. The foundation of most of UAV-based methods to monitor crops corresponds to the computation of the VIs, historically developed for satellite-based imagery data. The VIs products are a useful tool for monitoring, analyzing, and spatial mapping variations in vegetation structure as well as numerous biophysical parameters that can be vastly obtained in a short time (e.g., Hashimoto et al., 2019). Using this simple 2

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images with MS images to produce an image with both high spatial and spectral resolutions. The most advanced approaches in this domain are developed via deep learning models and assembled with multiple transforming layers stacked to form a total transformation with high nonlinearity characteristics to MS and PAN data in general and to the data collected above vineyards in particular. Owing to their low and unfixed operating altitude, UAVs and UAS systems can produce high-resolution data. Yet these tools cannot provide spectral information across the SWIR region, the data fusion technique is needed. Thus, the high-resolution data can be used as PAN image for pan-sharpening the MS data by the CNN architecture enabling vineyard assessment at a high spatial resolution. In this study, the utilization of the CNN image reconstruction approach for estimating vine growth performance is demonstrated. The proposed methodology was tested by comparing the output of the CNN image reconstruction approach with the data about the real eco-physiological behavior of vines reconstructed as well through the application of dendro-ecological techniques. The latter, borrowed from the forest science domain, can be useful to reconstruct past vine growth and eco-physiology through the retrospective analysis of signals hidden in tree-ring series and have been recently applied to grapevine to highlight different growth strategies linked to cultivation management (De Micco et al., 2018). Indeed, wood is an archive recording anatomical and isotopic traits (signals) which are the results of intrinsic factors and environmental drivers, as climate, soil, and cultivation management (Farquhar et al., 1989; Voltas et al., 2013; Schweingruber, 1996; Cirillo et al., 2017). The analysis of tree rings in vines allows calculating wood anatomical and isotopic traits (especially δ13C and δ18O) about water conductivity and water use efficiency that are parameters linked with several processes in plant metabolism including: gas exchanges and photosynthesis (the carbon source), biomass accumulation through the activity of vascular cambium (one carbon sink), plant hydraulics and resource allocation (Farquhar et al., 1989; Scheidegger et al., 2000; Cernusak et al., 2003; Saurer et al., 2004; Sperry et al., 2006; Barbour, 2007; Roden and Farquhar, 2012; Beeckman, 2016; De Micco et al., 2019). The vine water status is traditionally evaluated through the measurement of water potentials in leaves and stem, although attempts to evaluate it through carbon isotope composition have been recently increasing (Scholander et al., 1965; Dixon et al., 1984; Gaudillère et al., 2002; Brillante et al., 2018). The analysis of carbon isotopes in must has been shown to be linked with water availability and is considered a continuous integrator of the vine water status throughout the ripening period, although still controversial results are reported (Brillante et al., 2018). However, the main added value of dendro-sciences (e.g., dendroanatomy and dendro-isotopes) is in their potential to evaluate the spatial and temporal variability of plant eco-physiological behavior at a fine scale, going back in time with inter- and intra-seasonal resolution. However, the suggested multi-disciplinary approach is based on highresolution space-borne multispectral remote sensing (below 2 m resolution) able to provide information on: plant behavior, integrating processes involved in soil-plant-atmosphere system (in particular the plant water and nutrient availability driven by soil spatial variability, Bonfante et al., 2017b) coupled with the application of dendro-sciences (which reconstruct plant water use). In this context, the study of soil spatial variability was supported by means of a georadar survey able to perform non-invasive sub-surface investigation devoted to gathering information about soil stratigraphy and buried targets, among which tangles of roots. The approach proposed herein was conceived to improve the fusion of drone and satellite-based imagery data, to advance the pan-sharpening methods retrieving high spatial resolution MS data, and to develop an unconventional monitoring framework of plant stress. To exploit the advantages of deep learning network, for image restoration and reconstruction, a real study case was identified in a winery of southern Italy (Fonzone-Caccese), where a differentiation along the

accurate estimations of stem and canopy conditions using NDII and a land-cover classification map. The study highlighted the importance of plant-based measurements rather than plot-based measurements and demonstrated cases in which the satellite and airborne remote sensing data are simply unable to produce accurate enough data for the allometric relationship analyses. These statements raise the need for detailed and multiscale observation approaches and accurate photogrammetric reconstructions methods. The multiscale capabilities of UAVs and UAS systems make boundless the ability of remote sensing to fulfill these needs. Its ultra-high spatial resolution is producing big data that supports many machine learning (ML) applications. ML algorithms are a very powerful tool for characterizing objects and events in precision agriculture (e.g., Delgado et al., 2019; Spachos and Gregori, 2019) that are critical for understanding the drivers affecting the evolution of the system on a local or regional scale (Kamilaris and Prenafeta-Boldú, 2018). While traditional approaches for characterizing crop objects and events are primarily based on the use of hand-coded features (e.g., ad-hoc morpho-physiological measurements), ML algorithms provide automated detection from data with improved performance using pattern-mining techniques. However, these approaches are not yet applicable to the Spatio-temporal objects and events with amorphous boundaries and their associated uncertainties, since their pattern-mining approaches should account for the spatial and temporal properties at once. Many recent supervised ML algorithms are used to analyze remote sensing data and produce estimates of crop health variables and variation at moderate spatial scales and at regular intervals of time. These tasks are supported by the multispectral space-borne (satellite-based) data. The advanced studies in precision agriculture developed artificial neural network (ANN) models derived from multispectral images to forecast spatial variability of stem water content of crops. Recent studies (Poblete et al., 2017; Romero et al., 2018) classified the water status level of the grapevine based on ten vegetation indices across VISNIR region and found no significant relationships between VIs individually and stem water content, further proposed ANN model reported correlation between VIs across VIS-NIR region with stem water content. However, these results are scene dependent as they were trained by plot-based measurements and highly mixed soil-plant spectral data. ANN is a well-known approach in scientific and applied research, starting from the perceptron algorithm in the 1960s to contemporary “deep” architectures with a deep hierarchy of latent features that convert complex features to compositions of simpler features. The deep learning methods are automatically extracting relevant features from the data, thus are beneficial for problems where it is challenging to create hand-coded features for objects, events, and relationships or for multiscale data sources. This framework is mainly accomplished using convolutional neural networks (CNN). The CNN framework is primarily trained to learn recognizable objects and further apply its knowledge to recognize other types of objects and events. This net has also been explored for downscaling outputs of physical models and generating patterns at local scales, and classifying objects in high-resolution satellite images. Recently the CNN architecture was implemented on space-borne remote sensing data for multiscale pan-sharpening (e.g., Zhao et al., 2017; Ghamisi et al., 2018). The pan-sharpening concept is long-known in the remote sensing community, and its purpose is to overcome the main drawback of the multispectral space-borne data, which is its spatial resolution. Pan-sharpening aims at fusing a multispectral (MS) and a panchromatic image (PAN), featuring the result of the processing with the spectral resolution of the MS and the spatial resolution of the PAN. In the last decades, many algorithms addressing this task have been presented in the literature, discussing the technical limitations of sensors and other factors, for which remote sensing images with both high spatial and spectral resolutions are currently unavailable. Therefore, major efforts are addressed to pan-sharpening, which fuse PAN 3

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The long-term (2003−2013) mean daily temperature annually of the study area was 14.7 ( ± 0.9) C, while the mean annual rainfall was 802 ( ± 129) mm (data from the regional weather station of Mirabella Eclano, Avellino, 10 km away from the study area). During the grapevine growing season (April–October), the average daily temperature is 19.7 ( ± 4.1) °C, with a maximum and minimum absolute daily value of 39.5 °C ( ± 1.7) (during the summer, July–August) and 0.7 °C ( ± 1.4) (70% of cases in April and 30% in October) respectively. The rainfall amount ranged from 205 to 534 mm, with an average value of 320 ( ± 112) mm, while the reference evapotranspiration ranged from 548 to 1066 mm with an average value of 812 ( ± 140) mm. In 2015, an important differentiation in plant behavior was described along the slope, which influenced the vineyard management and farmer activities (e.g. soil tillage, pruning, harvesting). More specifically, the following parameters were measured: number of spurs and buds per vine (after winter pruning), percent of bud burst and number of total shoots (after bud burst at BBCH 09–10), true fertility and number of bunches per vine (after blooming), yield per plant, berry fresh weight, soluble sugars (Brix°), total acidity and phenolics content in skins and grapeseeds (at harvest). The content of soluble sugars and acidity was measured following the methods by the International Organisation of Vine and Wine (ref. to OIV, 2019), while phenolics were quantified according to Mattivi et al. (2002) and Harbertson et al. (2003). Vines growing in the upper part of slope (US) were characterized by lower vegetative vigor and productivity if compared with those growing in the lower part (LS) (Table 1; Fig. 2). US vines were also characterized by lower yield: number and weight of bunches were lower than in LS vines. Finally, berry composition was statistically different in bunches from LS than from US in terms of soluble sugars, acidity, and accumulation of phenolics in both skin and seeds.

Fig. 1. Study area in southern Italy on Aglianico vineyard (Fonzone-Caccese farm).

vineyard rows of Aglianico grapevine was recorded in 2015. In particular, the proposed multiscale CNN image reconstruction was applied at field scale and evaluated at sub-plot scale representative of vineyard behavior. The main aim was to investigate and establish the relationship between soil water availability for plant, water flow and use efficiency in plants, with water plant status detection across SWIR region, vegetation pigments across VIS region and red edge/leaf structure across NIR region VIs derived from pan-sharpened/reconstructed temporal Sentinel-2A imagery.

2. Materials and methods 2.1. Study area

2.2. Data collection

The study area is located in a hilly environment of southern Italy (Paternopoli, Avellino, Campania region: lat 40.961426°, lon 15.062929°, 401 m a.s.l.), in the Fonzone-Caccese farm which is oriented to the production of high-quality wines (about 20 ha) (Fig. 1). The study area is included in the “marls and stone/carbonate hills” landscape system (D3). The only information on soil types is available on a rather coarse scale: a soil-landscape map of the whole Campania region at a 1: 250,000 scale (Di Gennaro et al., 2002). The main soil types of the Paternopoli area are identified as Haplic Calcisols. The vineyard studied was an Aglianico cultivar (controlled designation of origin – DOC/AOC), planted in 2006 with E-W row orientation and 2.2 × 1 m spacing (≈ 4545 vines/ha) and placed along a slope of 120 m length, with a 9% gradient. Vines are trained to a verticalshoot-positioned trellis system with a unilateral cordon. The support structure is made of woody posts (12 × 12 cm section as end posts and 8 × 8 cm every 5 mt on the rows).

2.2.1. Vineyard row characterization: georadar and soil survey A pedological survey supported by georadar survey was applied to study the soil spatial variability along a selected row representative of slope vineyard, where data collected in 2015 evidenced the different vine behavior between US and LS. This step is very relevant in order to compare plant responses derived from Sentinel-2A imagery reconstructed by the multiscale CNN approach and dendro-traits, considering the soil spatial variability (Bonfante et al., 2017a, Brillante et al., 2015). Ground Penetrating Radar (GPR) is a special kind of radar for noninvasive detection and localization of targets hidden into optically opaque media, such as material interfaces and localized objects, by exploiting the ability of microwaves to penetrate non-metallic materials and interact with targets herein located. The GPR sensing principle is as follows: a transmitting (Tx) antenna radiates an electromagnetic signal into the probed medium, and when the radar wave impinges on a target

Table 1 Plant responses and bunch characteristics at harvest in 2015 (avarage value ± standard error). Plant responses/bunch characteristics

Sampl.

Spurs/vine Buds/vine Bud burst (%) Total shoots True fertility (n bunches/total shoots) Bunches/vine Yield/vine (gr) Berry fresh weight (g 100 berries) Brix° pH Total acidity (g l−1 tartaric acid equivalent) Phenolics in skin (mg kg−1 of grape) Phenolics in grapeseeds (mg kg−1 of grape)

(n = 20)

(n = 3)

4

Upper part of slope

Lower part of slope

5.23 ( ± 0.176) 10.4 ( ± 0.432) 1.81 ( ± 0.041) 11 ( ± 0.406) 1.41 ( ± 0.056) 14.46 ( ± 0.545) 1807 ( ± 128.28) 166 ( ± 3.24) 22.2 ( ± 0.161) 2.64 ( ± 0.008) 8 ( ± 0.028) 3348 ( ± 12.82) 1297 ( ± 7.447)

5.33 ( ± 0.253) 10.7 ( ± 0.5) 2.27 ( ± 0.043) 10.4 ( ± 0.444) 1.09 ( ± 0.061) 11.47 ( ± 0.568) 640.6 ( ± 38.471) 140 ( ± 1.38) 21.5 ( ± 0.04) 2.8 ( ± 0.016) 6.3 ( ± 0.028) 3120 ( ± 16.98) 1042 ( ± 3.348)

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to sample 5 vines per sub-plot (US vs LS) for tree-ring analysis. Although this number is lower than the minimum number of sampling (10 plants) of standard dendrochronology to produce a climate reconstruction, it was enough for our specific aims of comparing the two hydraulic systems as already proven in other vineyards (De Micco et al., 2018). 2.2.2.2. Tree rings. Wood cores were seasoned in a fresh-air dry store and sanded with different grain size paper. To improve the distinctness of tree-ring boundaries, their surface was smoothed out by cutting sections through a sliding microtome. Tree-ring width (TRW) measurements were made at a resolution of 0.01 mm, using LINTAB measurement equipment fitted with a stereoscope and equipped with TSAP Win software (Frank Rinn, Heidelberg, Germany). Tree-ring series were visually cross-dated and compared using standard dendrochronological techniques (Stokes and Smiley, 1968). We used the Gleichläufigkeit (GLK) as a measure of similarity between two chronologies based on the first difference between successive tree rings (Eckstein and Bauch, 1969; Schweingruber, 1988). A mean GLK of 65% was obtained for the chronologies. Finally, the years from 2015 to 2018 were selected, and wood growth was assessed by calculating the Cumulative Basal Area Increment (Cumulative BAI) for those years.

Fig. 2. Aglianico grapevine in the middle of September 2015 along the field slope (Upper part of Slope -US; Lower part of Slope -LS).

2.2.2.3. Wood anatomical traits. Semi-thin (15–20 μm) cross sections from each core were obtained through the sliding microtome and mounted on slides with mineral oil for fluorescence microscopy. The sections were then observed under an epi-fluorescence microscope (BX60 Olympus, Germany) equipped with a mercury lamp, 330–385nm band-pass filter, 400 nm and above dichromatic mirror, and 420 nm and above barrier filter. These settings allow detecting the autofluorescence of lignified cell walls (Fukuzawa, 1992; Ruzin et al., 1999). Digital images of the sections were collected with a digital camera (XC50, Olympus) at different magnifications to obtain digital images of whole tree rings corresponding to the years from 2015 to 2018. The microphotographs were analyzed by means of the Cell F 3.4 (Olympus) software program to quantify wood anatomical traits. Measured parameters included: ring width (in 6 points per each ring) and vessel lumen area (in all vessels detected within each ring). Vessels were then classified into classes of lumen area and the incidence of the different size-classes over the total conductive area was evaluated. Finally, the hydraulic diameter (Hd) and potential hydraulic conductivity (Kh) were calculated taking into account the Hagen–Poiseuille lumen theoretical hydraulic conductivity for a vessel size (Tyree and Zimmermann, 2002). More specifically, Dh was estimated as:

(i.e., an electromagnetic perturbation into the medium), part of the energy is reflected/backscattered and captured by the receiving (Rx) antenna, which collect the received signal into a fixed observation time windows. Therefore, by moving Tx and Rx antennas along a line and by collecting, at each measurement point, the backscattered field as a function of the round trip travel time, i.e. the signal propagation time along the path Tx antenna - target - Rx antenna, a 2D image of the surveyed scenario, referred as radargram, is obtained. This image is referred to as radargram and shows on the horizontal axis (x-axis) the measurement points and on the vertical axis (z-axis) the round trip travel time, t, which is converted into depth, z, according to the following relation.

z=

ct 2 εb

(1)

wherein c = 3 ∗ 10 m/s represents the light propagation velocity and εb is the average relative dielectric permittivity of the investigated medium, which is supposed to be homogeneous. The radargram provides a coded representation of the subsurface features, wherein localized objects appear as hyperbola, while material interfaces as constant signals (Daniels, 2004; Catapano et al., 2019). In this paper, GPR was adopted to gather information about the soil stratigraphy as well as about the rooting depth of the vines. The survey was performed by means of the time domain, IDS manufactured, RIS K2_FW GPR system, equipped with a single fold shielded dual frequency antenna, whose nominal central frequencies are 200 MHz and 600 MHz, an odometer and a carriage, to cover easily long distance while gathering the position of the measurement point along the acquisition vineyard row. After the Georadar analysis, soil survey (soil profiles descriptions, minipit and augering) was realized. Soil profiles were described according to IUSS (IUSS Working Group WRB, 2014). The grain size distribution (GSD) was determined by a laser granulometer (Malvern Mastersizer, 2000) and the chemical analyses were performed according to the official methods of the Italian Ministry of Agriculture and Forestry (Colombo and Miano, 2015). 8

Σd5/Σd4

(2)

where d is the lumen diameter of each vessel (Sperry et al., 1994). The Kh was calculated as:

Kh = (ρ × π × Σd4)/(128 × μ × Ar)

(3)

where ρ is the density of water at 20 °C (998.2 kg m − 3 at 20 °C), d is the vessel lumen diameter, μ is the viscosity of water (1.002 × 10–9 MPa s, at 20 °C) and Ar is the wood area analyzed (Fan et al., 2012). 2.2.3. Isotope measurement Rings from 2015 to 2018 were divided annually and grounded with a centrifugal mill (ZM 1000, Retsch, Germany) using a mesh size of 0.5 mm to assure homogeneity. The C stable isotope composition was measured by continuous-flow isotope ratio mass spectrometry (Delta V plus Thermo electron corporation, Bremen Germany) using 0.06 mg of dry matter for δ13C. WUEi for each ring was derived from tree-ring δ13C values. WUEi is defined as the ratio of the rate of carbon assimilation (A, photosynthesis) and stomatal conductance (gs); it can be calculated as:

2.2.2. Wood characterization in trunk 2.2.2.1. Wood sampling. Common dendro-ecological techniques to build tree-ring chronologies were applied. Core sampling was carried out in January 2019, passing the trunk from side to side using a small increment borer (diameter 5 mm) at a 30 cm height. We were allowed 5

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WUEi = A/gs = ca ∗ [(1 − (Δ–a/b–a))1/1.6]

(4)

with Δ = (δ13Cair –δ13Cplant )/(1 + δ13Cplant )

(5)

per measured spectrum. Each target was measured systematically by collecting about 10 points on a plant to the direction of the flight. All points in a designed matrix were about 1 m distant from each other and the spectral measurement was taken from 1 m height with a bare-optics of a 24° field of view (FOV) (about 60 cm2 footprint on soil and 10 cm2 on a plant) with spectral error (standard deviation).

where, Δ is the carbon isotope discrimination, ca is atmospheric CO2 concentration; δ13Cair is the isotopic ratio of atmospheric CO2; δ13Cplant is the isotopic ratio of the plant; a (4.4‰) is the fractionation due to diffusion, and b (27‰) is the biochemical fractionation (Farquhar et al., 1982; Farquhar et al., 1989). Variation of ca and δ 13 Cair values were obtained from the records from Mauna Loa, Hawaii (http://cdiac.ornl.gov/). Anatomical and isotopic results were subjected to statistical analysis (ANOVA) using the SPSS 13.0 statistical package (SPSS Inc., Chicago, IL, USA). Shapiro–Wilk and Kolmogorov– Smirnov tests were performed to check for normality.

2.3. Data preparation 2.3.1. UAV imagery preprocessing 2.3.1.1. Geo-referencing. The images were processed via structure from motion (SfM) method (Boon et al., 2016), implemented in Pix4D Mapper Pro (v. 4.3.31), which completes all the main SfM steps. Prior processing the images, an automatic image quality module (in MATLAB using the horizontal and vertical components of the Sobel edge filter before inputting to the SfM workflow) identified and removed all blurry images from the data folder (detailed explanation for blurry images by Sieberth et al., 2016). The SfM workflow starts with feature identification, followed by feature matching, the camera model optimization, and the final step is bundle block adjustment (Küng et al., 2012). The point density option was set to ‘optimal’ and the minimum number of matches was set to three using a matching window size of 9 × 9 pixels. The accuracy of Phantom 4pro based digital surface model (DSM) was 2 cm in the horizontal (X, Y) coordinates, and < 3 cm in the vertical (Z) coordinate, calculated for 5 validation GPS points that were measured in the field but were not used in Pix4D model.

2.2.4. UAV imagery The drone remote sensing data were collected between July and September 2018 by DJI Phantom 4 Pro quadcopter and its standard camera, and RedEdge-MX Micasense camera (5 bands data). The DJI Phantom 4 Pro is a low-weight ready-to-fly system, and its camera has 12.4 million effective pixels giving a pixel size of 1.2 cm at a low flight altitude (~30 m from the ground) with a ground speed of 3 ms−1. The drone MS data were acquired by Matrice 600 Pro DJI, surface radiance of the vineyard was measured using the RedEdge-MX 5-band camera with a pixel size of 2 cm. The UAVs cameras were stabilized in pitch, roll and yaw by a three-axis gimbal and followed pre-programmed flight plans to assure full covering applying a frontal overlap of 90% and an adjusted side overlap (by a number of flight) using an autopilot module of Pix4D capture application. The image sequences were collected in perpendicular flight lines by using the ‘double grid’ option of the autopilot software. The absolute vertical and horizontal accuracy is comparable to the GPS device (several meters). This might be significantly improved by means of dGPS system in the field (to cm level). For that purpose, 10 ground control points were measured in total, well distributed throughout the study area and near visible horizontally and vertically important objects (pillars, weather station, road crosses, etc.).

2.3.1.2. Radiometric and atmospheric correction. Radiometric correction procedure that converts the raw (digital numbers DN) sensor values into useful data was performed by the following steps: 1) sensor-related radiometric and spectral calibration, 2) atmospheric radiative transfer model and irradiance measurement, and 3) bidirectional reflectance distribution function (BRDF) correction. The sensor-related radiometric calibration joint the relative radiometric, spectral and absolute radiometric calibrations into a harmonized workflow. The recorded dark images during the data acquisition were used to normalize DNs to uniform response in image space (entire scene) and time (operational period). The spectral and absolute radiometric calibrations were performed under laboratory conditions, taking into consideration that the spectral and radiometric performances can be different in laboratory and actual flight environments. Despite the popular claim across the UAVs/UAS community that absolute radiometric calibration can be omitted in cases where only reference is needed, these calibrations are crucial steps to the complete conversion of DNs to useful and realistic physical/spectral data. Thus, the raw data must be transformed into physical unit radiance (W m−2 sr−1 nm−1) for each spectral band. The atmospheric radiative transfer (MODTRAN) model provides a simulated irradiance (from the sun to top of the canopy) and radiance (ground to the sensor) responses using basic parameters, e.g. time, date, location, temperature, humidity, and aerosol optical depth. To improve the performance of the model, a stationary radiance measurement against the Spectralon panel on the ground was performed. The ground radiance (also reflectance) measurements were radiometrically and spectrally cross-calibrated with the simulated radiance. Further, the retrieved coefficients were used for atmospheric correction. The suggested method is similar to the SVC correction method (Brook and Ben Dor, 2011) with the BRDF module (Brook et al., 2018). The BRDF correction was performed by the empirical model of non-linear radiometric angle-dependent correction coefficients relying on phenomenological physics of the radiative transfer model with empirical local modifications. The last preparation stage was up-scaling the drone orthomosaic by aggregation to a desired resolution that enabled multi-scale crop assessment in vineyards. The high resolution imagery data were subjected to the pan-sharpening CNN network after upscaling to a resolution of

2.2.4.1. Sentinel-2A. The acquisition of both UAV and satellite imagery was nearly simultaneous, taking place on the same day in July 2018, but slightly different hours. The image was obtained from Copernicus Sci Hub as a Level 2A product. Atmospheric correction was performed by using the sen2cor (v. 2.4.0) software provided by ESA (Main-Knorn et al., 2017). The level-2A processing is applied to granules of Top-OfAtmosphere (TOA) to bottom-of-atmosphere (BOA) conversion with empirical BRDF correction for Level-1C ortho-rectified reflectance products starts with the scene classification, retrieval of the aerosol optical depth and the water vapor content. The modified pansharpening scheme proposed by (Park et al., 2017) was applied on level 2A images with spatial resolutions of 20 m and 60 m producing band-layer stack with spatial resolution of 10 m across VNIR-SWIR regions. 2.2.5. Ground-truth spectral data The ground spectra of the validation targets were measured with the portable field spectrometer OceanOptics USB4000 and NIR-FLAME which consists of hundreds of wavelengths ranging between 350 and 1800 nm, with bandwidths of 0.5 nm in the VNIR region (350–1100 nm) and 10 nm in SWIR region (950–1800 nm), and wavelength accuracy of ± 1 nm/ ± 5 nm. Each ground target (the background surface and the targets to be validated) was measured by averaging 40 spectra of both radiance and reflectance values during the overpass. The reflectance mode was calibrated against a Spectralon® white reference panel. The optimization procedure was programmed to work in both radiance and reflectance modes, averaging 40 replications 6

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(Sentinel-2A and drone PAN) through filtering layers adding lateral connection to preserve detailed spectral data at the spatial level. In the proposed network, the latent image is up-scaled gradually to the same spatial resolution as the MS image. Lastly, a 3 × 3 convolutional layer added on each merged map to minimize the aliasing effect of the upsampling operation. Moreover, a PAN image used to refine the feature map (spatial information) of the latent image at each scale and expanding the number of feature maps of the corresponding scale to the same as that of the latent image using a 1 × 1 convolution layer to get the final feature maps. The last deconvolution layer used for up-scaling and assures that the latent image will fit the spatial size of the PAN image. The new (upscale) latent image is fed into the last convolution layers to recover local-level spatial information and to transform the spectral information (according to the number of low resolution MS bands). The deep network performed on down-sampled and interpolated MS data, as it considers the observed MS imagery as the targeted imagery at the first iteration. The first kernel was 7 × 7 for 12 spectral bands produces hundreds of feature maps to better exploit the spectral details at this stage. The shallow network performed on upsampling MS imagery as the spatial details are targeted. It upgrades the spatial details for spectral data preserved by the deep information stream that able to extract complementary descriptors for multi-sources information collected from same pixel from different remote sensing platforms. The spectral features of the down-sampling are enhanced by lateral connections, which merge the output of the up-sampling with the down-sampling information streams. Each lateral connection merges spectral feature maps of the same spatial level. The learning process terms search for optimal allocation of all parameters by iteratively updating all the kernels and lateral connections. This process completed once the loss between the reconstructed product and the spatial details on PAN (ground-truth) image reach acceptable convergence, applying an accurate end-to-end function is obtained. The loss function (quality assurance of the proposed architecture) described by the mean square error is the optimal solution to obtain a higher peak signal-to noise ratio and also the optimal super parameter configurations. The end-to-end function can be further applied on the MS data. The main advantage of the suggested method is the ability to encode spectral information of MS image into a latent image and then decode the latent image with a PAN image into the high resolution MS image. The main drawback of the suggested method is its spatial dependency, thus, the network can be successfully applied on the temporal series MS images from the trained site.

0.8 m and converted to greyscale product as PAN image. This stage completed the drone remote sensing data preparation, as the main contributions of PAN data for further textural and recognizable detailed objects analyses within the study area. Since the main objective of the present study is the vegetation assessment, the orthomosaic products (high resolution RGB image) masked by simple biomass index (Eq. 6) using a predefined threshold. The green vegetable fraction was further masked by the calculated delta height map from the DSM product, masking out the herbaceous species.

BiomassIndex = G − R/2 − B /2

(6)

2.3.2. Sentinel-2A imagery preprocessing The level 2A product was not flagged and no geometric shift was noted in the study area. Thus, no re-georeferencing was applied. On the other hand, the retrieved reflectance was fine-tuned by cross-calibration by the spectral in situ ground measurements of continuous (preferably large), homogenous and flat surfaces (e.g. gravel roads and paths). The fine-tuning calculations were performed by an empirical line between sensor reflectance and surface reflectance, to assure a sitelevel spectral accuracy. The calculated gain coefficients were further applied to the full satellite image; as offset might be affected by the local (incident) conditions, it was omitted. 2.4. Data processing 2.4.1. UAV and Sentinel-2A pan-sharpening and restoration The possibility of the neural network finding nonlinear constraints and discovering regularities is a prospect for reconstructing vegetation spectral characteristics and estimation of plant stress with high spatial accuracy from a satellite image. Nowadays, a convolutional neural network (CNN) is known as a more appropriate approach among deep machine learning techniques for image analysis and “Super-resolution” tasks (Dong et al., 2016). However, such algorithms for improving the quality and spatial resolution of images by computer vision have proved successful for changing the pixel size 2–3 times. The fusion of the PAN and MS images allowed achieving images with the highest resolution in both the spatial and spectral domains, but preserving the unified spatial-spectral accuracy for the fused image. The recently suggested super-resolution convolutional neural network by Yuan et al. (2018) was modified, trained and tested in the present study. The architectures of CNNs are formed by stacking multiple convolutional layers to fit a complex nonlinear transformation from the degraded features in Sentinel-2A image (with spatial resolution of 10 m) and high resolution observations in PAN image (drone upscale grayscale image with spatial resolution of 0.8 m) to pan-sharpen MS image, applying multiple linear filtered layers with unlimited depth of the network, expanded along the direction in which the layers are stacked. The features/textures from co-registered, down-sampled and interpolated MS and PAN data are extracted by the filtering processes in every convolutional layer using convolutional kernels. The generated latent image from global-level to local-level refines spatial information of the latent image and helps convolution kernels fully extract spatial features from the PAN image with multiscale receptive views and context information. The low-frequency component generated from the coarse resolution Sentinel-2A image and the high-frequency component from the highresolution spatial details in the drone grayscale image is shared in the estimated joint results. The pan-sharpening process includes a downsampling matrix in the spatial domain, and similarly, the spectral response matrix of the PAN image, which down-samples the suppressed ground truth along the spectrum. The fundamental three layer CNN is combined with two multiscale convolutional layer blocks as previously suggested by Yuan et al., 2018. The basic pan-sharpening with CNN includes passing the input data

2.4.2. Calculation of vegetation indices (VIs) Among numerous spectral vegetation indices (VIs), the following were calculated: VARI (visible atmospheric resistant index (Gitelson and Merzlyak, 1994) and ExG (normalized excess green index (Woebbecke et al., 1995), NDVI (normalized difference vegetation index), GNDVI (green NDVI (Gitelson et al., 1996), RENDVI (red-edge NDVI), CHL-RED-EDGE (Chlorophyll Red-Edge), SIPI (Structure Insensitive Pigment Index), and NDII (normalized difference infrared index), across VIS-NIR-SWIR bands for pan-sharpen Sentinel-2A images. The indices were calculated for a complete time series (about 40 Sentinel-2A images) from August 2015 to September 2018, for a representative growing period from April to September (See Table 2.). 2.4.3. GPR data processing The output of a GPR survey is, for each considered trace, a radargram that, as said before, provides a coded representation of the subsurface features. However, such a representation is affected by undesired components, such as the signal due to the direct coupling between the Tx and Rx antennas and the measurement noise, which obscure partially or at all the useful parts of the collected signal, i.e. the field backscattered by the hidden targets. As a consequence, filtering procedures are adopted to reduce the effects of the undesired signals, while improving the visibility of the target and, thus, the radargram 7

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and spectral distortions of the fused/restored product, with respect to available reference data. The ones that are most commonly used are presented in this study. A no-reference index (QNR) developed by Alparone et al. (2008), with spatial and spectral components (DS and Dλ) can quantify the quality of the retrieved results. In the present study, the original Sentinel-2A (MS) image input into the models to yield high-resolution results. The spatial component and the spectral component of the reconstructed image were compared to the multispectral drone image, obtained by Matrice 600 DJI with RedEdge-MX 5-band camera. Peak Signal-to-Noise Ratio (PSNR) is used to assess the spatial reconstruction quality of each band. In the training data, the PSNR is calculated as a ratio between the maximum power of a signal (target image) and the power of residual error (delta between target and fused images). Vector measures are useful to quantify the spectral distortion in test data. A simple measure is the spectral angle mapper (SAM) (Yuhas et al., 1992), which consists of calculating the angle between the corresponding pixels of the fused and reference spectra (MS drone image and spectral library) in the space defined by considering each spectral band as a coordinate axis. Another measure is the root mean square error (RMSE) calculated for spectral bias between the fused and reference spectra collected in situ via portable OceanOptics spectrometers (USB4000 and NIR-FLAME) across VNIR and SWIR regions. These last measures were used for the NDII spectral index accuracy assessment since the drone MS image has no spectral data across SWIR region.

Table 2 List of indexes calculated from complete time serie from August 2015 to September 2018 (about 40 Sentinel-2A images). Index

Formula

Reference

VARI ExG NDVI GNDVI RENDVI CHL-REDEDGE SIPI NDII

(B3-B4)/(B3 + B4-B2) (2*B3-B4-B2)/(B3 + B4 + B2) (B8-B4)/(B8 + B4) (B8-B3)/(B8 + B3) (B8-B5)/(B8 + B5) (B8/B5–1)

Gitelson et al., 2002 Woebbecke et al., 1995 Rouse et al., 1974 Gitelson et al., 1996 Gitelson and Merzlyak, 1994 Viña and Gitelson, 2005

(B8-B1)/(B8 + B4) (B8-B11)/(B8 + B11)

Penuelas et al., 1995 Hardisky et al., 1983

interpretability. The choice of these procedures is driven by the specific surveyed scenario (Daniels, 2004; Persico, 2014) and here we considered the processing chain depicted in Fig. 3. Specifically, the processing chain involved the following five procedures: 1. Zero timing, which is performed to define the starting time properly, t0, of the radargram and herein is also exploited to eliminate the direct antenna coupling that is put outside the observation time window; 2. Maximum timing, which aims at cutting the observation time window at the time instant, tmax, after that only noisy signal is gathered. 3. DeWOW procedure, which subtracts at each collected waveforms its mean value along the time axis. 4. De-noising procedure, which is applied to improve reduce the noise effect. Herein a de-noising procedure based on the computation of the Singular Value Decomposition of the data matrix is applied (Cagnoli and Ulrych, 2001; Kabourek et al., 2012). The key point to filter out the noise properly is the choice of the truncation threshold that allows us to discriminate the noise components from the useful signal (Ludeno et al., 2018) 5. Linear gain procedure, which is applied to compensate the effects due to the geometrical spreading and the electromagnetic losses acting on the microwave signal propagation into the probed medium. This procedure is useful to emphasize the response of deeper targets and to make it comparable with that of shallower ones.

3. Results 3.1. Vineyard characterization 3.1.1. Georadar and soil survey GPR trace was gathered on vineyard row, 125 m along the slope, where in 2015 an important crop spatial differentiation was recorded. Filtered radargram obtained from the data at 200 MHz is shown in Fig. 6. In these radargrams, as well as in those referred to 600 MHz, the two-way travel time was converted into depth by means of Eq. (1) and by assuming that the relative permittivity of the surveyed media. Fig. 4a shows the presence of several constant signals distributed along the entire trace and up to a depth of about 1.2 m. These constant signals identify four material interfaces as it is also corroborated by fig. A-scan superposition, which showed the superposition of all the scans gathered along a GPR trace. According to Fig. 4a, it is possible to infer that the stratigraphy of the investigated soil is composed as follows:

In particular, in this case study, the observation time window of the radargrams at 200 MHz is 128 ns wide, while that of the radargrams at 600 MHz is of 64 ns. Both the time windows were discretized by 512 time- samples. Furthermore, the obtained radargrams (200 MHz and 600 MHz) were performed the zero timing by setting t0 equal to 10 ns and 6 ns, respectively, and the maximum timing equal to tmax 26 ns and 13 ns, respectively. Furthermore, regarding the denoising procedure, the SVD threshold was fixed always in such a way to filter out all of the singular values whose amplitude is 0.01 (i.e., −40 dB) time lower than the maximum one.

-

L1 layer from the air/soil interface up to a depth of about 0.2 m; L2 layer from across 0.2 m up to 0.58 m; L3 layer from depth 0.58 m up to 0.82 m; L4 layer from 0.82 up to the maximum investigation depth (1.2 m).

As far as the radargrams at 600 MHz (Fig. 4b) are concerned, due to the higher frequency, the penetration depth of the signal is significantly reduced (its maximum value is about 0.5 m) but an improved depth resolution is obtained. Specifically, only the material interface L1 identified at 200 MHz can be still recognized (Fig. 4a). On the other hand, a further interface appears at a depth of 0.1 m and this could denote the rooting depth of the vines. On the other hand, a further

2.5. Data post processing 2.5.1. Pan-sharpening and restoration process Numerous indexes have been developed for evaluating the spatial

Fig. 3. GPR data processing chain. 8

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Fig. 4. Filtered radargram and A-Scan superposition of data gathered along the slope with antennas with nominal central frequency of 200 MHz (a) and 600 MHz (b). Soil profiles described along the trace in correspondences of grey and white triangles (c).

Moreover, even though both soils are classified as silt loam, the Cambisol presents more sand and rock fragments in its horizons compared to the Calcisol, able to affect the soil water balance processes and plant rooting depth (0–60 cm and 0–120 cm for Calcisol and Cambisol respectively).

interface appears at a depth of 0.1 m and this could be the rooting depth of the vines. In this frame, it is worth pointing out that the estimated depth values are expected to be only a rough estimation of the real ones since they have been determined by assuming that the surveyed medium is homogeneous with average relative permittivity equal to 4. More accurate estimations of the in depth location of the material interfaces could be provided by taking into account the actual relative dielectric permittivity of each layer. Then, following the georadar results, a pedological survey was realized at 10 and 110 m along the slope in agreement with radagrams results (Fig. 4, grey and white triangles) which is representative of the variability recorded on plants in the upper (US) and lower part of slope (LS) in 2015. At US and LS, two soil profiles were described and sampled for chemical and physical analysis. The pedological survey has confirmed the variability showed by georadar with a presence of two different soil profiles along the vineyard line: Haplic Calcisols and Calcaric Cambisols (IUSS Working Group WRB, 2014; Fig. 4cc) in the upper part and lower part of the slope respectively. The main soil characteristics are reported in Table 3. A different soil horizons sequence was recognized between the two soils able to produce a different water availability for the plants. In particular, the main differences regarding the presence in the Cambisol of a layer of dominant stones at −100 cm and the Ab horizon (buried) with high soil organic matter content (2.5%) at about −35 cm from soil surface.

3.1.2. Wood characterization in trunk of vines on different soils In agreement with the results obtained from georadar and soil survey, two sub-plots along the slope were identified for the trunk wood characterization in vines growing on both soils. These sub-plots were used as targets to extract VIs indexes behavior over season. The microscopy analysis of core microsections showed that tree-ring width and basal area were significantly higher in Cambisol than Calcisol vines in the wood formed in the years 2015, 2016 and 2017 (Fig. 5a, b); on the opposite, in 2018, tree rings showed significantly higher width in Calcisol than in Cambisol vines (Fig. 5a, b). To evaluate the efficiency in water transport, we classified the measured vessels into classes of lumen size and here we report data about the class occupying > 75% of the total xylem area, namely vessels with lumen larger than 5000 μm2. In this class, vessel lumen area was significantly higher in Cambisol than Calcisol vines in the wood formed in the years 2015 and 2017 (Fig. 5c). Not significantly differences were found in 2016 and 2018, although the trend was opposite in 2018 with vessel lumen area being larger in Calcisol than in Cambisol vines (Fig. 5c). In agreement with larger wood production and vessel lumen area of

Table 3 Soil characteristics. Soil

Soil Horizon and thickness (m)

Particle size fraction Clay (g 100 g

Haplic Calcisol

Calcaric Cambisol

Ap1 (0–0.15) Ap2 (0.15–0.45) Bwk (0.45–0.80) BC (0.80–1.00) C (> 1.00) Ap1 (0–0.15) Ap2 (0.15–0.30/0.45) 2Ab (0.30/0.45–0.50/0.70) BC (0.50/0.70–1.00) C (> 1.00)

19.6 25.7 26.9 29.8 29.0 14.8 15.9 13.4 11.5 –

Silty

Sand

Rock fragments

pH (H2O)

−1

)

Total limestone (g 100 g

66.4 69.9 68.7 68.5 70.8 62.1 61.3 63.3 42.4 –

14.0 4.5 4.4 1.7 0.2 23.1 22.9 23.3 46.1 –

a a a a a b b b c d

8.1 8.3 7.9 8.1 8.2 8.1 8.1 7.9 7.2 –

−1

AWC (mm)

1.5 0.7 0.5 0.3 – 1.7 1.3 2.5 0.2 –

201.00

)

16.0 17.5 17.8 15.8 17.3 13.3 13.7 10.3 11.0 –

a = absent; b = common fine and medium sub-rounded gravel; c = few coarse sub-rounded gravel; d = Dominant stones. AWC = available water content, calculated in the first 100 cm of soil depth through HYPRESS pedotransfer function (Wösten, 2000). S.O.M = Soil organic matter. 9

S.O.M.

178.00

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Fig. 7. Comparison between vines grown on the Cambisol and those on Calcisol in terms of δ13C-derived WUEi, in the wood formed from 2015 to 2018. Mean values and standard errors are shown. Different letters correspond to significantly different values (p < .05).

The stable isotopes analysis of annual tree rings showed that δ13Cderived WUEi was significantly higher in Cambisol than Calcisol vines in wood formed in the years 2015, 2016, and 2017 (Fig. 7). In the year 2018, WUEi tended to decrease in vines growing on the Cambisol, while increased in those growing on Calcisol; therefore, in 2018 WUEi in vines on both soil types was similar. 3.2. Reconstructed MS data The multiscale CNN is proposed to fuse low resolution Sentinel-2A (spectral component) and high resolution UAV downscaled grayscale images by full-connected end-to-end architecture. It can effectively restore Sentinel-2A images with high spatial-resolution and reconstruct a time series of the scene. Therefore, the suggested approach is scene dependent and should be retrained for every location. Since the UAV is widely used in agriculture, this condition should be easily fulfilled. The training procedure will take approximately two days (on a standard workstation with one GPU card, 32GB memory, and NVIDIA Quadro M620), but once the network is trained, the reconstruction process will take less than 2 s. The performance of the network with different settings was tested. From the performance-to-epoch curve in Fig. 8, it is clear that the learning architecture of the multiscale CNN quickly reaches (< 50 epoch) high accuracy, while its ceiling is still far, applying the following hyper-parameters for training: initial learning rate ε 0.1, γ 0.5, and fixed μ 0.9, with the full-reference Q metric. The hyper-parameters are controlling the connectivity of the whole network. Once the number of parameters in the whole network is reduced, the computation is more efficient. Fig. 8 confirms that the default setting of γ = 0.5 is a balanced

Fig. 5. Comparison between vines grown on the Cambisol and those on Calcisol in terms of tree-ring width (a), basal area (b) and vessel lumen area (c), in the wood corresponding to the years from 2015 to 2018. Mean values and standard errors are shown. Different letters correspond to significantly different values within each year (p < .05).

Cambisol than Calcisol vines, in most analyzed years, both hydraulic diameter (Hd) (Fig. 6a) and potential hydraulic conductivity (Kh) (Fig. 6b) were higher in vines growing on Cambisol, but the difference was significant only for Kh.

Fig. 6. Comparison between vines grown on the Cambisol and those on Calcisol in terms of hydraulic diameter (Hd) (a) and potential hydraulic conductivity (Kh) (b), in the wood formed from 2015 to 2018. Mean values and standard errors are shown. Different letters correspond to significantly different values (p < .05). 10

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Table 4 The results of the multiscale CNN with different activation functions for gravel target (in Table 4). Activation function

SAM

RMSE

Relu Than PRelu

0.02 0.18 0.11

0.01 0.10 0.05

Table 5 Spatial quality assurance (US = upper part of slope; LS = lower part of slope). Index

Gravel

Sub-plot US on Calcisol Soil

LS on Cambisol Plant

Soil

Plant

Fig. 8. Average Q for the training data. PNSR

decision between error decrease in the early training epochs and smooth convergence in the later stages. Note that by setting a low value for γ the opportunity of breaking out of local minima may be lost. Since the training time was relatively long, approximately two days, the possible solution to reduce the training time is by skipping connection. The spectral reduction through the convolutional layer between two multiscale blocks was tested. The skip connection approach, previously suggested by Kim et al., 2016, preserved spectral dimensionality until the image fed into the last layer. Although the alternative architecture might suppress the training time, it is simply not deep enough for residual learning. The average Q for the training data with initial learning rate ε 0.1, γ 0.5, and fixed μ 0.9, for both architectures is presented in Fig. 9. The multiscale CNN was tested with different activation functions: ‘Relu’, ‘Tanh’ and ‘PRelu’. The reconstructed multispectral data were compared to the ground truth spectra collected in situ (Fig. 12). The results with the activation function of ‘Relu’ slightly outperforms the other two functions (Table 4). The QNR metrics are computed with drone MS references, which is an unattainable ground truth in this study. Thus, these metrics quantify the similarity of components in the fused images to the low-resolution observations and provides spatial and spectral fidelities at the level of high spatial and spectral resolutions. The QNR index was 0.87, the Ds spatial component was 0.1 and the Dλ spectral component was 0.08. The general similarity to the drone MS image was relatively high, yet the reconstructed image slightly better compared with the spectral component rather than with the spatial component. The spatial similarity with the high-resolution drone RGB image reached 0.12.

38.9

48.6

35.6

49.4

37.7

The average PSNR of all bands are used over five selected ground truth targets: gravel road, two soil surfaces (Calcisol and Cambisol) and two plant sub-plots (upper part of slope on Calcisol and lower part of slope on Cambisol) in the training data are reported in Table 5. The higher the value of PSNR, the higher the spatial quality of the reconstructed image over a particular target. The spectral similarity was also assessed by the SAM and root mean square error (RMSE) indexes between reconstructed pixels and spectral measurements collected over the same five selected ground truth targets in test data. Two representative examples are presented in Fig. 10, as expected, the vegetation spectra are the most dissimilar, since it is impossible to measure the same surface at both platforms. However, the reported SAM and RMSE scores (Table 6) conform to the high similarity in the spectral domain.

3.2.1. Vegetation indices (VIs) calculation for reconstructed Sentinel-2A imagery The Sentinel-2A data have been widely used for production management in large and homogeneous areas (e.g. Skakun et al., 2017; Zarco-Tejada et al., 2019). However, site-specific variability in the given vineyard plot cannot be reflected by 10 m*10 m spatial resolution data means, as one of the two zones is smaller than 100m2. Fig. 11 shows Sentinel-2A image from Fonzone site collected in July 2018 paired with the NDVI map, versus the results obtained by the suggested multiscale CNN approach and its NDVI map. Prior applying VIs and crop analysis, the performance of the network with fixed settings was tested and confirmed. Indeed, due to the low spatial resolution of the Sentinel data, it is impossible to separate the effects of the soil from effects of the crops prior calculating any vegetation index for any given pixel in vineyard (Fig. 12), where effective canopy covers is only 25% of the total surface area for vertical images. The squared correlation (R2) and root mean square error are shown in Figs. 13 and 14, between seven VIs across VNIR region calculated for the drone MS and the reconstructed image independently for 93 (the maximum iterative number to pull out without overlapping) randomly selected subzones (each 3 × 3 pixels) all over the image (masked out prior to the training of the CNN). The highest correlation and the lowest EMSE are recorded for the GNDVI, in which ~60% of the selected zones reported R2 > 0.95 and ~80% of the selected zones represented by RMSE < 0.1. The poorest performance was recorded for the SIPI, as the majority of the selected zones retrieve R2 < 0.85 with RMSE > 0.2. The NDVI was the second best correlation between the drone MS and the restored image, where > 90% of the zones calculated R2 > 0.90 and RMSE < 0.1. Fig. 15 shows the spatial/temporal detailed plot-scale differences.

Fig. 9. Average Q for the training data, a) the proposed network architecture, b) an alternative architecture (preserving spectral dimensionality to the last layer). 11

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Fig. 10. Comparison between spectra from the reconstructed image (solid spectrum) with the ground-truth spectra (dashed spectrum) measured in situ by portable OceanOptics spectrometers (USB4000 and NIR-FLAME), reported with the calculated standard deviation (STD), with: a) the gravel road, b) crop canopy US (at the upper part of the slope on Calcisol). Table 6 Spectral quality assurance (US = upper part of slope; LS = lower part of slope). Index

Gravel

Sub-plot US on Calcisol Soil

SAM RMSE

0.02 0.01

0.05 0.02

LS on Cambisol Plant 0.14 0.10

Soil 0.04 0.02

Plant 0.13 0.10

The presented VIs (GNDVI, NDVI, and NDII) are illustrating different spatial patterns within the plot during the growing period. The GNDVI measures the green spectrum from instead of the red spectrum, thus, it is more sensitive to chlorophyll concentration than NDVI. The NDVI represents VIS-NIR VI, which is highly correlated with the general canopy cover and in particularly the leaf area index (LAI). The NDII is sensitive to changes in the water content of the plant canopy. The inter-seasonal crop growth and development is reflected by the VIs presented in Fig. 16. The spatial differences and patterned behavior is typical for plants along slope and mixed soils. Looking closer to the sub-plots, in which the wood samples were collected (upper part of slope on Calcisol and lower part of slope on Cambisol), similar temporal patterns were recognized which follow the evolution of leaf phenology and canopy architecture during the growing season up to leaf senescence (Fig. 16). A standard analysis of variance (ANOVA) was implemented for each VI. F-values and p-values identified according to *** p < .001, ** p < .01, * p < .05 and ns, non-significant were: NDII 6.79***, NDVI 2.26**, RENDVI 1.12*,

Fig. 12. NDVI maps scaled from 0 to 1 for a) the original Sentinel-2A data, and b) the reconstructed image via multiscale CNN.

GNDVI 0.84ns. More specifically, the NDVI in Fig. 16a presents a clear deviation and an extremely good fitting to the 2nd order polynomial function. The NDVI peak obtained in this period was higher compared to the values cited in the literature, not because of the horizontal observations, as previously noted (Junges et al., 2017; Junges et al., 2019), but due to increased (reconstructed by multiscale CNN) spatial resolution in the vertical observation with an accurate and detailed spectral information. The NDVI resulted significantly higher in Cambisol than Calcisol vines throughout the whole vegetation period but the beginning and the end of the growing season. The GNDVI in

Fig. 11. a) the original Sentinel-2A image, b) the calculated NDVI (10 m), c) the reconstructed image, d) the calculated NDVI (0.8 m), both NDVI maps scaled from 0 to 1. 12

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Fig. 13. The calculated squared correlation (R2) between the drone MS image and the reconstructed image (from the same period) for seven applicable VIs.

August. However, a longer periodic examination evidenced another trend, where the differentiation between plants on Cambisol and Calcisol becomes smaller from 2015 to 2018 (Fig. 16). In 2018, no separation was recognized, and during July the trend was opposite to other years as the CWC/VWC on Calcisol is higher than plants on Cambisol (Fig. 17). The RENDVI in Fig. 16d showed no visual separation between the selected plants on both soils throughout the vegetation season, but only a similar phenology-related trend up to leaf senescence. The intra-seasonal deviation linked to the canopy development and plant growth rate was recognizable, and both zones fits well the 2ed order polynomial function. Coming back to VIs sensitive to chlorophyll content and leaf

Fig. 16b showed no visual separation between the selected plants on Calcisol and Cambisol until August. The seasonal deviation linked to the canopy development and plant growth rate was recognizable, and both zones fits well the 2nd order polynomial function. Starting from the beginning of August, vines on Calcisol showed significantly lower values than those on Cambisol, indicating that leaf senescence occurs earlier in the first type of sub-plot. The NDII in Fig. 16c presented the largest deviation between the plants, reporting water stress in both zones at the beginning and the end of the growing season. The peak value identified that the CWC/VWC is higher on Cambisol, thus indicating a higher water deficit in Calcisol vines throughout the vegetation period especially from the end of June to the beginning of

Fig. 14. The calculated root mean square error (RMSE) between the drone MS image and the reconstructed image (from the same period) for seven applicable VIs. 13

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b

c

Sept 15, 2017

August 31, 2017

August 16, 2017

August 01, 2017

July 27, 2017

July 07, 2017

May 18, 2017

a

0

0.5

1

Fig. 15. Three selected VIs calculated for the reconstructed Sentinel images during the growing season in 2017, all VIs value ranges from −1 to 1, where a) is the GNDVI (the common range for green vegetation is 0.2 to 0.8), b) is the NDVI (the common range for green vegetation is 0.2 to 0.8), c) is the NDII (the common range for green vegetation is 0.02 to 0.6; in the presented maps the calculation is 1-NDII for increasing illustrated contrast with other VIs, values above 0.6 identify possible water stress). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

14

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Fig. 16. The seasonal records from two selected zones (according to the wood sampling) on Calcisol and Cambisol sub-plots observed during the growing period in 2017. The temporal evolution of a) NDVI, b) GNDVI, c) NDII, and d) RENDVI are shown.

Fig. 17. The temporal evolution of NDII from 2015 to 2018 only mid dates of July August and September in both Calcisol and Cambisol sub-plots.

4. Discussion

pigment (VARI, ExG, GNDVI, CHK-RED-EDGE, SIPI), the present study could evaluate quality and level of performance of VIs across VIS-NIR region of Sentinel-2A MS data, owing to the produced high spatial and accurately preserved spectral data. Among the examined VIs in VIS region, VARI could correctly reproduce the temporal evolution presented by NDII and NDVI, but only in September (Fig. 18). The poorest performs was recorded by SIPI, which slightly varies during the growing season, suggesting that VARI is expressing the changes that occur in the plant with different CWC/VWC content only by the end of the growing season.

The assessment of plant hydraulic behavior from 2015 to 2018 through anatomical and isotopic traits proved to be useful to validate the output of the CNN image reconstruction methodology. The latter was able not only to reconstruct at a very fine spatial resolution (i.e. the sub-plot), the indices related to plant growth, canopy evolution throughout the growing season and plant water status but also to differentiate plant behavior at the inter-annual scale. Bearing in mind that the traditional MS space-borne observation is limited by the pre-defined and fixed scale, regardless of whether these 15

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Fig. 18. The temporal evolution of VARI from 2015 to 2018 only mid dates of July August and September in both Calcisol and Cambisol sub-plots.

previously published CNN network, the proposed method can increase spatial resolution more than ten times while maintaining accurate spectral information. However, it is worth little if the reconstructed data are raw with physical plant parameters and field measurements. The correctness of the reconstruction was confirmed by both the data about the morphological development and production recorded invivo in the year 2015 and by anatomical and isotopic data in tree rings from 2015 to 2018. More specifically, anatomical data suggested that vines growing on Cambisol are characterized by higher wood production (increased tree-ring width and basal area index) and tend to form larger vessels indicating a higher potential hydraulic conductivity to sustain increased biomass. The larger vessels in Cambisol vines allow a more efficient water transport when water is available, while narrower vessels would allow Calcisol vines to better withstand periods of summer drought, according to the well-established trade-offs between efficiency and safety of water flow (Sperry et al., 2006). The increased hydraulic conductivity, associated with higher WUEi confirmed by the carbon isotopes, would support the higher values of the presented VIs at the Cambisol sub-plot since, at the single-plant level, positive relations between hydraulic conductance and leaf expansion and photosynthetic rate have been proven (Limousin et al., 2009; Scoffoni et al., 2012). Indeed, in a simplistic view, the latter two processes are in turn related to plant water content, being leaf expansion also based on a turgordriven mechanism, while photosynthetic rate is also generally associated to improved photosynthetic pigment pool (Tardieu, 2013). The severe effect of soil properties on vine water status, due to different soil water holding capacity, is in agreement with findings by Brillante et al. (2018): the latter found the most serious water stress, indicated by the highest values of δ13C in sugars in mature grapes of vines growing on gravelly soils on steep slopes. Notwithstanding the complex relationships, not always linear, among morpho-anatomical, physiological and biochemical traits in the different plant organs, which would explain the species-specific adaptive strategies after environmental constraints, both the CNN image reconstruction methodology and the retrospective reconstruction of vines ecophysiology were able to indicate that the studied Aglianico vineyard showed different performances in successive years. This adaptive plasticity of xylem traits in the vines of the two analyzed subplots seems to favor the growth of vines on the Cambisol in years with low water availability (namely 2015–2017), where rooting depth is also higher, while would favor vine growth on the Calcisol in years characterized by high water availability in summer, like in 2018. Indeed, in years with abundant water availability, vines on Cambisol would suffer from conditions of anoxia at the root level likely resulting in lower photosynthetic activity, reduced xylogenesis and cell enlargement (leading to narrower vessels), thus biomass production and general plant health. Again, such a reverse trend found in anatomical and

scales are appropriate for the analysis (Dark and Bram, 2007), these data might be too coarse for vineyard, where crop- and soil- specific variability demands for a finer, even inter- or intra-row resolution. By reason of the low spatial resolution of those images, it is impossible to separate the effects of the soil from the effects of the crops on the VIs for any pixel acquired for a vineyard with a vertical-oriented canopy of only 25% of the total surface area. Yet, several recent studies introduced new methods for monitoring daily evaporation over vineyards using multi-sensor system and implement these data on Landsat8 thermal and SWIR spectral bands (Semmens et al., 2016), for monitoring leaf area index at 30 m resolution Landsat8 spectral data (Sun et al., 2017) and mapping of evapotranspiration retrievals using Landsat8 high-spatiotemporal thermal data for operational water use and stress monitoring in a vineyard (Knipper et al., 2019). All the proposed methods are applicable at relatively coarse resolution (from 30 m up), thus are less informative for small vineyards. The pan-sharpening techniques that are widely known for increasing the spatial resolution of the main satellite spectral images, might result in a moderated level of spectral details in vegetation and forest areas caused by internal limitations in these methods (e.g. prior and posterior probabilities, linear mixing models, signal transformations and Eigen decomposition) as reported by numerous recent studies (Polinova et al., 2019, Zhou et al., 2019). Since, the low resolution multispectral data, e.g., the presented Sentinel2 imagery data, on a pixel-level is a non-linear mixing between ground targets and a combination of their abundance and their albedo levels (Brook and Dor, 2016), the pan-sharpening methods cannot accurately restore the desired spatial and spectral information. The pan-sharpening methods are designed to restore the information by applying a signal separation strategy, they cannot preserve detailed and accurate information of ground targets (Fasbender et al., 2008). Therefore, these techniques are less efficient for determining the vegetation parameters required for agriculture studies. It can be argued that the designed network successfully coped with reconstructing spectral characteristics from a moderate spatial resolution Sentinel2 data. The main issue to de addressed in all networks is the fact that the high resolution spectral image is generated by simultaneously encoding low resolution spectral and high resolution images and decoding the latent image. In other words, features from early convolution layers encode low level spatial visual information (mainly edges) and high-level features (deeper convolution layers) encode high-level semantic information (e.g., textures). Therefore, the spatial information of high-level latent image is not efficient and unbalanced, that might cause severe scaling deformations of the ground features. In contrast to the pan-sharpening methods, the proposed approach doesn't require a high-resolution image for further applications and uses nonlinear spectral unmixing function, and unlike the majority of 16

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CNN architecture for remote sensing imagery pan-sharpening and reconstruction was presented in the current study. The multiscale extraction together with multi-depth sharing, and merging of features from the spatial/spectral domain of the Sentinel-2A imagery and the high resolution spatial domain of the UAV images creates new fused data. These data are valid for applications that could not be supported by the original Sentinel multispectral data. The proposed approach validated on a real vineyard system with data collected in-vivo and through retrospective analyses on wood, can be further improved to consider other anatomical and isotopic parameters, such as those linked with water flow efficiency/safety and nutritional status. Moreover, it has the great potential to be extended to other woody crop systems and forests. Finally, we can conclude that in general the integration between knowledge from different scientific ambits (remote and proximal sensing, botany, ecophysiology and pedology) represents a powerful approach. In particular, on a vineyard, such a multi-scale and -disciplinary approach can be considered a good opportunity to support the farmer in the field management and at the same time a valuable opportunity to study the plant adaptation to pedo-climatic conditions. This last information represents the base to understand the vine adaptive capability and plan the actions for vineyard management under different climatic scenarios. Indeed, emerging CNN methodologies would support real-time monitoring of several parameters related to plant health to better follow plant growth in the field and evaluate its performance depending on changing environmental conditions. This would furnish real-time evaluation of plant performance to design precision agriculture and forestry management.

isotopic data between 2017 and 2018 was detected in the VIs indexes which, especially in July 2018, showed a CWC/VWC higher on Calcisol than Cambisol vines. The different plant behavior was likely induced by the different soil types which have affected the water available for the vines. Indeed, the reconstructed NDII indexes suggested a higher level of water stress in vines growing on Calcisol compared to those growing on Cambisol in the years 2015–2017. The higher level of water stress suffered from Calcisol vines was confirmed by tree-ring anatomical data which showed narrower vessels in Calcisol vines compared to Cambisols. The narrower vessels are generally the result of reduced protoplast enlargement during the cell differentiation process after cambium division: the reduced availability of both water and free sugars and amino acids would have failed to build and maintain enough turgor pressure to favor the formation of larger conduits (De Micco et al., 2019). The narrower vessels in Calcisol vines would also allow a slower still safer water transport against embolism phenomena if compared with Cambisol vines. Indeed, the regulation of vessel lumen size, even at intraseasonal level, is a well-established strategy adopted by woody plants in semi-arid environments to cope with fluctuations in water availability: the occurrence of narrower vessels, as in Calcisol vines, allows overcoming drought events still maintaining slow water flow in case of spreading embolism in larger vessels (De Micco et al., 2008; De Micco et al., 2016). The obtained results have underlined as the soil spatial variability produces a differentiation in terms of spatial plant water availability and then plant responses in the vineyard. This relation is not a surprise and it is at the base of the concept of terroir and viticultural zoning, already recognized in the same study area on Aglianico by Bonfante et al. (2015, 2017b). In particular, the authors demonstrated how the plant responses (physiological behavior and grape quality) were strongly related to the soil horizons sequences and characteristics (vertical distribution, thickness and hydraulic properties) that drive the soil water balance and plant interaction in a dynamic way. Moreover, it is important to stress that the investigated soils were not so different from those identified in the analyzed case study (Calcisol and Cambisol). Finally, it is important to stress also that though many satellite images are provided for free (e.g., Sentinel imagery via Copernicus) they are not directly used by the farmers due to their low spatial resolution (that is not able to support precision agriculture) and the high level of expertise needed to manage them. In this sense, the proposed multidisciplinary approach can help to make operational the free satellite images in vineyard due to its ability to work at resolution able to support site-specific agriculture management. On the other hand, the model can be fully operative and applicable by the farmer with low cost, only if the model is set and validated considering each aspect of the vineyard, studied with scientific approaches attributable to the scientific sectors involved (e.g. botany, ecophysiology, pedology, etc.).

Acknowledgments We acknowledge Dr. M. Buonanno and G. Cantilena for UAV measurements, Dr. R. De Mascellis for supporting the pedological survey, and Francesco Niccoli and Chiara Amitrano for technical support in stem wood analyses. The present work was carried out within the LCIS project “An advanced low-cost system for farm irrigation support”, a joint ItalianIsraeli R&D project, “Fifteenth Call for Proposals for Joint R&D Projects – 2017, industrial track”. It was funded by the Ministry of Foreign Affairs and International Cooperation General Directorate for Country Promotion - Italian Republic and Israel Innovation Authority Ministry of Economy. We also wish to thank Radicirpine di Canonico & Santoli for partial financial support to the field and laboratory activities and the KTB group that inspired our work. References Aasen, H., Honkavaara, E., Lucieer, A., Zarco-Tejada, P., 2018. Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows. Remote Sens. https://doi.org/10.3390/rs10071091. Ali, A.M., Darvishzadeh, R., Skidmore, A.K., van Duren, I., 2017. Specific leaf area estimation from leaf and canopy reflectance through optimization and validation of vegetation indices. Agric. For. Meteorol. 236, 162–174. Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., Nencini, F., Selva, M., 2008. Multispectral and panchromatic data fusion assessment without reference. Photogramm. Eng. Remote. Sens. 74, 193–200. https://doi.org/10.14358/PERS.74.2. 193. Anderson, M.C., Zolin, C.A., Sentelhas, P.C., Hain, C.R., Semmens, K., Yilmaz, M.T., Gao, F., Otkin, J.A., Tetrault, R., 2016. The evaporative stress index as an indicator of agricultural drought in Brazil: an assessment based on crop yield impacts. Remote Sens. Environ. 174, 82–99. Barbour, M.M., 2007. Stable oxygen isotope composition of plant tissue: a review. Funct. Plant Biol. https://doi.org/10.1071/fp06228. Beeckman, H., 2016. Wood anatomy and trait-based ecology. IAWA J. 37, 127–151. Bonfante, A., Agrillo, A., Albrizio, R., Basile, A., Buonomo, R., De Mascellis, R., Gambuti, A., Giorio, P., Guida, G., Langella, G., Manna, P., Minieri, L., Moio, L., Siani, T., Terribile, F., 2015. Functional homogeneous zones (fHZs) in viticultural zoning procedure: an Italian case study on Aglianico vine. SOIL 1, 427–441. https://doi.org/ 10.5194/soil-1-427-2015. Bonfante, A., Sellami, M.H., Abi Saab, M.T., Albrizio, R., Basile, A., Fahed, S., Giorio, P.,

5. Conclusion Based on the results, the vegetation/canopy water content cannot be accurately assessed without the SWIR spectral information. The most suitable platform for performing continues land management and monitoring is satellite remote sensing. Due to its relatively coarse spatial resolution and limited observation by the pre-defined and fixed scale, the multispectral satellite remote sensing is applicable in large areas and imperfect in non-fully closed canopy (e.g., soil and plant mixture) sites. The available commercial UAVs and UAS systems can produce high resolution information across VIS-BIR region, which is not enough for an accurate vegetation/canopy water content, and at the same time not suitable to monitor relatively big surfaces. Since none of the available commercial UAVs and UAS systems can provide spectral information across SWIR region, one possible solution is to introduce a fusion technique to combine spatial and spectral data. The multiscale 17

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Ezenne, G.I., Jupp, L., Mantel, S.K., Tanner, J.L., 2019. Current and potential capabilities of UAS for crop water productivity in precision agriculture. Agric. Water Manag. https://doi.org/10.1016/j.agwat.2019.03.034. Fan, Z.X., Zhang, S.B., Hao, G.Y., Ferry Slik, J.W., Cao, K.F., 2012. Hydraulic conductivity traits predict growth rates and adult stature of 40 Asian tropical tree species better than wood density. J. Ecol. https://doi.org/10.1111/j.1365-2745.2011.01939.x. FAO, 2009. Conference STRATEGIC FRAMEWORK 2010–2019, C 2009/3. (Rome). Farquhar, G.D., O’Leary, M.H., Berry, J.A., 1982. On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Australian J Plant Physiol 9, 121–137. Farquhar, G.D., Ehleringer, J.R., Hubick, K.T., 1989. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Biol. 40, 503–537. Fasbender, D., Radoux, J., Bogaert, P., 2008. Bayesian data fusion for adaptable image pansharpening. IEEE Trans. Geosci. Remote Sens. 46 (6), 1847–1857. https://doi. org/10.1109/tgrs.2008.917131. di Francescantonio, D., Villagra, M., Goldstein, G., Campanello, P.I., 2018. Leaf phenology and water-use patterns of canopy trees in Northern Argentinean subtropical forests. Tree Physiol. 38, 1841–1854. https://doi.org/10.1093/treephys/tpy072. Fuentes-Peailillo, F., Ortega-Farias, S., Rivera, M., Bardeen, M., Moreno, M., 2018. Comparison of vegetation indices acquired from RGB and multispectral sensors placed on UAV. In: 2018 IEEE International Conference on Automation/XXIII Congress of the Chilean Association of Automatic Control (ICA-ACCA), https://doi. org/10.1109/ica-acca.2018.8609861. Fukuzawa, K., 1992. Ultraviolet microscopy. In: Lin, S.Y., Dence, C.W. (Eds.), Methods in Lignin Chemistry. Springer, Berlin, pp. 110–131. Gaudillère, J.P., Van Leeuwen, C., Ollat, N., 2002. Carbon isotope composition of sugars in grapevine, an integrated indicator of vineyard water status. J. Exp. Bot. 53, 757–763. https://doi.org/10.1093/jexbot/53.369.757. Ghamisi, P., Rasti, B., Yokoya, N., Wang, Q., Hofle, B., Bruzzone, L., Bovolo, F., Chi, M., Anders, K., Gloaguen, R., Atkinson, P.M., 2018. Multisource and Multitemporal Data Fusion in Remote Sensing. arXiv preprint arXiv:1812.08287. Gitelson, A., Merzlyak, M.N., 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. J. Photochem. Photobiol. B Biol. https://doi.org/10.1016/1011-1344(93)06963-4. Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80, 76–87. https://doi.org/ 10.1016/S0034-4257(01)00289-9. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. https://doi. org/10.1016/s0034-4257(96)00072-7. Harbertson, J.F., Picciotto, E.A., Adams, D.O., 2003. Measurement of polymeric pigments in grape berry extracts and wines using a protein precipitation assay combined with bisulfate bleaching. Am. J. Enol. Vitic. 54, 301–306. Hardisky, M.A., Klemas, V., Smart, R.M., 1983. The influence of soil-salinity, growth form, and leaf moisture on the spectral radiance of Spartina-alterniflora canopies. Photogrammetric Engineering and Remote Sensing 49, 77–83. Hashimoto, N., Saito, Y., Maki, M., Homma, K., 2019. Simulation of reflectance and vegetation indices for unmanned aerial vehicle (UAV) monitoring of paddy fields. Remote Sens. 11 (18), 2119. Hunt, E., Li, L., Friedman, J., Gaiser, P., Twarog, E., Cosh, M., 2018. Incorporation of stem water content into vegetation optical depth for crops and woodlands. Remote Sens. https://doi.org/10.3390/rs10020273. IUSS Working Group WRB, 2014. World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. In: World Soil Resources Reports. No. 106https://doi.org/10.1017/ S0014479706394902. Junges, A.H., Fontana, D.C., Anzanello, R., Bremm, C., 2017. Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil. Ciência e Agrotecnologia 41, 543–553. https://doi.org/10. 1590/1413-70542017415049016. Junges, A.H., Fontana, D.C., Lampugnani, C.S., 2019. Relationship between the normalized difference vegetation index and leaf area in vineyards. Bragantia. https://doi. org/10.1590/1678-4499.2018168. Kabourek, V., Černý, P., Mazánek, M., 2012. Clutter reduction based on principal component analysis technique for hidden objects detection. Radioengineering 21, 464–470. Kamilaris, A., Prenafeta-Boldú, F.X., 2018. Deep learning in agriculture: a survey. Comput. Electron. Agric. https://doi.org/10.1016/j.compag.2018.02.016. Kim, J., Kwon Lee, J., Mu Lee, K., 2016. Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654. Knipper, K.R., Kustas, W.P., Anderson, M.C., Alsina, M.M., Hain, C.R., Alfieri, J.G., Prueger, J.H., Gao, F., McKee, L.G., Sanchez, L.A., 2019. Using high-spatiotemporal thermal satellite ET retrievals for operational water use and stress monitoring in a California vineyard. Remote Sens. 11 (18), 2124. Kramer, P.J., 1983. Water Relations of Plants. Academic Press, New York. Küng, O., Strecha, C., Beyeler, A., Zufferey, J.-C., Floreano, D., Fua, P., Gervaix, F., 2012. The accuracy of automatic photogrammetric techniques on ultra-light UAV imagery. Isprs - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. https://doi.org/10.5194/isprsarchives-xxxviii-1-c22-1252011. Limousin, J.M., Rambal, S., Ourcival, J.M., Rocheteau, A., Joffre, R., Rodriguez-Cortina, R., 2009. Long-term transpiration changes with rainfall decline in a Mediterranean Quercus ilex forest. Glob. Chang. Biol. 15, 2163–2175. Ludeno, G., Catapano, I., Renga, A., Vetrella, A.R., Fasano, G., Soldovieri, F., 2018. Assessment of a micro-UAV system for microwave tomography radar imaging.

Langella, G., Monaco, E., Bouma, J., 2017a. The role of soils in the analysis of potential agricultural production: a case study in Lebanon. Agric. Syst. 156. https://doi. org/10.1016/j.agsy.2017.05.018. Bonfante, A., Alfieri, S.M., Albrizio, R., Basile, A., De Mascellis, R., Gambuti, A., Giorio, P., Langella, G., Manna, P., Monaco, E., et al., 2017b. Evaluation of the effects of future climate change on grape quality through a physically based model application: a case study for the Aglianico grapevine in Campania region, Italy. Agric. Syst. 152, 100–109. Bonfante, A., Monaco, E., Manna, P., De Mascellis, R., Basile, A., Buonanno, M., Cantilena, G., Esposito, A., Tedeschi, A., De Michele, C., Belfiore, O., Catapano, I., Ludeno, G., Salinas, K., Brook, A., 2019. LCIS DSS—An irrigation supporting system for water use efficiency improvement in precision agriculture: A maize case study. Agric. Syst. 176, 102646. https://doi.org/10.1016/J.AGSY.2019.102646. Boon, M.A., Greenfield, R., Tesfamichael, S., 2016. Unmanned aerial vehicle (UAV) photogrammetry produces accurate high-resolution orthophotos, point clouds and surface models for mapping wetlands. South African Journal of Geomatics. https:// doi.org/10.4314/sajg.v5i2.7. Brillante, L., Mathieu, O., Bois, B., van Leeuwen, C., Lévêque, J., 2015. The use of soil electrical resistivity to monitor plant and soil water relationships in vineyards. Soil 1, 273–286. https://doi.org/10.5194/soil-1-273-2015. Brillante, L., Mathieu, O., Lévêque, J., van Leeuwen, C., Bois, B., 2018. Water status and must composition in grapevine cv. Chardonnay with different soils and topography and a mini meta-analysis of the δ13C/water potentials correlation. J. Sci. Food Agric. 98, 691–697. https://doi.org/10.1002/jsfa.8516. Brook, A., Ben Dor, E., 2011. Supervised vicarious calibration (SVC) of hyperspectral remote-sensing data. In: 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), https://doi.org/10.1109/ whispers.2011.6080943. Brook, A., Dor, E.B., 2016. Quantitative detection of settled dust over green canopy using sparse unmixing of airborne hyperspectral data. IEEE J. Select. Topics Appl. Earth Observ. Rem. Sens. 9 (2), 884–897. https://doi.org/10.1109/jstars.2015.2489207. Brook, A., Polinova, M., Ben-Dor, E., 2018. Fine tuning of the SVC method for airborne hyperspectral sensors: the BRDF correction of the calibration nets targets. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2017.09.014. Cagnoli, B., Ulrych, T.J., 2001. Singular value decomposition and wavy reflections in ground-penetrating radar images of base surge deposits. J. Appl. Geophys. 48, 175–182. https://doi.org/10.1016/S0926-9851(01)00089-1. Catapano, I., Gennarelli, G., Ludeno, G., Soldovieri, F., Persico, R., 2019. Ground-penetrating radar: Operation principle and data processing. In: Webster, J.G. (Ed.), Encyclopedia of Electrical and Electronics Engineering. Wiley, New York. Cernusak, L.A., Arthur, D.J., Pate, J.S., Farquhar, G.D., 2003. Water relations link carbon and oxygen isotope discrimination to phloem sap sugar concentration in Eucalyptus globulus. Plant Physiol. 131, 1544–1554. https://doi.org/10.1104/pp.102.016303. Cirillo, C., De Micco, V., Rouphael, Y., Balzano, A., Caputo, R., De Pascale, S., 2017. Morpho-anatomical and physiological traits of two Bougainvillea genotypes trained to two shapes under deficit irrigation. Trees 31, 173–187. https://doi.org/10.1007/ s00468-016-1466-6. Colombo, C., Miano, T., 2015. Metodi di analisi chimica del suolo, 3 edizione. Pubblicità & Stampa, Modugno (BA), Italy, 978-88-940679-0-3pp. 2015. Daniels, D.J., 2004. Ground Penetrating Radar (IEE Radar, Sonar and Navigation Series Vol 15). IEE, London, UK. Dark, S.J., Bram, D., 2007. The modifiable areal unit problem (MAUP) in physical geography. Prog. Phys. Geogr. https://doi.org/10.1177/0309133307083294. De Micco, V., Aronne, G., Baas, P., 2008. Wood anatomy and hydraulic architecture of stems and twigs of some Mediterranean trees and shrubs along a mesic-xeric gradient. Trees 22, 643–655. De Micco, V., Campelo, F., de Luis, M., Bräuning, A., Grabner, M., Battipaglia, G., Cherubini, P., 2016. Intra-annual density fluctuations in tree rings: how, when, where, and why? IAWA J. 37, 232–259. De Micco, V., Zalloni, E., Battipaglia, G., Erbaggio, A., Scognamiglio, P., Caputo, R., Cirillo, C., 2018. Rootstock effect on tree-ring traits in grapevine under a climate change scenario. IAWA J. https://doi.org/10.1163/22941932-20170199. De Micco, V., Carrer, M., Rathgeber, C.B.K., Camarero, J.J., Voltas, J., Cherubini, P., Battipaglia, G., 2019. From xylogenesis to tree rings: wood traits to investigate tree response to environmental changes. IAWA J. 40https://doi.org/10.1163/2294193240190246. press. Delgado, J.A., Short Jr., N.M., Roberts, D.P., Vandenberg, B., 2019. Big data analysis for sustainable agriculture on a geospatial cloud framework. Front. Sustain. Food Syst. 3, 54. https://doi.org/10.3389/fsufs. Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., Yan, Y., 2018. UAV-based multispectral remote sensing for precision agriculture: a comparison between different cameras. ISPRS J. Photogramm. Remote Sens. https://doi.org/10.1016/j.isprsjprs.2018.09.008. Di Gennaro, A., Aronne, G., De Mascellis, R., Vingiani, S., Sarnataro, M., Abalsamo, P., Cona, F., Vitelli, L., Arpaia, G., 2002. I sistemi di terre della Campania. In: Reg. Camp. Risorsa srl, Assessor. alla Ric. Sci. Selca, Firenze. Dixon, M.A., Grace, J., Tyree, M.T., 1984. Concurrent measurements of stem density, leaf and stem water potential, stomatal conductance and cavitation on a spaling of Thuja occidentalis L. Plant Cell Environ. 7, 615–618. https://doi.org/10.1111/1365-3040. ep11592146. Dong, C., Loy, C.C., Tang, X., 2016. Accelerating the super-resolution convolutional neural network. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp. 391–407. https://doi.org/10.1007/978-3-319-46475-6_25. Eckstein, D., Bauch, J., 1969. Beitrag zur Rationalisierung eines dendrochronologischen Verfahrens und zur Analyse seiner Aussagesicherheit. Forstwissenschaftliches Zentralblatt 88, 230–250.

18

Remote Sensing of Environment 240 (2020) 111679

A. Brook, et al.

J.H., Hain, C.R., Cammalleri, C., Yang, Y., Xia, T., 2016. Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens. Environ. 185, 155–170. Sieberth, T., Wackrow, R., Chandler, J.H., 2016. Automatic detection of blurred images in UAV image sets. ISPRS J. Photogramm. Remote Sens. 122, 1–16. Skakun, S., Vermote, E., Roger, J.-C., Franch, B., 2017. Combined use of Landsat-8 and sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale. AIMS Geosci 3, 163–186. https://doi.org/10.3934/geosci.2017.2.163. Spachos, P., Gregori, S., 2019. Integration of Wireless Sensor Networks and Smart UAVs for Precision Viticulture. IEEE Internet Comput. 23, 8–16. https://doi.org/10.1109/ MIC.2018.2890234. Sperry, J.S., Nichols, K.L., Sullivan, J.E.M., Eastlack, S.E., 1994. Xylem embolism in ringporous, diffuse-porous, and coniferous trees of northern Utah and interior Alaska. Ecology 75, 1736–1752. Sperry, J.S., Hacke, U.G., Pittermann, J., 2006. Size and function in conifer tracheids and angiosperm vessels. Am. J. Bot. https://doi.org/10.3732/ajb.93.10.1490. Stokes, M.A., Smiley, T.L., 1968. An Introduction to Tree-Ring Dating. University of Chicago Press, Chicago, US, pp. 73. Sun, L., Gao, F., Anderson, M., Kustas, W., Alsina, M., Sanchez, L., Sams, B., McKee, L., Dulaney, W., White, W., Alfieri, J., 2017. Daily mapping of 30 m LAI and NDVI for grape yield prediction in California vineyards. Remote Sens. 9 (4), 317. Tardieu, F., 2013. Plant response to environmental conditions: assessing potential production, water demand, and negative effects of water deficit. Frontiers Plant Physiol 4, 1–11. Terribile, F., Agrillo, A., Bonfante, A., Buscemi, G., Colandrea, M., D’Antonio, A., De Mascellis, R., De Michele, C., Langella, G., Manna, P., et al., 2015. A web-based spatial decision supporting system for land management and soil conservation. Solid Earth 6, 903. Tyree, M.T., Zimmermann, M.H., 2002. Xylem Structure and the Ascent of Sap. SpringerVerlag, Berlin Heidelberg GmbH, pp. 283. Viña, A., Gitelson, A.A., 2005. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophys. Res. Lett. 32. https://doi.org/10.1029/2005GL023647. Voltas, J., Camarero, J.J., Carulla, D., Aguilera, M., Ortiz, A., Ferrio, J.P., 2013. A retrospective, dual-isotope approach reveals individual predispositions to winterdrought induced tree dieback in the southernmost distribution limit of Scots pine. Plant Cell Environ. https://doi.org/10.1111/pce.12072. Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A., 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE. https://doi.org/10.13031/2013.27838. Wösten, J.H., Lilly, A., Nemes, A., Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169–185. https://doi.org/10. 1016/S0016-7061(98)00132-3. Xue, J., Su, B., 2017. Significant remote sensing vegetation indices: a review of developments and applications. Journal of Sensors. https://doi.org/10.1155/2017/ 1353691. Yuan, Q., Wei, Y., Meng, X., Shen, H., Zhang, L., 2018. A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2018.2794888. Yuhas, R.H., Goetz, A.F.H., Boardman, J.W., 1992. Discrimination among Semi-Arid Landscape Endmembers Using the Spectral Angle Mapper (SAM) Algorithm. Zarco-Tejada, P.J., Hornero, A., Beck, P.S.A., Kattenborn, T., Kempeneers, P., HernándezClemente, R., 2019. Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline. Remote Sens. Environ. 223, 320–335. https://doi.org/10.1016/J.RSE.2019.01.031. Zhao, W., Jiao, L., Ma, W., Zhao, J., Zhao, J., Liu, H., Cao, X., Yang, S., 2017. Superpixelbased multiple local CNN for panchromatic and multispectral image classification. IEEE Trans. Geosci. Remote Sens. 55 (7), 4141–4156. Zhou, F., Hang, R., Liu, Q., Yuan, X., 2019. Pyramid Fully Convolutional Network for Hyperspectral and Multispectral Image Fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 1549–1558. https://doi.org/10.1109/JSTARS.2019.2910990.

Remote Sens. Environ. https://doi.org/10.1016/j.rse.2018.04.040. Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., Müller-Wilm, U., Gascon, F., 2017. Sen2Cor for Sentinel-2. Image and Signal Processing for Remote Sensing XXIII. https://doi.org/10.1117/12.2278218. Mattivi, F., Zulian, C., Nicolini, G., Valenti, L., 2002. Wine, biodiversity, technology, and antioxidants. Ann. N. Y. Acad. Sci. 957, 37–56. https://doi.org/10.1111/j.17496632.2002.tb02904.x. Momen, M., Wood, J.D., Novick, K.A., Pangle, R., Pockman, W.T., McDowell, N.G., Konings, A.G., 2017. Interacting effects of leaf water potential and biomass on vegetation optical depth. Journal of Geophysical Research: Biogeosciences 122 (11), 3031–3046. OIV – International Organization of vine and wine, 2019. Compendium of International Methods of Wine and Must Analysis. volumes I and II OIV, Paris978-2-85038-003-7. Park, H., Choi, J., Park, N., Choi, S., 2017. Sharpening the VNIR and SWIR bands of sentinel-2A imagery through modified selected and synthesized band schemes. Remote Sens. https://doi.org/10.3390/rs9101080. Penuelas, J., Filella, I., Gamon, J.A., 1995. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 131, 291–296. https://doi.org/10. 1111/j.1469-8137.1995.tb03064.x. Persico, R., 2014. Introduction to ground penetrating radar: Inverse scattering and data processing. In: Introduction to Ground Penetrating Radar: Inverse Scattering and Data Processing, https://doi.org/10.1002/9781118835647. Poblete, T., Ortega-Farías, S., Moreno, M.A., Bardeen, M., 2017. Artificial neural network to predict vine water status spatial variability using multispectral information obtained from an unmanned aerial vehicle (UAV). Sensors 17. https://doi.org/10.3390/ s17112488. Polinova, M., Jarmer, T., Brook, A., 2018. Spectral data source effect on crop state estimation by vegetation indices. Environ. Earth Sci. 77 (22), 752. Polinova, M., Wittenberg, L., Kutiel, H., Brook, A., 2019. Reconstructing pre-fire vegetation condition in the wildland urban interface (WUI) using artificial neural network. J. Environ. Manag. 238, 224–234. Riihimäki, H., Luoto, M., Heiskanen, J., 2019. Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2019.01.030. Roden, J.S., Farquhar, G.D., 2012. A controlled test of the dual-isotope approach for the interpretation of stable carbon and oxygen isotope ratio variation in tree rings. Tree Physiol. https://doi.org/10.1093/treephys/tps019. Romero, M., Luo, Y., Su, B., Fuentes, S., 2018. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Comput. Electron. Agric. https://doi.org/10. 1016/j.compag.2018.02.013. Rouse, J.W., Haas, R.H., Deering, D.W., Sehell, J.A., 1974. Monitoring the vernal advancement and retrogradation (Green wave effect) of natural vegetation. Final Rep. RSC 1978-4, Remote Sensing Center, Texas A&M Univ., College Station. Ruzin, S.E., et al., 1999. Plant Microtechnique and Microscopy. Oxford University Press, New York. Saurer, M., Siegwolf, R.T.W., Schweingruber, F.H., 2004. Carbon isotope discrimination indicates improving water-use efficiency of trees in northern Eurasia over the last 100 years. Glob. Chang. Biol. 10, 2109–2120. Scheidegger, Y., Saurer, M., Bahn, M., Siegwolf, R., 2000. Linking stable oxygen and carbon isotopes with stomatal conductance and photosynthetic capacity: a conceptual model. Oecologia. https://doi.org/10.1007/s004420000466. Scholander, P.F., Hammel, H.T., Bradstreet, E.D., Hemmingsen, E.A., 1965. Sap pressure in vascular plants. Science 80 (148), 339–346. https://doi.org/10.1126/science.148. 3668.339. Schweingruber, F.H., 1988. Tree Rings: Basics and Applications of Dendrochronology. Kluwer Academic Publishers, Dordrecht, The Netherlands. Schweingruber, F.H., 1996. Tree Rings and Environment: Dendroecology. Paul Haupt AG Bern. Scoffoni, C., McKown, A.D., Rawls, M., Sack, L., 2012. Dynamics of leaf hydraulic conductance with water status: quantification and analysis of species differences under steady state. J. Exp. Bot. 63, 643–658. Semmens, K.A., Anderson, M.C., Kustas, W.P., Gao, F., Alfieri, J.G., McKee, L., Prueger,

19