Deriving phenology of barley with imaging hyperspectral remote sensing

Ecological Modelling 295 (2015) 123–135

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Deriving phenology of barley with imaging hyperspectral remote sensing Angela Lausch *, Christoph Salbach, Andreas Schmidt, Daniel Doktor, Ines Merbach, Marion Pause Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Permoserstr. 15/D-04318 Leipzig, Germany

A R T I C L E I N F O

A B S T R A C T

Article history: Received 6 January 2014 Received in revised form 26 July 2014 Accepted 1 October 2014 Available online 13 October 2014

The aim of this paper was to create a model that predicts the different phenological BBCH macro-stages of barley in laboratory on the plot scale and to transfer the most suitable model to the landscape scale. To characterise the phenology, eight vitality and phenology-related vegetation parameters like leaf area index (LAI), Chl-SPAD content, C-content, N-content, C/N-content, canopy chlorophyll content (CCC), gravimetric water content (GWC) and vegetation height at the same time as all imaging hyperspectral measurements (AISA-EAGLE, 395–973 nm). These biochemical–biophysical vegetation parameters were investigated according to the different phenological macro-stages of barley. The predictive models were developed using four different types of vegetation indices (VI): (I) published VI’s, (II) reflectance VI’s as well as (III) VI(xy) formula combinations and (IV) a combination of all VI index types using the Library for Support Vector Machines (LibSVM) and tested with a recursive conditional correlation weighting selection algorithm (RCCW) to reduce the number of variables. To increase the performance of the model a 10-fold cross-validation was carried out for all statistical models. The GWC was found to be the most important variable for differentiating between the phenological macro-stages of barley. The most suitable model for predicting the phenological BBCH macro-stages was achieved by a model that combined all three kinds of VI’s: published VI’s, reflectance VI’s and formula combination VI’s with a classification accuracy of 84.80%. With the classification model for the reflectance VI’s Y = 746 nm and for the VI formula combinations Y = (527 + 612) nm and Y = (540 + 639) nm. The best predictive model was applied to the airborne AISA-EAGLE hyperspectral data to model the phenological macro-stages of barley at the landscape level. The classification error of the best predictive model of 12.80% as well as disturbance factors such as channels and areas with weeds or ruderal vegetation lead to misclassifications of BBCH macro-stages at the landscape level. By using One Sensor At Different Scales-Approach (OSADIS), sensor-specific differences in the model building and model transfer can be eliminated. The approach described in the paper for determining the phenology based on imaging hyperspectral RS data shows that in the process of plant phonological development a number of biochemical–biophysical vegetation traits in vegetation change, which can be thoroughly recorded with hyperspectral remote sensing technology. For this reason, hyperspectral RS constitutes an ideal, costeffective and comparable approach, with whose help vegetation traits and changes can be quantified, which are key for ecological modelling. ã 2014 Elsevier B.V. All rights reserved.

Keywords: Phenological stage BBCH barley Hyperspectral sensor AISA Spectral indices Vegetation characteristics

1. Introduction In the course of its development, vegetation goes through a series of different phenological stages. During these stages the plant physiology changes resulting in a change to the biochemical– biophysical and structural parameters of the plants. Therefore,

* Corresponding author. Tel.: +49 341 2351961; fax: +49 341 235 1939. E-mail address: [email protected] (A. Lausch). http://dx.doi.org/10.1016/j.ecolmodel.2014.10.001 0304-3800/ ã 2014 Elsevier B.V. All rights reserved.

investigations on the phenology of the vegetation are in fact investigations on the spectral response of a remote-sensing sensor in relation to biochemical–biophysical structural vegetation parameters over time. The normalized difference vegetation index (NDVI) derived from the NOAA/AVHRR (Advanced Very High Resolution Radiometer) sensors has been implemented for some time now to evaluate phenological characteristics over larger areas and time periods (Badeck et al., 2004; Doktor et al., 2009; Bégué et al., 2011). Other studies have also implemented new sensors with a higher spectral

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and spatial resolution for deriving vegetation phenology such as MODIS (MODerate Resolution Imaging Spectro-radiometer) and Spot-vegetation (Jönsson et al., 2010; Li et al., 2010). Jönsson et al. (2010) were also able to demonstrate based on the time-series of MODIS data that the imaging of seasonal changes in the spring phenology of coniferous forests was better when using the wide dynamic range vegetation index (WDRVI) compared to the normalized difference vegetation index (NDVI). Several authors have used the NDVI temporal profile to derive and model phenological key stages such as budburst and senescence (e.g. Botta et al., 2000), whereas Myneni et al. (1997) used the NDVI to monitor plant growth. The NDVI (NDVI (NIR
RED)/(NIR + RED) differences in reflectance between the visible and infrared spectrum depends on different vegetation parameters. Numerous studies have documented the spatial and spectral behaviour of NDVI for the predictive power of the phenology of vegetation. The greenness of the vegetation, the green leaf-area index (LAI), the absorption of chlorophyll, the green aboveground phytomass, the photosynthesis capacity as well as primary production all play a significant role here. Generally speaking, relationships between individual parameters and the NDVI should be considered with regard to specific locations and points in time (Badeck et al., 2004; Bégué et al., 2011). So far the NDVI has been a more important and universal indicator to derive the status and phenology of vegetation and is primarily used in a continental and/or global context (Huete et al., 1994). Deriving and modelling phenology based on the NDVI is not always straightforward and subject to some difficulties. Deriving the phenology in heterogeneous landscapes with several species of vegetation or agricultural fields and pastures with different forest patches can therefore be problematic (Schwartz and Reed, 1999; Hu et al., 2000; Doktor et al., 2009). Moreover, ascertaining the seasonal metrics of forested areas proves to be a problem. The satellite often detects the greening of the understory (Schwartz and Reed, 1999; Hu et al., 2000) that takes place about 2 weeks prior to a greening of the canopy. This is about the actual temporal resolution of the NOAA satellites affecting the numerous observations to be discarded due to cloud coverage. Furthermore, the NDVI has been found to saturate for LAI values >3 (e.g. Carlson and Ripley, 1997). However, such high LAI values are not usually found before the greening of a substantial part of the canopy. Consequently, one would still expect a reasonably high sensitivity towards increasing LAI after the greening-up of the understory. At the peak of the growing season the LAI for grassland demonstrated values of 2–3 (Fensholt et al., 2004). Myneni et al. (2002) found the NDVI for broadleaved forests to be saturating at LAI 3 (NDVI 0.7). The identification of robust metrics to derive the senescence of vegetation based only on certain spectral wavelengths limits the opportunity to investigate the significance of other important biochemical–biophysical vegetation parameters to characterise the phenological changes to vegetation. In this way Zwiazek et al. (2001) emphasize that seasonal changes in photosynthetic efficiency, cell water status and thus phenology are not directly visible to the eye, however physiological changes associated with cold hardening such as clumping of chloroplasts, modification of cell membranes, cell dehydration and the forming of intracellular ice will alter light absorbance and reflectance. Remote sensing could also detect such changes by using wavelengths outside the range included in NDVI or WDRVI (Grace et al., 2007; Nakaji et al., 2006; Jönsson et al., 2010). Hyperspectral remote-sensing data have a high spectral range of 400–2500 nm with a spectral resolution of 2.5–10 per spectral band. They are thus ideally suited to answer questions about deriving indicators of seasonal vegetation changes. Hyperspectral

imagery has been applied more frequently over recent years. With the launch of the satellite hyperspectral sensors EnMAP (Environmental Mapping and Analysis Program) foreseen for 2017, the routine implementation of hyperspectral satellites will be possible for a more precise spectral diagnostic and quantitative monitoring of the status and phenology of vegetation over larger areas. Previous investigations based on the implementation of airborne hyperspectral sensors show that in addition to the previously used phenology indicators, there are others that will be able to model the senescence of vegetation over time more accurately (Filella et al., 2004; Nakaji et al., 2006; Ye et al., 2009). In this way Ye et al. (2009) and Dzikati et al. (2011) were able to show in their investigations on citrus vegetation based on hyperspectral imagery that the photochemical reflectance index (PRI) is very suitable for characterising vegetation phenology. Nakaji et al. (2006) were also able to quantify seasonal changes in coniferous forests by using the PRI. Kneubühler (2002) also confirms that the phenological stages can be differentiated from one another by looking at the water content of the vegetation. Kneubühler (2002) even considers the water content of vegetation to be one of the most promising vegetation parameters used to estimate and derive phenological stages. Filella et al. (2004) looked at how the remote sensing vegetation indices NDVI and PRI responded to seasonal and annual changes in an early successional stage of the canopy for Mediterranean coastal shrubland. They were able to show that the NDVI and PRI are good indicators strongly reflecting the species. The projection and knowledge of individual phenological phases helps to answer questions about plant growth and in selecting or recommending optimal times to apply fertiliser or watering and when to implement pest management, as well as how to avoid stress during critical stages of growth in crop growing. The acquisition, projection and modelling of vegetation phenology is therefore a very important variable for precision farming. So far, however, no approaches are known that connect the reflection properties of hyperspectral sensors on the biochemical– biophysical vegetation properties of the plant with certain phenological phases. There exist no approaches for characterization and classification of different phenology stages. Therefore, the objective of this paper was to create a model to predict different phenological BBCH macro-stages of barley based on imaging hyperspectral sensor data in a laboratory on the plot scale as well as transfer the best predictive model from the plot level to the landscape level for modelling different phenological stages of barley. Another important aim of the investigation was to identify the biochemical–biophysical vegetation parameters that are decisive indicators of the phenological development and changes of barley. 2. Methodical approach for determining phenology with hyperspectral RS data Determining phenological stages of crop vegetation are based on two approaches which are linked to each other (c.f. Fig. 1). The basis for model building is a monitoring experiment carried out in a spectral lab over the entire vegetation period (Lausch et al., 2013a). The goal of the monitoring is to obtain systematic and comparable measurements of spectral, biochemical–biophysical and phenological properties of barley crop vegetation performed over the entire vegetation period (c.f. Table 2). By way of machine learning methods (c.f. Section 3.5), the data model gained from the spectral lab experiment is trained, tested, validated and evaluated. The best model gained from the experimental approach is used in the airborne hyperspectral remote sensing data with the goal of predicting phenological

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Fig. 1. Flowchart of the methodical approach for deriving phenology with hyperspectral RS data, LibSVM – support vector machines, RCCW – Recursive Conditional Correlation Weighting selection algorithm.

stages in barley (Fig. 1,Table 2). Procedure, data and approach of the methodical approach for phenological model building and classification of phenology on the landscape level are shown in Fig. 1. 3. Data and methods 3.1. Vegetation material and experimental setting We sowed eight cultivars of spring barley (Hordeum vulgare L.) in containers with a width of 0.8  0.8 m and a height of 0.5 m

(Fig. 2c). Four sampling plots were cultivated on a chernozemic soil (Altermann et al., 2005) and four on a banded sandy para-brown soil to introduce some heterogeneity to the experiment. The distance between the seeding rows was 12.5 cm as it is applied on the landscape scale. The fertilization treatment was chosen at the common optimal level for the soil types applying 15 g of nitrogen, 15 g of phosphorus and 20 g of potassium. After seeding, all plant plots were treated under normal water conditions that are represented by 60% of maximum soil water capacity. A total number of 176 sampling points were measured on 22 days distributed over seven phenological stages from tillering to

Fig. 2. (a) Schematically display of different phenological BBCH stages of barley (b) vegetation of spring barley in 0.8  0.8 m containers in laboratory experiment (c) construction of the laboratory experiment (d) imaging hyperspectral sensors AISA-AEGLE/HAWK mounted on the ceiling, (e) quantification of imaging vegetation indices over the whole vegetation period and different BBCH-stages of barley.

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senescence classified after the BBCH-scale or code. The BBCH code (Biologische Bundesanstalt, Bundesamt and Chemical Industry [the German scale used to identify the phenological development stages of a plant], Munger et al., 1998; Witzenberger and Hack, 1989) was developed by the German Federal Variety Authority (BSA), the German Federal Biological Research Centre for Agriculture and Forestry (BBA) and the German National Association of Manufacturers of Agrochemical Products (TVA). The decimal code used in the BBCH is a standardized description and classification of the phenological development process of crop vegetation. Due to great variations in the length of single phenological stages, the number of sampling points also varies substantially. To provide reasonably uniform data sets, measurements from some phenological stages were collected to represent a phenological group for final analyses (Table 1). To evaluate imaging spectral indices in terms of their sensitivity to biochemical and biophysical vegetation parameters, the following variables were measured over an entire growing season (DOY 117 to 201, 22 measurement days) of spring barley: leaf area index (LAI), chlorophyll meter value – Chl-SPAD-502, canopy chlorophyll content (CCC), leaf gravimetric water content (GWC), C-content, N-content, C/N-content and vegetation height (VH). The vegetation characteristics of spring barley over an entire growing season are shown in Table 1.

that correlates with the chlorophyll concentration in plants (Atzberger et al., 2003; Haboudane et al., 2002; Nakano et al., 2006). We randomly selected 10 leaves in each container and their SPAD-502 readings were recorded. The average from these SPAD readings was then calculated and later converted into Chlab (units: mg cm
2) by means of our empirical calibration function. The total canopy chlorophyll content (CCC, units g m
2) for each plot (container) was calculated by multiplying the Chlab by the corresponding LAI CCC = LAI  Chlab (1)

3.2.3. Leaf gravimetric water content (GWC) Destructive biomass sampling was carried out to retrieve fresh biomass (fresh weight FW) and information on the vegetation water content of plants from weighing. The percentage of leaf water content was calculated from the difference between fresh weights (FW) and dry weights (DW). For this the plant material was dried at 60  C in an oven until a constant weight was obtained (dry weight, DW). To avoid gaps in the canopy, plant samples were taken from an outer row which was not included in the final analyses. Leaf GWC was calculated using Eq. (2) (Colombo et al., 2008). GWC ¼

3.2. Measurements of vegetation parameters 3.2.1. Leaf area index (LAI) To take the leaf area index (LAI; m2 m
2) measurements of spring barley a LI-COR, Inc. (Lincoln, Nebraska, USA; LI-COR, 1992) LAI-2000 Plant Canopy Analyzer was used (Welles and Norman, 1991). This system compares above- and below-canopy light levels detected in five conical rings, with the view zenith angle ranging from nadir to 75 to infer LAI and characteristics of canopy architecture. At each sampling plot two measurements were taken, each of which involved the sampling of three LAI values and an average of six observations. All LAI measurements were taken on the same day that the spectral measurements were conducted. 3.2.2. Chlorophyll measurements (Chl SPAD, CCC) Determining leaf chlorophyll content on a laboratory scale is very time consuming and requires a wide range of equipment, therefore a hand-held chlorophyll meter was used for groundtruthing the vegetation canopy “greenness”. Hence, a ChlorophyllMeter SPAD-502 (Minolta Osaka Company, Ltd., Japan, Minolta, 2003) was used to provide rapid and reasonable estimates of leaf chlorophyll characteristics (Markwell, 1995). The SPAD-502 measures the transmittance of plant leaves in the red (650 nm) and NIR (920 nm) spectral wavelength bands. The ratio of these two transmittances is proportional to the total leaf chlorophyll content. The ability to predict chlorophyll content from SPAD-502 has been demonstrated in different studies (Markwell et al., 1995; Yang et al., 2003; Darvishzadeh et al., 2008). To test the relationship between SPAD measurements and the direct units of leaf chlorophyll concentration we carried out measurements with the organic extraction of chlorophyll a and chlorophyll b. SPAD measurements provide a unit-less, but very reproducible measure

(1)

ðFW
DWÞ DWð%Þ

(2)

3.2.4. C-content, N-content, C/N-content To investigate the vegetation parameters C-, N- and C/Ncontent on every day that measurements were taken, vegetation samples were taken from leaves, stems and ears of spring barley. These were dried in a drying chamber for 24 h at 60  C and subsequently dried for 24 h. The samples were then pulverised into a very fine powder (Schwingmühle, Typ MM2, Fa. Retsch). The analyser TruSpec CHN Macro (LECO Instrumente GmbH) was used to record the C-content and the N-content of the vegetation. Recordings were taken twice for all samples. The recordings of Ccontent, N-content, C/N-content were calculated as the percentage weight (%) of leaves, stems and ears as the total C-content, Ncontent as well as C/N-content of the plant. 3.2.5. Vegetation height (VH) The vegetation height of spring barley was calculated by taking the average from three measurements each time. 3.3. Imaging spectrometer data 3.3.1. Imaging spectrometer data – plot level The AISA-EAGLE (395–973 nm) Airborne Imaging Spectrometer for Applications sensor was mounted onto a lifting platform at a height of 2.6 m above the ground (Fig. 2c). The parameters of the recorded imaging hyperspectral data can be found in Table 2. In front of the AISA sensor optical lenses, rotating mirrors were installed to guide illumination to the line of the sensors and to retrieve imaging data. A dark room of 5 m  5 m made of lightproof material was constructed for the hyperspectral measurements. The use of this kind of dark room prevents any disruptive factors from

Table 1 Vegetation characteristics of spring barley over an entire growing season. Phenological macro-stages after BBCH

Code for phenological stage

Time period [DOY]

No. of sampling points

Phenological description

2 3, 4, 5 7 8, 9

2 5 7 9

117–144 145–166 167–180 181–201

56 48 30 42

Tillering Stem elongation, booting, heading Development of fruit Ripening, senescence

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Table 2 Parameters of the recorded imaging hyperspectral data for laboratory and aircraft platforms. Recording date

Recording ground resolution [m]

5 month, twice a week 2009- 0.025 04–27–2009–07-20 1 2010-06–15

Focal length

FOV Swath [m] [degree]

18.5

36, 7

18.5

36, 7

Spectral range [nm]

0.5  0.5 395–973 512

having an effect over the entire series of tests. Light was provided by artificial light sources using 2000-W Thungsten halogen quartz lamps (Kaiser StudioLight, Kaiser Fototechnik, Buchen, Germany). The observation height was set at two meters above the canopy of the plant material. After the data had been recorded the atmospheric corrections and the transformation of radiances to reflectances were carried out using a spectralon reflectance panel using a procedure after (Carter, 1994). The semi-geometric correction was realised using a master-slave geometric correction approach under ENVI. After preprocessing, the hyperspectral data were then referred to as ground reflectance data with a spatial ground resolution of 0.2 cm and a FOV of 0.5  0.5 m. 3.3.2. Airborne imaging spectrometer data – landscape level To model the phenological BBCH macro-stages we used a study area situated in the Harz region of central Germany (Fig. 3). The area is part of the TERENO long-term monitoring region (Terrestrial Environmental Observatories, www.tereno.net, Zacharias et al., 2011). Imaging was carried out using the hyperspectral sensor AISA-EAGLE along a 25 km land cover gradient (N51.36 , E11,00 – N51.45 , E11,26 ) with a ground resolution of 1 m (recording date 2010-06-15). Other parameters of the recorded imaging hyperspectral data can be found in Table 2. After the data had been recorded, the airborne AISA-EAGLE raw data were radiometric-corrected based on the procedure CaliGeo (Spectral Imaging Ltd.) run under ENVI (ITT Visual Information Solution, Boulder, CO, USA). Following radiometric correction, ocular linear and non-linear miscalibrations of the hyperspectral data were reduced by implementing an image-driven, radiometric recalibration and rescaling method (ROME: reduction of miscalibration effects; Rogaß et al., 2011). The atmospheric correction was performed using the software ATCOR4 (Richter and Schlapfer, 2002) and the radiative transfer algorithm MODTRAN-2. The program corrects at-sensor radiance images for solar luminance, aerosol and Raleigh scattering. The ATCOR program was adapted for the specific band characteristics of the AISA sensors. The orthorectification of the airborne hyperspectral image was carried out using a digital elevation model (DEM) together with the geocoding procedure CaliGeo. After pre-processing, the hyperspectral data were referred to as ground reflectance data with spatial ground resolutions of 1 m. 3.4. Calculation of spectral vegetation indices and spectral derivatives In the current study several indices and index types were calculated based on the imaging hyperspectral sensor AISA-EAGLE and tested according to their suitability to differentiate between the phenological stages (BBCH macro-stages) of spring barley over an entire vegetation period. The spectral indices used were categorised into four groups:

395–973

Spatial pixel

Spectral resolution [nm]

Spectral bands

Sensors

512

2.5–3.0

252

512

2.5–3.0

252

AISALaboratory EAGLE + mirror AISA-EAGLE Aircraftcessna 206

Platform

level. Fig. 4 contains the formulas and references of the vegetation indices that were tested in this study. (ii) Reflectance VIs: for the second type of indices we used the reflectance value (R(X)) at the central wavelength (x nm) of each band of the imaging AISA-EAGLE spectrometer (400– 970 nm, 252 spectral bands). (iii) Subtraction VIs(xy): we tested all combinations of 252 AISAEAGLE bands and used a formula that also includes two bands. (iv) A combination of all three VI types.

3.5. Statistical approach for prediction of phenological stages The aim of the statistical analysis was to investigate the predictive power of the four different imaging spectral vegetation index types (see Section 4.2) to predict the phenological BBCH macro-stages 2, 5, 7 and 9 for spring barley over an entire vegetation period (DOY 117–201). Firstly, we replaced any missing values and normalized the hyperspectral data information with a range transformation method from 0 to 100. To avoid over-fitting the models, for all approaches we used a recursive conditional correlation weighting selection algorithm (RCCW) to reduce these to the relevant feature sets after Schowe and Morik (2011). The RCCW removes the features which have the weakest conditional correlation with the label. This process of block-wise elimination is repeated until only k features are left (in our case = three). For the prediction function of the phenological macro-stages we used the support vector machine learner based on the Java LibSVM as a classification algorithm by Chang and Lin (2002). In contrast to other SVM learners, the LibSVM also supports internal multiclass learning and probability estimation based on scaling for proper confidence values after applying the learned model on a classification data set. To test the performance of all models, a ten-fold crossvalidation method with stratified sampling was applied (Efron and Tibshirani, 1993). As performance criteria for the predictive power of the phenological macro-stages the following statistical indicators were used: classification accuracyCV, kappaCV (Cohen, 1968) and Kendall’s tauCV (Kendall and Bradford, 1953). The best model for predicting phenological macro-stages of barley on the plot level was transferred to the airborne imaging hyperspectral AISA-EAGLE data. Based on this, the macro-stages 2, 5, 7 and 9 of barley were modelled on the landscape level. The calculations of all spectral vegetation indices of imaging hyperspectral data were carried out using ENVI/IDL and MatLab. The statistical data analysis was conducted using the software packages MatLab and RapidMiner V.5.3. 4. Results 4.1. Temporal behaviour of biophysical vegetation parameters for phenological stages

(i) Published VIs: published vegetation indices from relevant

literature were used. Most of these were developed from hyperspectral image data and/or from the implementation of radiative transfer models (RTM) for use at the leaf and canopy

For all phenological macro-stages at the same time as all imaging spectral measurements were recorded, the vegetation parameters: leaf area index (LAI), chlorophyll meter value

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Fig. 3. Study area of the TERENO region located in Saxony-Anhalt, Germany.

(Chl-SPAD), canopy chlorophyll content (CCC), leaf gravimetric water content (GWC), C-content, N-content, C/N-content and vegetation height (VH) were also measured over an entire growing season of spring barley (DOY 117–201). The distribution of the value ranges of all vegetation variables for the respective macrostages as well as the temporal dynamics of DOY 117–201 can be taken from Table 1 and Fig. 5a–p. 4.2. The importance of biophysical vegetation parameters for predicting phenological macro-stages Based on the model approach described in Section 3.5 the significance of each individual vegetation parameter: leaf area index (LAI), chlorophyll meter value – Chl-SPAD-502, canopy chlorophyll content (CCC), leaf gravimetric water content (GWC), C-content, N-content, C/N-content and vegetation height (VH) was

investigated to differentiate the phenological BBCH macro-phases 2,5,7, and 9 of spring barley. The GWC was found to be the most crucial vegetation variable with a classification accuracy of 82.97% in separating the different phenological BBCH phases (Fig. 6, Table 3). The variables C/Ncontent, Chl as well as CCC when considered as individual variables also have a high predictive power to differentiate between the phenological macro-stages with a classification accuracy of ca. 70%. In addition to the predictive power of every individual vegetation parameter the power to differentiate between the phenological macro-states from a combination of different vegetation variables was tested in the model. The highest predictive power was obtained with a classification accuracy of 84.64% through a combination of the vegetation variables GWC, Ccontent and N-content (Table 4).

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Fig. 4. Vegetation indices that are proposed in the relevant literature, which are included in the analysis and known to have a relationship with phenological parameters of the BBCH scale (Babar et al., 2006; Blackburn, 1998; Broge and Leblanc, 2000; Buschman and Nagel, 1993; Carter and Miller, 1994; Chen, 1996; Chen et al., 2005; Cho and  uelas et al., 1995; Skidmore, 2006; Datt, 1999; Gitelson et al., 1996; Gitelson and Merzlyak, 1994; Huete et al., 2002; Lichtenthaler et al., 1996; Merzlyak et al., 1999; Pen Penuelas et al., 1994; Peñuelas et al., 1997; Pontius et al., 2005; Pontius et al., 2008; Qi et al., 1994; Rondeaux et al., 1996; Rouse et al., 1974; Sims and Gamon, 2002; Vogelmann et al., 1993; Zarco-Tejada and Miller, 1999; Zarco-Tejada et al., 2001).

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Fig. 5. Measured biophysical vegetation parameters: (a) leaf area index (LAI) vs. DOY, (b) leaf area index (LAI) vs. phenological BBCH macro-stages, (c) chlorophyll meter value – Chl-SPAD-502 vs. DOY, (d) chlorophyll meter value vs. phenological BBCH macro-stages, (e) canopy chlorophyll content (CCC) vs. DOY, (f) canopy chlorophyll content (CCC) vs. phenological BBCH macro-stages, (g) leaf gravimetric water content (GWC) vs. DOY, (h) leaf gravimetric water content (GWC) vs. phenological BBCH macro-stages, (i) Ccontent vs. DOY, (j) C-content vs. phenological BBCH macro-stages, (k) N-content vs. DOY, (l) N-content vs. phenological BBCH macro-stages, (m) C/N-content vs. DOY, (n) C/Ncontent vs. phenological BBCH macro-stages, (o) vegetation height (VH) vs. DOY, (p) vegetation height (VH) vs. phenological BBCH macro-stages over an entire growing season of spring barley, DOY 117–201.

With the help of the confusions matrix (Table 4) it can be seen that with a combination of the vegetation variables GWC, -content and N-content in particular the BBCH-stage 7 with a class precision of 60.87% is the most difficult to classify compared to all other phenological macro-stages. The phenological macro-stage 2 is the easiest to predict with a classification precision of 100% using the vegetation variables GWC, C-content and N-content.

published VI’s, reflectance VI’s and VI(xy) formula combinations – for the classification of phenological macro-stages 2, 5, 7 and 9 for spring barley DOY 117–201.

4.3. Hyperspectral indicators for predicting phenological macro-stages The aforementioned analyses were conducted to investigate the prediction power of (i) published VI’s, (II) reflectance VI’s, (III) VI formula combinations as well as (IV) the comprehensive model including all three types of VI’s to classify the macro-stages 2, 5, 7 and 9 of the BBCH scale for spring barley on the plot scale over an entire vegetation period. Table 5 compiles the results from all of these models. All models were found to fit very well to macro-stage 2, achieving a class precision of 94–96%. Likewise, all models demonstrated a good fit with 88–92% precision for macro-stage 9. There were greater differences in the predictive power of the models for macro-stages 5 and 7. Classification performance of a combination of the best three k-variables – comprehensive model with all three VI types:

Fig. 6. Predictive power of each individual measured vegetation parameter – leaf gravimetric water content (GWC), C/N-content (C/N), chlorophyll meter value – (Chl), canopy chlorophyll content (CCC), N-content (N), C-content (C), leaf area index (LAI), vegetation height (VH) to differentiate between the phenological BBCHmacro-stages 2, 5, 7 and 9 for spring barley. Expression of the predictive power in terms of classification accuracy [%].

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Table 3 Classification performance of the combined first, second and third best vegetation variables to classify the phenological BBCH macro-stages 2, 5, 7 and 9 for spring barley, DOY 117–201. No. of important k-variable

Biochemical variable

Classification accuracy [%]

Kappa

Kendall’s tau

1 2 3

GWC GWC, C-content GWC, C-content, N-content

82.97 (5.07) 82.35 (6.44 84.64 (6.76)

0.766 0.797 0.790

0.914 0.931 0.917

Table 4 Classification performance from a combination of the best three k-vegetation variables: GWC, C-content, N-content to classify the phenological macro-stages 2, 5, 7 and 9 for spring barley, DOY 117–201. Best k-variables: GWC, C-content, N-content, classification accuracy = 84.64% (6.76%), kappa = 0.790, Kendall’s tau = 0.917 Macro-stagesa Pred. Pred. Pred. Pred. Class a

2 5 7 9 recall

True 2

True 5

True 7

True 9

Class precision

54 1 0 1 96.43%

0 41 7 0 85.42%

0 5 14 11 46.67%

0 0 2 40 95.24%

100.00% 87.23% 60.87% 76.92%

Macro-stages of BBCH scale for barley.

The best model fit to classify the macro-stages 2, 5, 7 and 9 was obtained from the comprehensive model that included all three VI types with a classification accuracy of 84.80% (Table 5). This best model from the plot level was then applied to the airborne hyperspectral AISA-EAGLE data (c.f. Section 3.3.2) on the landscape level. The results from modelling the phenological BBCH macro-stages of barley can be clearly seen in Fig. 7. It was possible to differentiate between the BBCH macro-stages for barley. 5. Discussion 5.1. Statistical model approach The use of hyperspectral sensors to analyse and model the phenological macro-stages of vegetation opens up extensive opportunities for more specific investigations compared to conventional optical remote-sensing data that uses only a limited number of spectral bands. By using the AISA-EAGLE hyperspectral sensor in the wavelength range 395–973 nm 252 spectral bands could be recorded. If from these 252 spectral bands other additional indicators are formed through the function generation of different band combinations, then in our analyses with observations n = 176 and plots p = 63.251 different spectral features result. Traditional methods of statistical analysis as well as machinelearning algorithms such as support vector machines (SVM),

random forests or naive Bayes used for their robustness to the dimensionality of the data lead to an over-fitting of the predictive models (p > n) due to an excessive number of variables (Schowe and Morik, 2011). According to Schowe and Morik (2011) the use of high-dimensional data prove to be challenging in several ways: (I) more complex models are difficult to understand and communicate, (II) the p-dimensional space grows exponentially in p compared to the number of observations, (III) the greater the number of dimensions, the greater the variance, whereby the stability of the model and therefore the selection of variables changes. To avoid an over-fitting of our models, in all approaches we used a recursive conditional correlation weighting selection algorithm (RCCW) to reduce the relevant feature sets (Schowe and Morik, 2011). The RCCW removes the features, which have the weakest conditional correlation with the label. This process of block-wise elimination is repeated until only k features are left (in our case = three). In this study the classifiers Naive Bayes, Random Forest as well as LibSVM were tested and compared in the classification of macro-stages. For this comparison the data set of the published VI's was used against the macro-stages with the RCCW feature selection of the 3 best k-features. The LibSVM learner was found to have a classification accuracy of 82.39% (8.34%), the Naïve Bayes classifier a classification accuracy of 78.46% (10.22%) compared to the Random Forest classifier with a classification accuracy of 75.10% (10.14%). The best classification results were thus achieved by implementing the LibSVM classifier, which we then subsequently implemented as the classifier for all predictive models. 5.2. Important biochemical–biophysical vegetation parameters An important goal of the study was to find out which biochemical and biophysical vegetation parameters measured during the investigations best enable a differentiation of the individual phenological macro-stages i.e. leaf area index (LAI), chlorophyll meter value – SPAD-502, canopy height, canopychlorophyll-content (CCC), leave gravimetric water content (GWC), C-content, N-content, C/N-content and vegetation height (VH) of spring barley. When comparing all measured biochemical and biophysical variables GWC performs best at separating

Table 5 Best model fits for the classification of macro-stages 2, 5, 7 and 9 based on published VI’s, reflectance VI’s, VI formula combinations and all three VI types for spring barley over an entire vegetation period, DOY 117–201. Macro-stagesa

Y = 542 nm Y = 595 nm Y = 646 nm PRIRDVIWBI published VI’s true 2/ reflectance VI’s true 2/5/7/9 5/7/9

Y = (399 + 507) nm Y = (527 + 612) nm Y = (540 + 639) nm VI formula comb. true 2/5/ 7/9

Y = 746 nm Y = (527 + 612) nm Y = (540 + 639) nm all three VI types true 2/ 5/7/9

94.00% 74.07% 68.97% 88.37% 82.39%(8.34%)

95.65% 70.31% 78.26% 88.37% 82.48%(10.62%)

96.00% 75.00% 78.26% 88.37% 84.71%(9.33%)

95.83% 74.14% 75.86% 92.68% 84.80%(10.14%)

(8.34%) 0.761

0.762 0.894

0.792 0.912

0.795 0.912

Class precision Pred. 2 Pred. 5 Pred. 7 Pred. 9 Total classification accuracy Kappa Kendall’s tau a

Macro-stages of BBCH scale for barley.

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Fig. 7. (a) AISA-EAGLE hyperspectral image, recording date – 2010-06-15, 225 spectral bands (395–973 nm), 1 m ground resolution, showing 3 spectral bands as a CIR-Image, (b) modelling of the phenological macro-stages of barley based on the best predictive model – a comprehensive model including all three VI types – published VI’s, reflectance VI’s and VI(xy) formula combinations: Y = 746 nm; Y = (527 + 612) nm; Y = (540 + 639) nm.

thephenological macro-stages 2, 5, 7 and 9 of spring barley with a classification accuracy of 82.97%. The best model fit for predicting the phenological macro-stages of barley was obtained with a classification accuracy of 84.64% from a combination of the biochemical vegetation variables GWC, C-content and N-content. The fact that vegetation water content (GWC) is very good at separating the phenological macro-stages was already established by Kneubühler (2002) who found vegetationwater content to be the most promising vegetation parameter for estimating and deriving phenology. Kneubühler (2002) modelled the vegetation water content of barley via senescence using regression with an RMS-error of 3–8%. Dzikati et al. (2011) showed that by using hyperspectral data the seasonal trend of canopy reflectance of citrus trees can be described very well using water indices. Our investigations also show that in addition to the GWC, the C-content as well as the N-content are other important biochemical indicators that can be used to differentiate between the different macro-stages. In our investigations chlorophyll could not

be identified as a decisive basis for photosynthesis as an indicator of senescence or for the classification of different macro-stages. Similarly, Kneubühler (2002) was not able to successfully model leaf chlorophyll over several vegetation stages by using wavelength ration in the visible and near infrared fields of non-imaging hyperspectral data. According to Kneubühler (2002) LAI is an important key parameter in terms of plant vitality; however his models are only possible in the various senescence phases with an RMS-error of 20%. The current results from our investigations confirm this. LAI was not an important variable for differentiating between senescence phases over an entire growing cycle of spring barley. 5.3. The best model for predicting phenological macro-stages To investigate a separation of the phenological macro-stages 2, 5, 7 and 9 of spring barley 58 of the known published VI’s were included

Fig. 8. The root mean squared error (RMSE) of each reflectance value (R(X)) at the central wavelength (x nm) of the imaging AISA-EAGLE spectrometer bands (395–973 nm, 252 spectral bands) calculated using linear regression analysis for the seven biochemical–biophysical vegetation variables, (a) RMSE for leaf area index (LAI), (b) RMSE for Chl-SPAD, (c) RMSE for C/N-content, (d) RMSE for N-content, (e) RMSE for canopy chlorophyll content (CCC), (f) RMSE for leave gravimetric water content (GWC), (g) RMSE for C/N-content, (h) RMSE for vegetation height (altered and modified according to Lausch et al., 2013b).

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in the analyses. Our results show that the best fit to predict the different macro-stages occurs from a combination of the published VI’s PRI (photochemical reflectance index), RDVI (renormalized difference vegetation index) as well as the WBI (water band index) with a classification accuracy of 82.39% (8.34). The PRI is a physiological reflectance index which according to Gamon et al. (1992) is sensitive towards the xanthophyll cycle pigments and efficiency of photosynthesis. It is often referred to as the indicator of water stress for canopy vegetation, as it shows the de-epoxidaton of the xanthophyll pigment cycle caused by water stress (Suárez et al., 2008). After Suárez et al. (2008) the PRI is sensitive to diurnal changes in physiological indicators of water stress. Winkel et al. (2002) demonstrated the sensitivity of the PRI to water stress conditions, although structural effects in the vegetation canopy also influence the reflectance signal. Nakaji et al. (2006) were able to use the PRI to demonstrate seasonal changes on the leaf level of vegetation. From our own investigations on linear regression with each reflectance value (R(X)) at the central wavelength (x nm) of the imaging AISA-EAGLE spectrometer bands (c.f. Fig. 8f) low RMSE values were obtained for the GWC around wavelengths of 530–560 nm. As the PRI is calculated exactly with the wavelengths of 531 and 570 nm (Fig. 4) we presume that the differentiation of the different macro-stages is carried out in particular through the sensitivity to the GWC. This fits very well with the results presented here in terms of the significance of biochemical–biophysical vegetation parameters, whereby the GWC, the C-content and the N-content were identified as important vegetation variables for predicting the phenological macro-stages of barley. The WBI was found to be another important published VI for the predictive power in classifying the phenological macro-stages of barley. According to Naumann et al. (2008) the WBI is sensitive to changes in canopy water status. Furthermore, the RDVI or the renormalized difference vegetation Index (Rougean and Breon, 1995) was also identified as another important published VI. The calculation of the RDVI was carried out with the wavelengths 670 nm and 800 nm (Fig. 4). According to our own model calculations the wavelength 670 nm showed a low RMSE in terms of Chl-SPAD (Fig. 8b) whereas the wavelength 800 nm showed a low RMSE in terms of LAI (Fig. 8a). These results also confirm the works of Haboudane et al. (2004) and Miao et al. (2009), who were able to model LAI well using the RDVI. With the help of the RDVI it can be demonstrated that in addition to the GWC the Chl-SPAD as well as the LAI also play a role as indicators for deriving senescence, even though this is quite low when compared to the vegetation parameters GWC, C-content und N-content. The best model fit for predicting the phenological macro-stages 2, 5, 7 and 9 was obtained from the comprehensive model with all three VI types: published VI’s, reflectance VI’s and VI formula combinations with a classification accuracy of 84.80% (10.14%). For the classification model with reflectance VI’s Y = 746 nm, as well as with VI formula combinations Y = (527 + 612) nm and Y = (540 + 639) nm are used. The wavelength 746 nm is particularly suitable for the vegetation variable GWC to show C-content, N-content as well as C/N-content. Furthermore, the wavelength 746 nm also shows a low RMSE in the linear regression model in terms of growth height. This proves that the separation of the phenological macro-stages is also influenced by structural parameters as is shown by the growth height. To classify the phenological macro-stages the VI formula combinations Y = (527 + 612) nm and Y = (540 + 639) nm are equally important. The wavelengths 512 nm and 540 nm integrated into these reflectance VI’s can be identified as indicators of GWC (Fig. 8f) as well as C/N-content (Fig. 8g), the wavelengths 612 nm and 639 nm are determined in the linear model to characterise Chl-SPAD (c.f. Fig. 8b) as well as CCC (Fig. 8e).

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5.4. Transferring the model from the plot level to the landscape level The results of the best predictive model for the phenological macro-stages of barley were transferred to the airborne hyperspectral AISA-EAGLE data set (recording date 2010-06-15, 1 m ground resolution). Based on the best predictive model the phenological macro-stages 2, 5, 7 and 9 of barley were modelled on the landscape level (Fig. 7). The results from the model show that at the time of recording the airborne hyperspectral data on 2010-06-15 the macro-stages 5 and 7 were able to be classified. A validation of the model results in the classified field could not be carried out. Possible errors in predicting the phenological BBCH macro-stages of barley could be accounted for by the following: (i) The classification error of the phenological macro-stages is 15.20%. The highest inaccuracy here was found for the assignment to macro-stage 7. Hence, this class had the weakest model. (ii) All 4 phenological stages were modelled at the time of recording the hyperspectral data on 2010-06-15. However, the occurrence of 4 phenological macro-stages on one day is not physiologically possible. Therefore, macro-phases of vegetation might have been modelled from the field that cannot be assigned to barley. Hence, parts of the image might have been classified that could either be channels or areas with high crop failure due to extreme weather events, subsequently colonised by ruderal vegetation. Furthermore, a strong growth of weeds in the barley stand can also reduce the correct classification of the phenological macro-stages. Kneubühler (2002) found imprecise vegetation parameters only as a result of weeds in the barley stand during the growth phase. 6. Conclusion Up until now it has not been possible to investigate the suitability of hyperspectral RS data for depicting the phenology of vegetation due to the lack of airborne and satellite hyperspectral remote sensing data. In 2017, the first hyperspectral satellite (EnMAP) is slated to be launched with a spectral range of 400–2500 nm. This will make it possible for the first time to investigate the entire range of biochemical–biophysical vegetation characteristics which are important for the classification of phenology. The current study describes a method whereby the spectral response of the imaging hyperspectral remote sensor (ASIAEAGLE) was measured in terms of a change to the vitality-related biophysical and biochemical parameters over a long-term vegetation monitoring period (DOY 117–201) under controlled and therefore comparable conditions in the laboratory. The aim of this study was to create a model to predict the different phenological macro-stages of barley in the laboratory on the plot scale and then to transfer the best model to predict the macro-stages of barley on the landscape level. The model approach to depict the phenology of crop vegetation is comprised of two separate methodical experiments. 1. The long-term monitoring attempt in a spectral lab involved

recording important variables in phenology imaging and non-imaging RS spectroscopy under standardized and comparative measuring conditions as well as carrying out parallel comprehensive vegetation measurements. To characterise the phenology eight vitality and phenology-related vegetation parameters: leaf area index (LAI), Chl-SPAD, C-content, N-content, C/N-content, canopy chlorophyll content (CCC), leaf gravimetric water content (GWC) and vegetation height (VH) were recorded at the same time as all imaging spectral measurements. These biochemical–biophysical vegetation parameters were investigated in terms of their predictive power to classify phenological BBCH macro-stages 2, 5, 7 and 9 of

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barley. From the individually investigated eight vegetation variables the GWC was found to be the most important variable to differentiate between the phenological macro-stages 2, 5, 7 and 9 of barley. The best model was built on the basis of this lab experiment by using machine learning methods (LibSVM) and produced a classification accuracy of 84.80%. For the classification model with the reflectance VI’s Y = 746 nm, as well as the VI formula combination Y = (527 + 612) nm and Y = (540 + 639) nm. The minor classification error was achieved in the lab because only the barley was kept under ideal growth conditions, and there were no disturbing factors in the lab such as weeds, the elements or damage from being walked on. 2. The best model from the long-term investigations on the plot was then transferred to the data of the airborne hyperpsectral campaign data. Very advantageous for transferring the model was the fact that for both analyses on laboratory plot and landscape level the same imaging hyperspectral sensor AISAEAGLE was used (OSADIS, One Sensor At Different ScalesApproach, Lausch et al., 2013a), meaning that sensor-specific differences in the analysis were eliminated. Since no parallel ground truth measurements were carried out during the flight period, a field validation could not be performed. Non-classified areas in the airborne hyperspectral data are possible due to weeds, ruderal vegetation or clusters of barley in the field since these effects occur mainly on the edge and near channels. In addition to hyperspectral flights, future investigations absolutely have to perform a comprehensive ground truth analysis in the field to determine the BBCH stages to be able to ideally validate the model result in the field. The approach described in the paper for determining the phenology based on imaging hyperspectral RS data constitutes an important basis for understanding spectral response in the context of phenology patterns of plants. References Altermann, M., Rinklebe, J., Merbach, I., Korschens, M., Langer, U., Hofmann, B., 2005. Chernozem – soil of the year 2005. J. Plant Nutr. 168, 725–740. Atzberger, C., Jarmer, T., Schlerf, M., Kötz, B., Werner, W., 2003. Retrieval of wheat bio-physical attributes from hyperspectral data and SAILH+PROSPECT radiative transfer model. Habermeyer, M., Müller, A., Holzwarth, S. (Eds.), Proc. 3rd EARSeL Workshop on Imaging Spectroscopy 473–482. Bégué, A., Vintrou, E., Ruelland, D., Claden, M., Dessay, N., 2011. Can a 25-year trend in Soudano-Sahelian vegetation dynamics be interpreted in terms of land use change? A remote sensing approach. Global Environ. Change Human Policy Dimensions 21, 413–420. Babar, M.A., Reynolds, M.P., van Ginkel, M., Klatt, A.R., Raun, W.R., Stone, M.L., 2006. Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci. 46, 578–588. Badeck, F., Bondeau, A., Böttcher, K., Doktor, D., Lucht, W., Schaber, J., 2004. Responses of spring phenology to climate change. New Phytol. 162, 295–309. Blackburn, G.A., 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyper-spectral approaches. Remote Sens. Environ. 66, 273–285. Botta, A., Viovy, N., Ciais, P., Friedlingstein, P., Monfray, P., 2000. A global prognostic scheme of leaf onset using satellite data. Global Change Biol. 6, 709–725. Broge, N.H., Leblanc, E., 2000. Comparing prediction power and stability of broadband and hyper–spectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76, 156–172. Buschman, C., Nagel, E., 1993. In vivo spectroscopy and internal optics of leaves as a basis for remote sensing of vegetation. Int. J. Remote Sens. 14, 711–722. Carlson, T.N., Ripley, D.A., 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 62, 241–252. Carter, G.A., Miller, R.L., 1994. Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sens. Environ. 50, 295–302. Carter, G.A., 1994. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int. J. Remote Sens. 15, 697–703. Chang, C.C., Lin, C.-J., 2002. Training v-support vector regression: theory and algorithms. Neural Comput. 14, 1959–1977. Chen, D.Y., Huang, J.F., Jackson, T.J., 2005. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and shortwave infrared bands. Remote Sens. Environ. 98, 225–236.

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