An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression

Agricultural and Forest Meteorology 275 (2019) 146–158

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Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet

An improved surface soil moisture downscaling approach over cloudy areas based on geographically weighted regression

T

Peilin Songa,b, Jingfeng Huanga,b, , Lamin R. Mansarayc ⁎

a

Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, 310058, PR China Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Zhejiang Province, Hangzhou, 310058, PR China c Laboratoty of Agro-Meteorology and Geo-Informatics, Magbosi Land, Water and Environment Research Centre (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Tower Hill, PMB 1313, Freetown, Sierra Leone b

ARTICLE INFO

ABSTRACT

Keywords: Surface soil moisture (SSM) Land surface temperature (LST) interpolation Passive microwave Downscaling Geographically Weighted Regression (GWR)

This study proposed a methodological framework for downscaling AMSR-2 surface soil moisture (SSM) products over cloudy areas using MODIS LST/NDVI datasets. The experiment was conducted in a relatively large area of 430,000 km2 in the middle and lower reaches of the Yangtze and Huaihe rivers in China, which is characterized by humid climate and frequent cloudy weather conditions. As MODIS LSTs suffer from serious pixel loss due to cloud interference in this area, an effective LST interpolation method was preliminarily applied to achieve daily LST datasets with quasi-full covers. And rather small RMSEs in the range 1.5 K–3.5 K were obtained when the interpolated LST datasets were validated against a reference LST dataset built from observed relationships between LST and ground-based near-surface air temperatures on clear sky days. A regression equation was then established between AMSR-2 SSM and spatially resampled MODIS datasets using “Geographically Weighted Regression (GWR)” to implement the SSM downscaling process. SSM estimates downscaled by the GWR-based method showed a better performance over those downscaled by the traditional “universal triangle feature (UTF)” based method in view of their “non-biased RMSEs (ubRMSEs)”, correlation coefficients, and mean biases with respect to ground-based soil moisture validation data. Comparisons between SSM estimates from MODIS LST inputs and those from interpolated LST inputs were conducted, and they showed that the SSM estimates downscaled by interpolated LST inputs performed only slightly poorer (with an ubRMSE difference no larger than 0.02 cm3/cm3) than those by MODIS data. Time series analysis further showed that the GWR-based downscaled SSM estimates with reconstructed LST data inputs are in phase with the variation in ground-based soil moisture with the exception of areas of extremely high vegetation cover or low temperatures. The framework proposed in this study thus proved feasible for the derivation of reliable downscaled high spatial resolution SSM estimates, an essential application in mitigating pixel loss under cloudy weather conditions.

1. Introduction Surface soil moisture (SSM) is an important variable in terrestrial hydrological cycles and global energy exchanges. SSM interacts with vegetation covers and plays an important role in ecosystem functioning, water resource cycles, and in climate/weather monitoring and prediction systems. With good penetration capability through the vegetation canopy and atmospheric layers coupled with the advantage of allweather observations, passive microwave remote sensing techniques based on datasets observed from space-borne sensors such as the Tropical Rainfall Measuring Mission Microwave Imager (TMI) (Gao et al., 2006), the “Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)” (Njoku et al., 2003), AMSR-2 (Parinussa

⁎

et al., 2015), Soil Moisture and Ocean Salinity Mission (SMOS) (Berger et al., 2002) and Soil Moisture Active Passive Mission (SMAP) (Entekhabi et al., 2010), have played a crucial role in the estimation of near surface soil moisture, especially for global-scale applications. However, the poor spatial resolution of passive microwave sensors (usually several tens of kilometers) as well as the practical constraints on antenna size and low altitude earth orbits, are usually considered not sufficient enough for regional and local scale studies, conditions under which SSM datasets with higher spatial resolution are required. Downscaling passive-microwave-derived SSM products by the synergistic coupling of optical/thermal-infrared datasets is currently one of the most widely applied techniques to obtain SSM representations at high spatio-temporal resolution, with its advantages of easier

Corresponding author at: Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou, 310058, PR China. E-mail addresses: [email protected] (P. Song), [email protected] (J. Huang), [email protected] (L.R. Mansaray).

https://doi.org/10.1016/j.agrformet.2019.05.022 Received 28 March 2018; Received in revised form 13 May 2019; Accepted 22 May 2019 Available online 28 May 2019 0168-1923/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Location, elevation range, and land cover classification of the study area overlaid with 65 national level soil moisture observation stations and 54 meteorological stations used for result evaluations in this study. The 3 soil moisture stations flagged in yellow are representative of the geographical settings and major land cover types: M1 (cropland), M2 (bare land or sparsely vegetated surface), and M3 (forest).

Actually, most of the above-mentioned studies have been conducted in particular regions of restricted areas. However, if applied within a relatively larger region, at least the following concerns should be raised. First, optical or thermal-infrared observations can suffer from severe pixel loss if the region is coupled with continuous cloudy, precipitation, aerosol or snow conditions. Second, for the UTF-based methods, the SSM downscaling model has usually been established at the image scale rather than pixel scale, using a single mathematical equation with fixed empirical coefficients. But this may not always be an optimum model describing the actual relationship between microwave-derived SSM and other land surface parameters since the evapotranspiration pattern is not spatially uniform probably due to heterogeneity in meteorological conditions, local land use types, agriculture model or vegetation cover density. On this premise, this study has proposed a methodological framework by improving the UTF for better downscaling of the 0.25°(˜25 km)-resolution AMSR-2 SSM products to the 1 km resolution of MODIS datasets, taking a large, cloudy and humid area covering the middle and lower reaches of the Yangtze and Huaihe rivers in China as case study. As MODIS LST is an indispensable but drastically changing parameter at daily scales, the first step in this study was to explore an effective and novel interpolation method for the daily MODIS LST images over this study region. Then with the interpolated LST and NDVI datasets of quasi full cover, the “Geographically Weighted Regression (GWR)” method was applied in the SSM downscaling process. The GWR method produces a continuous surface of parameter values through measuring the parameters at each local observation to denote the spatial variations of the surface, instead of estimating globally fixed parameters (Hu et al., 2013). This method has been widely used for spatial interpolation or downscaling of different remotely-sensed data such as precipitation (Chen et al., 2015; He et al., 2016; Kara et al., 2016) or LST (Duan and Li, 2016), though few studies have applied it in SSM downscaling with optical/thermal-infrared datasets (possibly

implementation and no requirements for extensive ground-based data or model-based output. The “universal triangle feature (UTF)” (Carlson et al., 1994, 1990) is a typical kind of method that uses optical/thermalinfrared datasets to fulfill passive microwave SSM downscaling (Sandholt et al., 2002). This method utilizes the feature space of remotely-sensed land surface temperature (LST) over heterogeneous areas against a vegetation index (VI) to capture the spatial and temporal dynamics of SSM signatures. The basic idea behind this method is that the precise description of variations in surface radiant temperature and vegetation growth conditions is highly associated with surface evapotranspiration and surface turbulent energy fluxes, which are sensitively dependent on SSM content (Carlson, 2007). By imposing different modifications, a number of studies on SSM downscaling have been carried out based on the UTF theory (Chauhan et al., 2003; Choi and Hur, 2012; Piles et al., 2014; Song et al., 2014; Zhao and Li, 2013). Apart from the UTF-based methods, another important SSM downscaling approach is proposed as “the Disaggregation based on Physical And Theoretical scale Change (DISPATCH)” by Dr. Merlin’s group and developed through a series of studies (Merlin et al., 2010, 2005; Merlin et al., 2013, 2015; Merlin et al., 2012, 2008; Molero et al., 2016). This method is based on a semi-physical model which describes the SSM variation as a function of soil evaporative efficiency. Other similar evaporation-based methods include that proposed by Kim and Hogue (2012), which employed a “soil wetness index” to associate AMSR-E SSM with MODIS optical/thermal-infrared datasets, and that conducted by Peng et al. (2016), which implemented the SSM downscaling by replacing the “soil wetness index” in Kim and Hogue (2012) with another index called “Vegetation Temperature Condition Index (VTCI)”. In recent years, approaches based on neural-network or machine learning have also been introduced for passive microwave SSM downscaling with optical/thermal-infrared datasets (Im et al., 2016; Jiang et al., 2017). And a review for all these methods has been published by Peng et al. (2017). 147

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ascribed to difficulties in the availability of full cover optical data). In this paper therefore, GWR has been integrated with the traditional UTFbased method in order to produce improved spatial representations of SSM (at the MODIS pixel scale) in this proposed study area characterized by frequent cloudy weather conditions and heterogeneous land cover. Downscaled SSM using GWR and traditional UTF-based downscaling methods were respectively evaluated using ground-based soil moisture observations to determine how differently the two methods can perform. Moreover, SSM values downscaled based on interpolated LST values were particularly evaluated against those downscaled based on MODIS observed LST values to determine how compatible the LST interpolation approach is to this GWR-integrated SSM downscaling method.

There are three major algorithms that have been applied for AMSR-2 0.25︒-resolution SSM retrievals. One is developed by JAXA (Fujii et al., 2009), a second is developed at the University of Montana (UMT) (Du et al., 2017) and another third named “Land Parameter Retrieval Model (LPRM)”, is developed by the “Vrije University Amsterdam in collaboration with the National Aeronautics and Space Administration (VUA-NASA)” (Owe et al., 2008). Among these algorithms, the LPRM algorithm has been applied to both AMSR-E and AMSR-2 SSM retrievals with intercalibration conducted between the two sensors (Parinussa et al., 2015), and a number of previous studies across different regions of the world have suggested that the LPRM-based SSM products perform better than AMSR-E (AMSR-2) SSM products on most other algorithms (Brocca et al., 2011; Chen et al., 2013; De Jeu et al., 2008; Draper et al., 2009; Rudiger et al., 2009). This study has therefore applied the LPRM-AMSR-2 SSM products for the retrieval of downscaled SSM at high spatial resolution. As indicated in Parinussa et al. (2015), little RFI contamination was diagnosed in this study area, so we have only used for downscaling, the SSM datasets acquired at C-band. Global AMSR-2 SSM products by the LPRM algorithm are freely distributed and are available at NASA’s Earth Observing System Data and Information System (EOSDIS). The period considered in this study is from September 1, 2012 to August 31, 2013.

2. Methods and materials 2.1. Study area The study area covers the middle and lower reaches of the Yangtze and Huaihe rivers in China (Fig. 1) and includes Jiangsu, Hubei, and Anhui provinces. Located between longitudes 108。E and 120。E and latitudes 29。N and 35。N, this region covers an area of about 430,000 km2. With an average annual precipitation ranging from 750 to 1800 mm, the area under study is predominantly of subtropical monsoon weather conditions. For most parts of the region (especially south of Huaihe River), lowest surface temperatures are usually around zero degrees Celsius even at the coldest (winter season) time and snow or frozen conditions are very seldom, meaning that SSM retrievals are practical for almost all year round. To present the spatial variation of land cover, MODIS MCD 12Q1 data in 2013 at 500 m spatial resolution is also shown in Fig. 1. The demonstrated land cover types are summarized according to vegetation canopy density level as prescribed by the IGBP classification scheme of 17 classes. The correspondence of these two classification schemes is shown in Table 1. In the middle and western parts of Hubei Province, the southernmost of Anhui Province and at the borders of these two provinces, is a mix of forests and savannas with a mountainous topography between 1000 and 3000 m. Otherwise, the dominant land cover on the plains (including gentle rolling hills below 500 m) of the study area is cropland, interspersed with a large number of lakes and streams around the main sections of the Huaihe and Yangtze rivers.

2.2.2. MODIS 1 km-resolution datasets MODIS has two sensors that operate on the Terra and Aqua spacecrafts, respectively. In this paper, the version 6 MODIS/Aqua 1-km resolution daily LST (MYD11A1, both daytime and nighttime data) product was employed since the Aqua spacecraft has similar orbit operation parameters with GCOM-W1, where AMSR-2 is aboard to ensure that Aqua MODIS LST datasets have close acquisition time with corresponding AMSR-2 SSM observations. An 8-day NDVI time series product of 1-km resolution was obtained using the 8-day phasing in the production of the 16-day composite NDVI datasets between Aqua/ MYD13A2 and Terra/MOD13A2 products. All MODIS data require to be converted from sinusoidal to geographically projected coordinates and mosaicking of the four tiles (h27v05, h28v05, h27v06, h28v06) covering our study area before applying the subsequent SSM downscaling procedures. MODIS data are freely accessible at NASA’s Earth Observing System Data and Information System (LPDAAC). 2.2.3. DEM The 30 m (1 arc-second) digital elevation model (DEM) generated by “NASA’s Shuttle Radar Topography Mission (SRTM)” within our study area are also used as an input dataset for MODIS LST interpolation. The dataset is preliminarily resampled to MODIS 1 km grids using the value averaging method for spatial integration with the MYD 11A2 dataset.

2.2. Datasets 2.2.1. AMSR-2 SSM product AMSR-2 is a passive microwave sensor onboard the Global Change Observation Mission1-Water (GCOM-W1) satellite that was launched by the Japan Aerospace Exploration Agency (JAXA) in May 2012. AMSR-2 is the successor of AMSR-E (May 2002–October 2011) which was widely used for the retrieval of SSM and had provided a number of consistent and continuous datasets for all kinds of land surface parameters for almost a decade. Sharing a similar design (e.g. orbit operation parameters, swath width etc.) with its predecessor, AMSR-2 is intended to extend the valuable legacy of AMSR-E and is expected to provide improved spatial resolution due to its added 7.3 GHz channel for Radio Frequency Interference (RFI) mitigation and larger reflectors.

2.2.4. Ground-based soil moisture observations In this study, the downscaled SSM datasets are evaluated using ground-based near-surface volumetric soil moisture datasets acquired from 65 national level soil moisture automatic observation stations (Fig. 1). All stations are part of the “Operation Monitoring System of Automatic Soil Moisture Observation Network in China” and thus use uniform observation standards and devices (Wu et al., 2014). Between remotely-sensed and ground-based (in situ) datasets, there indeed exists

Table 1 Correspondence description between the land cover classification scheme used in Fig. 1 and the IGBP scheme. Land cover type in Fig. 1

Corresponding land cover type included in the IGBP scheme.

1. 2. 3. 4. 5.

Barren or Sparsely Vegetated; Grass lands; Urban and built-up Croplands Evergreen Needleleaf Forest; Evergreen Broadleaf Forest; Deciduous Needleleaf Forest; Deciduous Broadleaf Forest; Mixed Forest; Shrublands Water; Permanent Wetlands Savannas; Woody Savannas

Sparsely vegetated lands Croplands Highly vegetated lands(forest) Water Intermediately vegetated lands

148

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unavoidable systematic bias or uncertainty, primarily due to heterogeneities in spatial and depth resolutions between footprint-scale and point-scale measurements. That means high accuracies in one-to-one validation between absolute values from both datasets are difficult to be achieved. However, the temporal dynamic patterns between these different datasets are mostly similar (Reichle et al., 2004) and thus their association can be exploited for evaluation of SSM data derived by remote sensing techniques. The means of three conventional performance metrics, i.e. correlation coefficient (R), mean bias (MB), and root mean square error (RMSE) of time series data at each site (station), were adopted to assess the relationship between in situ and remotely-sensed soil moisture data. RMSE is extensively used as a measure of accuracy for satellite geophysical retrievals though it is severely compromised by the bias in the mean and amplitude of time series data. Since MB has been employed as well, we used a non-biased RMSE (ubRMSE) instead of the original RMSE, as implemented by Molero et al. (2016). In this regard, the three conventional metrics can capture different attributes of the downscaling results. In effect, the systematic uncertainties between in situ and remotelysensed soil moisture data are inherently unavoidable due to their different spatial scales (Draper et al., 2009), which may deteriorate the performance of the aforementioned conventional metrics. Hence, more reasonable evaluation strategies on the downscaling method should focus on the relative gains of these statistical metrics from evaluation results of the corresponding low resolution satellite (AMSR-2) data, as introduced by Merlin et al. (2015). Through such gains, the improvement on spatial representations of the downscaled SSM from the original AMSR-2 data can be better evaluated. The gains are in the range -1˜1, where positive values indicate better correspondence of the downscaled time series data with in situ reference than AMSR-2 data. The gains for the ubRMSE and MB (GX, X= ubRMSE, MB) metrics were calculated using (1) while (2) is used for calculation of gain for the R (GR).

GX = (|XLR | GR = (RHR

|XHR |)/(|XHR | + |RLR|), X = ubRMSE , MB RLR )/(2

RHR

RLR )

Fig. 2. Flowchart of proposed methodological framework for improved SSM retrieval and evaluation.

2.3. Description of the MODIS LST interpolation and validation The degree of pixel loss of the MODIS images used in this study was first investigated (as shown in Fig. 3) through analysis of the “fraction of valid pixels (FVP, unit: %)”, calculated using valid pixel number against the total number of pixels within the study region. Obviously, daily LST images suffer from much more severe pixel loss throughout the study year than the composite NDVI datasets. More than 150 days have an FVP smaller than 20%, observed from the left panel in Fig. 3. Therefore a special interpolation procedure is required to be applied on MODIS day-time/night-time LST datasets. Traditional geostatistical interpolation methods such as Kriging or “Inversed distance weight (IDW)” methods have been widely used. However, they are not optimal for use in our study area because such methods are developed to predict the missing values at a given point through weighted average estimates from neighboring points, meaning it is difficult for them to work effectively in areas with large areas of missing pixels. Yu et al. (2015) developed a different method from geostatistical interpolation and the method has been shown to perform much better than the Kriging method on the Qinghai-Tibet Plateau, within which there is a larger fraction of areas of MODIS LST pixel loss. In this regard, we have proposed a new version of this method to fulfill LST interpolation. This method was developed based on the theory that the controlling factors of LST may change over time and season, and temporally neighboring images may share high similarity in terms of the controlling factors. In the original version of this method (Yu et al., 2015), for an image (LSTinter) to be interpolated at time t1, it can be expressed as a function of another reference image (LSTref) with quasi-full cover at time t0. The function is established using a simple linear formula (LSTinter=a×LSTref+b, a and b are regression coefficients) named as “transfer function” on the premise that t1 is temporally close to t0 so as to share a similar temperature change pattern (similar controlling environmental factors). Then the function requires to be applied on different categories of pixels for the whole study region. The categories were classified according to their similarity with regards to a series of controlling factors (DEM, slope, aspect, vegetation conditions, thermal factors etc.). In this developed method, we have integrated some of the most important factors that can be derived with remote sensing techniques, i.e. DEM and NDVI, into a “global” function without applying

(1) (2)

where the subscripts “HR” and “LR” denote high resolution (downscaled) and low resolution (AMSR-2) data, respectively. The MODIS 1-km resolution pixels where the aforementioned 65 stations are located are all homogenous considering the land cover types (using the land cover classification scheme in Fig. 1 and Table 1) of their 500-m resolution subpixels. Specifically, 39 stations are located in croplands, 18 stations in sparsely vegetated land surface and 8 stations are found in pixels of forest cover. For each station, near-surface volumetric soil moisture measurements (unit: cm3/cm3) taken at the top layer of the soil profile (0–10 cm) are correlated with spatially corresponding downscaled SSM values. As the ground-based soil moisture datasets acquired by the stations have an hourly temporal interval, their observations at 1:00 AM/PM and 2:00 AM/PM are averaged in order to integrate with the AMSR-2 descending/ascending overpass times (around 1:30 AM/PM for descending/ascending overpasses, respectively). The 3 stations labeled M1 (119.23︒E, 34.30︒N), M2 (112.88︒E, 30.42︒N) and M3 (118.15︒E, 30.06︒N) and highlighted in yellow in Fig. 1, being representative of the major land cover types of the area under investigation, are particularly used to evaluate the results in view of differing geographical and ecological scenarios. Among the three stations, M1 is representative of homogenous cropland while M2 is dominated with sparsely vegetated areas. And M3 is located in a mountainous area covered by forest. With all abovementioned datasets, our proposed methodological framework can be conducted. The work flowchart is schematically shown in Fig. 2 and will be described in greater detail in the following sections. 149

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Fig. 3. Information on “fraction of valid pixels (FVP)” for MODIS day-time/night-time LST (top/middle rows) and 8-day composite NDVI (bottom row) datasets. The histograms of quantitative distributions of FVP are shown in the left panel and their variation patterns within the study period are shown in the corresponding rows of the right panel.

denotes a normalized treatment to 0-1. This means P* is a function of original P values as well as the maximum (Pmax) and minimum (Pmin) values of P dataset, i.e.

this “controlling factor category classification”. Detailed information on the original method can be found in Yu et al. (2015) while in the current paper, only a simple description of the major steps of LST interpolation has been made along the following:

P* =

1) Screen out invalid pixels including cloud-covered pixels as well as pixels with large retrieval errors (> 2 K) from the “QC (Quality control)” field for all images. 2) The reference images are determined given that they have an FVP > = 90%. These images are first interpolated for their small fractions of missing pixel values using geostatistical interpolation (IDW) methods. Day/night time difference and seasonality variation for controlling factors are considered in the determination of reference images. This means that day-time and night-time images are treated respectively, and each month during study region should be ensured to have at least one reference image. To satisfy this condition, the reference images were selected over a longer period of time (from January of 2010 to December of 2016). The number of reference images for each month within this period is listed in Table 2. 3) Similar to the transfer function described in the last paragraph, the function developed in this study superimposes the controlling factors of DEM and NDVI images at the interpolated time t1 (NDVIt1) and is expressed as:

* er LSTint

t1

* = a × LSTref

t0

+ b × NDVIt1* + c × DEM * + d

P P min Pmax Pmin

(4)

Eq. (3) is constrained by |t0-t1| < = 30. If there are no reference images available for our study period, the reference images are searched from the closest neighboring years (under this condition, t1 and t0 just denote “day of year” regardless of their inter-annual difference) iteratively until the reference image set established in last step is exhausted. If there are more than 1 candidate of high-quality reference images, the image with minimum |t0-t1| value is selected. Eq. (3) is then regressed to obtain coefficients a, b, c, and d, and null pixels for the image of LSTinter-t1 can be estimated using (3). The interpolated LST data require particular validation before their use in SSM downscaling. Remotely-sensed LST is strongly related to in situ near surface air temperature measurements (Holmes et al., 2009; Sohrabinia et al., 2015) as opposed to in situ ground surface temperatures. This is because air temperature is more homogenous in space and thus has a closer spatial relationship with remotely-sensed LST. Previous studies suggest that daily maximum and minimum air temperatures can exhibit strong relationships with MODIS day-time (nighttime) LST data (Benali et al., 2012), or LST data observed by other satellites at similar local time (Holmes et al., 2009; Jones et al., 2010). In this study therefore, we first established a series of models for estimating MODIS-scale LST under cloudy sky using daily maximum and

(3)

In this equation, for any land surface parameter P, the symbol “P*”

Table 2 Number of day-time and night-time MODIS daily LST images with FVP > = 90% for each month during 2010–2016 for the study region. Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Day-time

Night-time

2010

2011

2012

2013

2014

2015

2016

Overall

2010

2011

2012

2013

2014

2015

2016

Overall

0 1 3 1 1 0 0 0 1 0 1 1

0 1 1 3 0 1 0 0 0 1 0 0

0 0 1 3 0 0 0 0 0 1 0 0

1 0 1 4 3 0 0 1 0 2 2 0

1 0 0 0 1 0 0 0 0 4 0 2

0 0 0 1 0 0 1 0 1 1 0 1

0 2 0 1 2 1 1 2 0 0 1 0

2 4 6 13 6 2 2 3 2 9 4 4

1 2 3 1 2 0 0 0 0 4 2 2

0 2 0 5 1 0 0 0 0 0 0 0

0 0 2 0 0 0 0 0 1 2 3 0

0 2 3 4 2 0 0 1 4 8 5 1

0 0 4 1 0 0 0 0 0 8 2 0

0 1 0 0 0 0 0 0 0 5 0 1

0 1 2 0 0 2 4 1 1 1 1 0

1 8 12 11 5 2 4 2 6 28 13 4

150

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minimum near surface air temperatures observed at each of the 54 national level meteorological stations within the study region (Fig. 1) as inputs. Such air temperature data are available at the National Meteorological Information Center of China (http://data.cma.cn). Considering the different factors influencing daily maximum and minimum temperatures in different seasons (e.g. local time, solar elevation angle), models were independently established for each month, exploiting the linear relationship between daily maximum and minimum air temperatures and their corresponding MODIS LST day-time (night-time) records under clear sky. Then the LST under cloudy sky was estimated at the meteorological stations based on these models, and datasets obtained from them were utilized as reference for validating the corresponding interpolated MODIS LST.

are described as follows: 1) Eq. (5) is combined with (6) at a spatial resolution of 0.25。, to obtain the following equation used for establishing a spatially nonstationary relationship between AMSR-2 SSM and resampled MODIS data:

SSMi*0.25deg =

+ 0.25deg (u i , 3

SSMi*1km =

SSM * = a × NDVI 2 + b × NDVI * × LST * + c × LST 2 + d × NDVI * * * + e × LST * + f (5)

1km (u i , 3

k (u i ,

vi ) x ik +

i

(

dij b

) 2]2 , dij < b ; wij = 0, dij > = b

vi ) ×

0.25deg (u i , 4

(LSTi*0.25deg )

× NDVIi*0.25deg )+

vi ) × (NDVIi*0.25deg ) +

0.25deg (u i , i

vi )

(9)

1km (u i , 0

vi ) +

1km (ui , 1

vi ) × (NDVIi*1km)2 +

1km (u i , 2

vi )

(LSTi*1km)

+

1km (u i , 4

1km (ui , i

vi ) × (NDVIi*1km) +

1km (u i , 5

vi)

vi )

3) As local evapotranspiration patterns define the shape of LST-VI triangle feature and have strong seasonal characteristics, it is not reasonable to establish only one regression formula using datasets for one year. Instead, the whole period was divided into several subperiods with each one favored with a corresponding regression Formula (9), in order to remove the influence of triangle feature seasonality. Each sub-period lasts for 2 days as this is substantially the revisit time interval of the AMSR-2 sensor to obtain full cover SSM observations within our study region. 4) In many previous studies as mentioned above, MODIS albedo (MCD43) is also considered an important controlling factor in addition to LST and VIs, on SSM variation. In this study, MODIS albedo (MCD43) is not taken into account due to the larger fraction of pixel loss even with a 16-day composite. Investigating the relative performances of GWR-integrated and traditional UTF-based methods, being one of the main objectives of this study, evaluations are conducted on SSM products downscaled using the aforementioned methods in Section 3.

(6)

(7)

3. Results and discussion

Where (μi, νi) is the unbiased estimate of the regression coefficient β, W(μi, νi) is the weighting matrix used to ensure that observations near the specific point have larger weighted values, and X and Y are the matrices for independent and dependent variables, respectively. Each element wij of the weighting matrix W(μi, νi), representing the weight of observation j for location i, is calculated using an adaptive bi-square function:

wij = [1

0.25deg (ui , 5

× (NDVIi*0.25deg ) 2

(10)

Where ui and vi represent 2-dimension geographical coordinates of location i; yi and xik are the dependent variable and the kth independent variable at location i, respectively. Correspondingly, β0(ui,vi) and βk(ui,vi) are the constant and coefficient for the kth independent variable while εi denotes the residual component of the regression at location i. The parameters β0 and βk can be estimated using the following equation:

ˆ (ui , vi ) = (X T W T (ui , vi ) X ) 1X T W (u, v ) Y

0.25deg (ui , vi ) 2 0.25deg 2 * (LSTi ) +

vi ) × (LSTi*1km) 2 + ×

m k=1

0.25deg (ui , vi ) 1 × (LSTi*0.25deg

× (LSTi*1km × NDVIi*1km)+

This equation was first regressed backward on data of coarse-resolution passive microwave grids using spatially resampled NDVI and LST datasets, to obtain regression coefficients in (5). Then it is used to estimate high-resolution (downscaled) SSM values. Generally, the regression coefficients are spatially fixed for a certain study region. However in this study, the GWR method is integrated with (5) to obtain a new SSM downscaling method from that based on UTF. GWR has been proposed for investigating the non-stationary relationship between dependent variables and independent variables using geographically varying regression coefficients. A typical GWR model can be expressed as follows:

vi ) +

vi ) +

2) A weighted least square estimation is employed to obtain regression coefficients ([βk0.25deg(ui,vi),k=0,1,2,3,4,5]) and residual 0.25deg (ui,vi) at 0.25︒resolution. These coefficients and residuals εi are then interpolated into MODIS 1-km resolution [βk1km(ui,vi), k= 0, 1, 2, 3, 4, 5 ;εi1km(ui,vi)] using cubic spline interpolation, and downscaled SSM at 1 km-resolution (SSM1km) are estimated using 1 km-resolution MODIS datasets in conjunction with these interpolated coefficients, i.e.

The traditional UTF-based downscaling approach employs the LSTVI spatial variability to estimate soil moisture signatures. For a typical formula, SSM was expressed (using SSM* with the same treatment in (4)) as a second-order polynomial regression (with a to f as regression coefficients) using NDVI and LST,

0 (ui ,

vi ) × +

2.4. GWR method integrated with UTF downscaling approach for AMSR-2 SSM

yi =

0.25deg (ui , 0

3.1. Results of MODIS LST interpolation Table 3 reports the validation results of the linear models between near-surface air temperatures and interpolated LST data at the meteorological stations. For both day-time and night-time data, the models are well fitted across months, with an adjusted R2 in the range 0.42 (mostly≥0.6) and 0.93, and RMSE ≤ 2.2 K. This revealed the feasibility to utilize MODIS-scale LST estimated from meteorological data as a reference dataset for validating the interpolated LST. Results of the validations are also reported in Table 3 while the corresponding scatter points of the interpolated LST versus meteorologically derived LST are shown in Fig. 4. Interpolated LSTs have RMSEs in the range 1.5 K˜3.5 K, which are generally larger than the RMSEs generated by the fitted models (in the range 1.1 K˜2.2 K). On the other hand, such RMSE differences caused by LST interpolation uncertainty (i.e. RMSEinterpolation -

(8)

Where dij represents the Euclidean distance between location i and its neighboring observation point j,b denotes the adaptive kernel bandwidth, which is estimated using a cross validation method, details of which and the GWR theory can be found in (Brunsdon et al., 1996). In this study, the SSM downscaling process based on the GWR method is conducted using a series of procedures similar to those for LST downscaling that have been introduced in (Duan and Li, 2016), and 151

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Table 3 Validation statistics of the linear regression models constructed for estimating day-time/night-time LST using near surface air temperature data as input, as well as the validation results of the interpolated LST using the model outputs as reference data. Tair_max indicates the daily maximum value of near surface air temperature and Tlst indicates the Aqua MODIS day-time LST. N indicates the sampling number. Month

Model equation

Evaluation of model fitness (unit: K) 2

Validation of interpolated LST using the model (unit: K)

Adjusted R

RMSE

Number

RMSE

MB

Number

Day time

January February March April May June July August September October November December

Tlst=1.00Tair_max+0.09 Tlst=1.05 Tair_max -13.76 Tlst=0.75 Tair_max +73.68 Tlst=0.91 Tair_max +28.33 Tlst=0.95 Tair_max +17.03 Tlst=0.80 Tair_max +61.32 Tlst=0.89 Tair_max +32.63 Tlst=0.79 Tair_max +63.74 Tlst=0.93 Tair_max +19.44 Tlst=0.90 Tair_max +31.44 Tlst=0.84 Tair_max +46.30 Tlst=0.85 Tair_max +42.84

0.9 0.91 0.71 0.69 0.68 0.49 0.48 0.42 0.49 0.68 0.85 0.93

1.6 1.9 1.8 2.1 2.1 2.2 2.2 1.9 1.9 1.6 1.3 1.1

192 543 589 365 695 315 224 389 352 359 428 523

2.8 2.8 2.5 2.7 2.1 3.3 2 1.5 2.5 2.3 2.4 2.8

1.4 1.9 1.4 1.9 1.1 2.0 0.2 0.4 1.7 1.6 0.9 1.7

1181 935 895 1022 825 1110 1226 1125 1111 1053 1016 796

Night time

January February March April May June July August September October November December

Tlst=0.93Tair_min+17.82 Tlst=0.85 Tair_min +39.36 Tlst=0.80 Tair_min +55.65 Tlst=0.81 Tair_min +53.95 Tlst=0.78 Tair_min +62.73 Tlst=0.86 Tair_min +39.42 Tlst=0.76 Tair_min +71.74 Tlst=0.74 Tair_min +75.84 Tlst=0.96 Tair_min +10.94 Tlst=0.79 Tair_min +58.85 Tlst=0.83Tari_min+47.35 Tlst=0.70Tari_min+80.59

0.67 0.75 0.74 0.67 0.7 0.64 0.71 0.65 0.73 0.49 0.81 0.69

2.2 1.8 1.8 1.9 1.8 1.7 1.1 1.1 1.5 1.7 1.5 1.5

226 471 669 452 521 289 334 547 340 331 477 559

3.3 3.5 2.9 2.2 2.2 2.4 2.3 2 2.2 2 2.8 2.3

−2.4 −1.7 −1.6 −0.8 −0.9 −1.3 −1.4 −1.4 −0.9 −1 −1.2 −0.8

1154 1048 721 947 814 1189 1119 942 1080 1088 937 577

RMSEmodel_fitness) can be controlled below 1.7 K. For the day-time and night-time data, interpolated LST data deviate from reference data with MB (mean bias) in the range 0.2 K–1.9 K and -0.8 K ˜ -2.4 K, respectively for different months. Such ranges of MB and RMSE are expected since our interpolation method predicts the theoretical LST under clear sky conditions which is slightly different from that under cloudy conditions. Two images, one on the daytime of 22 July 2013 and the other on the nighttime of 22 December 2012 are selected as representatives of the LST interpolation results for warm and cold conditions, respectively as shown in Fig. 5. Generally smooth spatial transitions and good consistencies have been achieved in texture between MODIS and interpolated LST pixels for both conditions after FVP of the images have been drastically improved from smaller than 30% to larger than 95%. In practice, most images are not favored with a 100% FVP because a small fraction of pixels in each image could have invalid interpolation results (LST values larger or smaller than their normal thresholds, which were set between 260 K˜320 K in this study). However, an LST interpolation result with an FVP larger than 90% is considered in this study to be effective in implementing GWR-based SSM downscaling and obtaining quasi-full cover and high spatial resolution SSM datasets. It should also be realized that there are other factors which may affect the interpolated LST apart from vegetation cover and elevation. For example, SSM could have important feedbacks on LST through surface evapotranspiration under (semi-) arid conditions and should have been considered in Eq. (3) under those conditions. However, Zhang and Dong (2010) found that such SSM feedbacks are relatively weak over wet areas (such as our study area). Therefore, it is feasible to apply the interpolated LST dataset in subsequent SSM downscaling in the current study while other factors such as SSM should be given due consideration in environments different from that studied herein.

seasons (two are in descending mode and two are in ascending mode). From visual interpretation, the GWR downscaling results seem superior to those of UTF in terms of their texture consistencies with AMSR-2 SSM images of coarser spatial resolution (especially in the regions marked out using red ellipse in Fig. 6) in all 4 seasons. Compared with GWR, the UTF-based estimates seem not so accurate in reflecting the spatial variation of SSM in a vast area with significant topography and land cover heterogeneity. They are liable to either underestimate or overestimate SSM in specific terrain (as in the 1 st and 2nd rows of Fig. 6) or become less effective in representing the SSM discrepancies in different sub-regions (as in the 3rd and 4rd rows of Fig. 6). A comparison of downscaling performances between GWR- and UTF-based approaches is conducted by validating their downscaled SSM datasets with reference to ground-based observations from the 65 soil moisture stations. The validation is made using data pairs (remote sensing and ground-based data) at each soil moisture station. Statistics of the average validation results, including both the conventional metrics and their gains from the AMSR-2 statistical results, are reported in Table 4. Generally, the GWR approach outperforms the UTF from all aspects. The ubRMSEs of GWR-based datasets are smaller than those of UTF-based datasets, with a difference of about −0.023 cm3/cm3 for the ascending mode and -0.027 cm3/cm3 for the descending mode. Correspondingly, the correlations of UTF-based downscaling results are much weaker (R smaller than 0.4) than those derived from GWR-based results (R up to 0.55). More superior performances of the GWR-based approach can be seen in the gain metrics (GubRMSE, GMB and GR) which are in the range 0.09˜0.20, as opposed to those of UTF which are either equal to or smaller than zero. The superiority of the GWR-based downscaling approach is also revealed in Fig. 7, which illustrates the distribution of the metric differences between GWR and UTF over different moisture stations. Specifically, the GWR-based results own larger GR in 63 out of the 65 stations in both ascending and descending modes. Moreover, 56 (ascending) stations and 58 (descending) stations showed larger GMB with the GWR-based downscaling results. Not all stations showed a better performance of the GWR-based method but this is still reasonable considering the inherent difference in soil moisture representation

3.2. Evaluation of SSM downscaling performances with different methods: GWR and UTF Fig. 6 presents the SSM downscaling results obtained from the UTF and GWR approaches based on 4 AMSR-2 image dates, representing 4 152

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Fig. 4. Scatter points for validation of day-time/night-time interpolated LST values for each month during the study period. The reference LST were retrieved by the linear regression model based on in situ near surface air temperature. The solid line in the figure indicates the 1:1 line.

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Fig. 5. Results of interpolations (bottom panel) against the original MODIS LST images (top panel) for the day-time data (left panel) on July 22th (in summer), 2013 and the night-time data (right panel) on December 22th (in winter), 2012. The grey pixels indicate invalid and eliminated interpolation results.

between ground-based and remotely-sensed data sources.

presented in Fig. 8(a) and (c) while evaluation results are reported in Table 5 for station-averages in a way similar to Table 4. Almost equal MB performances were achieved by using the interpolated LST data as opposed to the MODIS observed data. Differences in station-averages of the correlation coefficients do exist but are not obvious for both ascending and descending modes, with R-values of MODIS data being only slightly better (0.58 > 0.54 for ascending mode and 0.57 > 0.53 for descending mode). The differences in their ubRMSEs are subtle as well, only about 0.008–0.010 cm3/cm3 and 0.01–0.02 in terms of the corresponding GubRMSE. It should be noted that most of the stations have

3.3. Evaluation of the performance of downscaled SSM data based on interpolated LST data inputs As the GWR-based downscaled SSM dataset has been proven advantageous over the UTF-based dataset, all subsequent analyses are based on the former. In this regard, the SSM downscaled using MODIS LST inputs as well as interpolated LST inputs are compared to see their differences in performance. The scatter points for all stations are

Fig. 6. SSM downscaling results of GWR-(middle column) and UTF-(right column) based approaches as well as the corresponding AMSR-2 SSM images (left column) for four dates representing each of the four seasons: 1 st row, April 24, 2013, ascending mode; 2nd row, July 24, 2013, ascending mode; 3rd row, September 15, 2012, descending mode; 4th row, December 15, 2012, descending mode. 154

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Table 4 Validation results of GWR- and UTF-based downscaled SSM datasets against ground-based and AMSR-2 soil moisture datasets. All the metrics reported the average of the 65 stations. The subscripts “hr” and “lr” denote high resolution (downscaled) and low resolution (AMSR-2) data, respectively. ubRMSEhr

ubRMSElr

Rhr

Rlr

MBhr

MBlr

GubRMSE

GR

GMB

Ascending

GWR UTF

0.074 0.097

0.087 0.087

0.55 0.37

0.40 0.40

−0.061 −0.080

−0.089 −0.089

0.09 −0.01

0.14 −0.02

0.20 0.003

Descending

GWR UTF

0.074 0.101

0.091 0.091

0.54 0.28

0.34 0.34

−0.022 −0.027

−0.031 −0.031

0.11 −0.01

0.18 −0.04

0.13 −0.02

about 90–100 SSM samples downscaled by MODIS LSTs while more than 200 samples are downscaled by interpolated LSTs. Therefore, considering the much larger sample size of SSM estimates downscaled by interpolated LSTs, it is even more difficult to judge whether the larger RMSEs of SSM downscaled by interpolated LSTs are caused by larger sample size or just error bias from LST interpolation. In Fig. 8(b)–(d) and (f)–(h), the histogram distributions of the differences in ubRMSE, GR, and GMB between SSM downscaled by interpolated LSTs and MODIS LSTs are demonstrated for different stations. It is apparent that in most stations, SSM estimates downscaled by interpolated LSTs obtain larger ubRMSEs but only a small portion of stations show their ubRMSE differences (absolute values) larger than 0.02 cm3/ cm3. Also, differences in GR and GMB are maintained to a relatively low level, between −0.04˜0.04. Based on the above, it can be said that with interpolated LST, rather than satellite observed (MODIS) LST, the downscaling method may produce additional retrieval errors, which is to large degree generated by the inevitable error bias from LST interpolation (as stated in Section 3.1). However, the errors are not consequential and in most cases smaller than 0.02 cm3/cm3 in terms of the ubRMSE difference with reference to ground-based soil moisture measurements. And the methodological framework we have proposed in this study is regarded effective in producing a relatively reliable set of high spatio-temporal and quasi-full cover SSM images in cloudy areas.

time, the temporal patterns captured by ground-based measurements. Discernible bias exists in winter for the AMSR-2 time series, where there may be increased uncertainties in SSM retrievals due to presumably existing frozen or snow-covered soils considering that the M1 station lies at the northernmost of in our study area. Significant biases in other seasons, for example those that can be identified in July, are probably caused by larger retrieval errors of LPRM-AMSR-2 SSM data under denser vegetation cover, which is in line with the conclusion of Parinussa et al. (2011). For such conditions with significant AMSR-2 SSM retrieval error bias, the downscaled SSM time series has been found to mitigate the bias more or less, suggesting improved SSM representation from the AMSR-2 data. In M2 with sparsely vegetated land surface and relatively low NDVI of slight seasonal variation, the correlation of AMSR-2 SSM time series with respect to ground-based data is reduced with an even larger bias than M1 (cropland). This is because in our study period, MODIS pixels of sparsely vegetated land surface are usually distributed within AMSR2 pixels of much higher spatial heterogeneity (than the situation for cropland). And this of course has increased the uncertainty in evaluating AMSR-2 SSM using ground-based measurements. However, this bias has been significantly compensated for by the downscaled SSM time series for most of the annual cycle especially in the spring season when AMSR-2 data are maintained at an abnormally insensitive level. In M3 where the land surface is covered by dense vegetation all year round, both AMSR-2 and downscaled SSM time series produce poor consistencies with reference to ground-based data. The bias seems comparatively smaller in winter with minimum vegetation cover density, while it is far more conspicuous in warm seasons (spring and summer) when the NDVI series attains peak value. The primary reason for this poorer performance could still be attributed to the inherent inaccuracy of microwave-retrieved SSM data under dense vegetation, similar to the case of M1 in July. In addition, as our proposed downscaling approach is still based on the “LST/VI” triangle feature theory, this poorer performance under high vegetation covers has to some degree confirmed the findings of Sandholt et al. (2002), which showed that the uncertainty in the estimation of soil moisture using triangle feature theory (i.e. Temperature –Vegetation Dryness Index, TVDI) has

3.4. Evaluation of seasonal variations of GWR-downscaled SSM Since the GWR-based downscaling method has been proven generally reliable even with interpolated LST inputs, the performance of the downscaled SSM time series data generated by this method was analyzed further considering more environmental factors. In this regard, we investigated the seasonal variation of the downscaled SSM, and its spatially corresponding AMSR-2 SSM and MODIS composite NDVI data. This is illustrated together with the ground-based soil moisture measurements at three soil moisture stations (i.e. M1, M2, and M3 as described in Section 2.2) in Fig. 9. In M1 (cropland), the seasonal variation trend of time series of AMSR-2 and downscaled soil moisture data are highly consistent and they both follow well for most of the

Fig. 7. Distributions of the differences of GR (top panel) and GMB (bottom panel) between GWR and UTF downscaling approaches (GWR minus UTF) over the 65 soil moisture observation stations. 155

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Fig. 8. (a) Scatter points for SSM downscaled by interpolated LST inputs (green points) as well as MODIS LST inputs (red points) against ground-based soil moisture data for all stations in the ascending mode. (b)–(d) Distribution of differences on RMSE, GR, and GMB between SSM time series data downscaled by interpolated LST and those downscaled by MODIS LST in the ascending mode. (e)–(h) Similar demonstration results to (a)–(d) but for the descending mode.

a larger impact at higher values of NDVI. The seasonal analysis in Fig. 9 demonstrated the influence of land cover, especially vegetation density variation, on GWR-based SSM downscaling performance. In general, the downscaled SSM dataset produced by our proposed downscaling methodological framework has been proven effective in obtaining improved regional SSM representations from microwave-retrieved SSM datasets in croplands or sparsely vegetated bare lands. The method can perform poorer under condition of higher vegetation cover, primarily due to the constraints of AMSR-2 SSM accuracy. Since only 8 of the 65 stations are covered by forest, the station-averages of GR, GMB, and GubRMSE for the GWR-downscaled SSM were reasonably positive in Table 4 and Table 5. However, much attention should be paid to the design of improved SSM retrieval algorithms using AMSR-2 products over landscapes of vegetation extremes. Besides, future research works may also focus on soil moisture estimates from more advanced and specialized soil moisture monitoring sensors such as SMAP (Entekhabi et al., 2010), and on which can be applied, our proposed SSM downscaling methodological framework for further evaluation and improvement for use in similar or different geographical or ecological settings.

conditions, a special LST interpolation method is preliminarily applied to achieve daily LST datasets with quasi full covers and high reliability. The interpolated LST were validated against a reference LST dataset built from observed relationships between LST and ground-based nearsurface air temperatures on clear sky days, with absolute biases smaller than 2.5 K and RMSEs smaller than 3.5 K throughout the study period. Such optical data inputs of quasi-full covers and the use of GWR method allowed for an improvement of the traditional UTF downscaling regression equation for SSM, and the GWR-downscaled SSM data showed a better global consistency in texture with the AMSR-2 SSM data, relative to that observed with the traditional UTF-downscaled SSM data. Validations against ground-based soil moisture measurements also indicated higher accuracies and improved spatial representations with the GWR-downscaled SSM data. For the GWR-based downscaling results, the SSM estimates downscaled by MODIS LST inputs as well as interpolated LST inputs are evaluated respectively with reference to ground-based soil moisture measurements. Results showed that the SSM estimates downscaled by interpolated LST inputs performed only slightly poorer than those by MODIS data, with their ubRMSE differences being no larger than 0.02 cm3/cm3 in most cases. This confirms the relatively high feasibility of the LST interpolation method in achieving downscaled SSM maps of quasi-full covers. The time series of the downscaled SSM estimates is generally in phase with the variation in ground-based soil moisture measurements in cropland or land surface with sparse vegetation. Only extreme conditions such as dense levels of vegetation covers or low temperature probably coupled with frozen/snow-covered soils in winter season could deteriorate the performance of our proposed downscaling approach. However, this may also be partly ascribed to the poorer performance of AMSR-2 soil moisture observations (under extreme conditions). Therefore, better soil moisture representations at high spatial resolution in cloudy areas can be achieved if this

4. Conclusion This study proposed a methodological framework for obtaining downscaled soil moisture representations from AMSR-2 SSM products over cloudy areas. The experiment was conducted in an area in the middle and lower reaches of the Yangtze and Huaihe rivers in China, which is characterized by humid climate (with relatively weaker SSM feedbacks on LST) and frequent cloudy weather conditions. The SSM downscaling approach employs MODIS LST and NDVI datasets and is developed on the theory of “LST-VI universal triangle feature (UTF)”. As daily LST inputs suffer from serious pixel loss under cloudy weather

Table 5 Validation results of GWR-based SSM data downscaled by MODIS LST inputs as well as those downscaled by the interpolated LST inputs, respectively. ubRMSEhr

ubRMSElr

Rhr

Rlr

MBhr

MBlr

GubRMSE

GR

GMB

Ascending

MODIS LST Interpolated LST All LST

0.068 0.076 0.074

0.088 0.087 0.087

0.58 0.54 0.55

0.40 0.41 0.40

−0.060 −0.062 −0.061

−0.095 −0.086 −0.089

0.10 0.09 0.09

0.17 0.14 0.14

0.20 0.20 0.20

Descending

MODIS LST Interpolated LST All LST

0.067 0.077 0.074

0.091 0.091 0.091

0.57 0.53 0.54

0.34 0.34 0.34

−0.022 −0.022 −0.022

−0.034 −0.030 −0.031

0.12 0.10 0.11

0.20 0.16 0.18

0.14 0.12 0.13

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Fig. 9. Seasonal variations of soil moisture estimates at three different soil moisture stations (M1, M2, and M3, highlighted in Fig. 1) within the study region as representatives of three land cover types. Time series data include: GWR-based downscaled SSM estimates (black circles), AMSR-2 SSM product (red crosses), groundbased soil moisture observations (blue solid lines), and MODIS NDVI (MYD/MOD 13 A2) composite dataset (green dotted lines).

framework is applied to microwave soil moisture retrieval data with higher accuracy observations in the near future. Besides, since both the evaporation-based method (e.g. the DISPATCH, as mentioned in the Introduction section) and our GWR-based method have been conducted at the MODIS pixel scale, the performances of these two downscaling methods deserve a fair and particular inter-comparison in future studies, which would also help to better reveal the feasibility of the “soil evaporative efficiency” theory and the “triangle feature” theory in deriving SSM at the MODIS pixel scale.

-Eur. Space Agency 111, 113–121. Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner, W., et al., 2011. Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe. Remote Sens. Environ. 115 (12), 3390–3408. https://doi.org/10.1016/j.rse.2011.08.003. Brunsdon, C., Fotheringham, A.S., Charlton, M.E., 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr. Anal. 28 (4), 281–298. Carlson, T., 2007. An overview of the "triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors 7 (8), 1612–1629. https://doi.org/10.3390/s7081612. Carlson, T.N., Gillies, R.R., Perry, E.M., 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sens. Rev. 9 (1–2), 161–173. Carlson, T.N., Perry, E.M., Schmugge, T.J., 1990. Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agric. For. Meteorol. 52 (1–2), 45–69. https://doi.org/10.1016/0168-1923(90)90100-K. Chauhan, N.S., Miller, S., Ardanuy, P., 2003. Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach. Int. J. Remote Sens. 24 (22), 4599–4622. https://doi.org/10.1080/0143116031000156837. Chen, C., Zhao, S., Duan, Z., Qin, Z., 2015. An improved spatial downscaling procedure for TRMM 3B43 precipitation product using geographically weighted regression. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 8 (9), 4592–4604. https://doi. org/10.1109/JSTARS.2015.2441734. Chen, Y., Yang, K., Qin, J., Zhao, L., Tang, W., Han, M., 2013. Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J. Geophys. Res.-Atmos. 118 (10), 4466–4475. https:// doi.org/10.1002/jgrd.50301. Choi, M., Hur, Y., 2012. A microwave-optical/infrared disaggregation for improving spatial representation of soil moisture using AMSR-E and MODIS products. Remote Sens. Environ. 124, 259–269. https://doi.org/10.1016/j.rse.2012.05.009. De Jeu, R.A.M., Wagner, W., Holmes, T.R.H., Dolman, A.J., van de Giesen, N.C., Friesen, J., 2008. Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surv. Geophys. 29 (4–5), 399–420. https://doi.org/10. 1007/s10712-008-9044-0. Draper, C.S., Walker, J.P., Steinle, P.J., de Jeu, R.A.M., Holmes, T.R.H., 2009. An evaluation of AMSR-E derived soil moisture over Australia. Remote Sens. Environ. 113 (4), 703–710. https://doi.org/10.1016/j.rse.2008.11.011. Du, J.Y., Kimball, J.S., Jones, L.A., Kim, Y., Glassy, J., Watts, J.D., 2017. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave earth observations. Earth Syst. Sci. Data 9 (2), 791–808. https://doi.org/10.5194/essd-9-791-

Acknowledgment This work was supported by the “National Key R&D Program of China (Grant No. 2017YFD0300402-3)”, the Special Fund for Industrial Scientific Research in the Public Interest (Meteorology) of China (Grant No. GYHY201406028), and the National Natural Science Foundation of China (Grant No. < GN3 > 41471277 < GN3 >). The authors would like to thank the Jiangsu Meteorological Bureau, Anhui Meteorological Bureau, and the Wuhan Regional Climate Center of China, for providing ground-based soil moisture observation datasets. The authors would further like to thank the National Aeronautics and Space Administration (NASA) for providing AMSR-2, MODIS, and DEM datasets free of charge. The valuable comments and advice by anonymous peer reviewers are also well appreciated. References Benali, A., Carvalho, A.C., Nunes, J.P., Carvalhais, N., Santos, A., 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens. Environ. 124, 108–121. https://doi.org/10.1016/j.rse.2012.04.024. Berger, M., Camps, A., Font, J., Kerr, Y., Miller, J., Johannessen, J., et al., 2002. Measuring ocean salinity with ESA’s SMOS mission - advancing the science. ESA Bull.

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Agricultural and Forest Meteorology 275 (2019) 146–158

P. Song, et al.

downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency. Remote Sens. Environ. 112 (10), 3935–3946. https://doi.org/10.1016/j.se.2008.06. 012. Molero, B., Merlin, O., Malbéteau, Y., Al Bitar, A., Cabot, F., Stefan, V., et al., 2016. SMOS disaggregated soil moisture product at 1 km resolution: processor overview and firstvalidation results. Remote Sens. Environ. 180, 361–376. https://doi.org/10. 1016/j.rse.2016.02.045. Njoku, E.G., Jackson, T.J., Lakshmi, V., Chan, T.K., Nghiem, S.V., 2003. Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens. 41 (2), 215–229. https:// doi.org/10.1109/TGRS.2002.808243. Owe, M., de Jeu, R., Holmes, T., 2008. Multisensor historical climatology of satellitederived global land surface moisture. J. Geophys. Res.-Earth Surf. 113 (F1), 196–199. https://doi.org/10.1029/2007JF000769. Parinussa, R.M., Holmes, T.R.H., Wanders, N., Dorigo, W.A., de Jeu, R.A.M., 2015. A preliminary study toward consistent soil moisture from AMSR2. J. Hydrometeorol. 16 (2), 932–947. https://doi.org/10.1175/JHM-D-13-0200.1. Parinussa, R.M., Meesters, A.G.C.A., Liu, Y.Y., Dorigo, W., Wagner, W., de Jeu, R.A.M., 2011. Error estimates for near-real-time satellite soil moisture as derived from the land parameter retrieval model. IEEE Geosci. Remote Sens. Lett. 8 (4), 779–783. https://doi.org/10.1109/LGRS.2011.2114872. Peng, J., Loew, A., Merlin, O., Verhoest, N.E.C., 2017. A review of spatial downscaling of satellite remotely sensed soil moisture. Rev. Geophys. 55 (2), 341–366. https://doi. org/10.1002/2016RG000543. Peng, J., Loew, A., Zhang, S.Q., Wang, J., Niesel, J., 2016. Spatial downscaling of satellite soil moisture data using a vegetation temperature condition index. IEEE Trans. Geosci. Remote Sens. 54 (1), 558–566. https://doi.org/10.1109/TGRS.2015. 2462074. Piles, M., Sanchez, N., Vall-llossera, M., Camps, A., Martinez-Fernandez, J., Martinez, J., Gonzalez-Gambau, V., 2014. A downscaling approach for SMOS land observations: evaluation of high-resolution soil moisture maps over the Iberian Peninsula. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7 (9), 3845–3857. https://doi.org/10. 1109/JSTARS.2014.2325398. Reichle, R.H., Koster, R.D., Dong, J., Berg, A.A., 2004. Global soil moisture from satellite observations, land surface models, and ground data: implications for data assimilation. J. Hydrometeorol. 5 (3), 430–442. https://doi.org/10.1175/1525-7541(2004) 005%3C0430:GSMFSO%3E2.0.CO;2. Rudiger, C., Calvet, J.C., Gruhier, C., Holmes, T.R.H., de Jeu, R.A.M., Wagner, W., 2009. An intercomparison of ERS-Scat and AMSR-E soil moisture observations with model simulations over France. J. Hydrometeorol. 10 (2), 431–447. https://doi.org/10. 1175/2008JHM997.1. Sandholt, I., Rasmussen, K., Andersen, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ. 79 (2), 213–224. https://doi.org/10.1016/S0034-4257(01) 00274-7. Sohrabinia, M., Zawar-Reza, P., Rack, W., 2015. Spatio-temporal analysis of the relationship between LST from MODIS and air temperature in New Zealand. Theor. Appl. Climatol. 119 (3), 567–583. https://doi.org/10.1007/s00704-014-1106-2. Song, C.Y., Jia, L., Menenti, M., 2014. Retrieving high-resolution surface soil moisture by downscaling AMSR-E brightness temperature using MODIS LST and NDVI data. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7 (3), 935–942. https://doi.org/10. 1109/Jstars.2013.2272053. Wu, D., Liang, H., Cao, T., Yang, D., Zhou, W., Wu, X., 2014. Construction of operation monitoring system of automatic soil moisture observation network in China. Meteorol. Sci. Technol. 42 (2), 278–282 (written in Chinese). Yu, W., Nan, Z., Wang, Z., Chen, H., Wu, T., Zhao, L., 2015. An effective interpolation method for MODIS land surface temperature on the Qinghai-Tibet Plateau. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 8 (9), 4539–4550. https://doi.org/10. 1109/JSTARS.2015.2464094. Zhang, J., Dong, W., 2010. Soil moisture influence on summertime surface air temperature over East Asia. Theor. Appl. Climatol. 100 (1), 221–226. https://doi.org/10. 1007/s00704-009-0236-4. Zhao, W., Li, A., 2013. A downscaling method for improving the spatial resolution of AMSR-E derived soil moisture product based on MSG-SEVIRI data. Remote Sens. 5 (12), 6790–6811. https://doi.org/10.3390/rs5126790.

2017. Duan, S.-B., Li, Z.-L., 2016. Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: case study in Northern China. IEEE Trans. Geosci. Remote Sens. 54 (11), 6458–6469. https://doi.org/10.1109/TGRS.2016. 2585198. Entekhabi, D., Njoku, E.G., O’Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., et al., 2010. The soil moisture active passive (SMAP) mission. Proc. IEEE 98 (5), 704–716. https://doi.org/10.1109/JPROC.2010.2043918. Fujii, H., Koike, T., Imaoka, K., 2009. Improvement of the AMSR-E algorithm for soil moisture estimation by introducing a fractional vegetation coverage dataset derived from MODIS data. J. Remote Sens. Soc. Jpn. 29 (1), 282–292 (written in Japanese). Gao, H., Wood, E.F., Jackson, T.J., Drusch, M., Bindlish, R., 2006. Using TRMM/TMI to retrieve surface soil moisture over the southern United States from 1998 to 2002. J. Hydrometeorol. 7 (1), 23–38. https://doi.org/10.1175/JHM473.1. He, Q., Yang, T., Liu, B., Zhou, S., 2016. BeijingStudy on the Satellite-Based Precipitation Downscaling Algorithm in Tianshan Mountain, Geoscience and Remote Sensing Symposium2016. Study on the Satellite-Based Precipitation Downscaling Algorithm in Tianshan Mountain, Geoscience and Remote Sensing Symposium 605–608. https://doi.org/10.1109/IGARSS.2016.7729151. Holmes, T.R.H., De Jeu, R.A.M., Owe, M., Dolman, A.J., 2009. Land surface temperature from Ka band (37 GHz) passive microwave observations. J. Geophys. Res.-Atmos. 114 (D4), D04113–D04127. https://doi.org/10.1029/2008JD010257. Hu, X., Waller, L.A., Al-Hamdan, M.Z., Crosson, W.L., Estes, M.G., Estes, S.M., et al., 2013. Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression. Environ. Res. 121, 1–10. https://doi.org/10.1016/j. envres.2012.11.003. Im, J., Park, S., Rhee, J., Baik, J., Choi, M., 2016. Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches. Environ. Earth Sci. 75 (15), 1–19. https://doi.org/10.1007/s12665-016-5917-6. Jiang, H.T., Shen, H.F., Li, H.F., Lei, F.N., Gan, W.X., Zhang, L.P., 2017. Evaluation of multiple downscaled microwave soil moisture products over the Central Tibetan Plateau. Remote Sens. 9 (5), 402. https://doi.org/10.3390/rs9050402. Jones, L.A., Ferguson, C.R., Kimball, J.S., Zhang, K., Chan, S.K., McDonald, K.C., et al., 2010. Daily land surface air temperature retrieval from AMSR-E: comparison with AIRS/AMSU. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 3 (1), 111–123. https://doi.org/10.1109/JSTARS.2010.2041530. Kara, F., Yucel, I., Akyurek, Z., 2016. Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling. Hydrol. Sci. J. 61 (14), 2481–2495. https://doi.org/10.1080/ 02626667.2015.1133911. Kim, J., Hogue, T.S., 2012. Improving spatial soil moisture representation through integration of AMSR-E and MODIS products. IEEE Trans. Geosci. Remote Sens. 50 (2), 446–460. https://doi.org/10.1109/TGRS.2011.2161318. Merlin, O., Al Bitar, A., Walker, J.P., Kerr, Y., 2010. An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data. Remote Sens. Environ. 114 (10), 2305–2316. https://doi.org/ 10.1016/j.rse.2010.05.007. Merlin, O., Chehbouni, A.G., Kerr, Y.H., Njoku, E.G., Entekhabi, D., 2005. A combined modeling and multipectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration. IEEE Trans. Geosci. Remote Sens. 43 (9), 2036–2050. https://doi.org/10.1109/TGRS.2005. 853192. Merlin, O., Jose Escorihuela, M., Aran Mayoral, M., Hagolle, O., Al Bitar, A., Kerr, Y., 2013. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: an evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sens. Environ. 130, 25–38. https://doi.org/10.1016/j.rse.2012.11.008. Merlin, O., Malbeteau, Y., Notfi, Y., Bacon, S., Er-Raki, S., Khabba, S., Jarlan, L., 2015. Performance metrics for soil moisture downscaling methods: application to DISPATCH data in Central Morocco. Remote Sens. 7 (4), 3783–3807. https://doi.org/ 10.3390/rs70403783. Merlin, O., Rudiger, C., Al Bitar, A., Richaume, P., Walker, J.P., Kerr, Y.H., 2012. Disaggregation of SMOS soil moisture in Southeastern Australia. IEEE Trans. Geosci. Remote Sens. 50 (5), 1556–1571. https://doi.org/10.1109/TGRS.2011.2175000. Merlin, O., Walker, J.P., Chehbouni, A., Kerr, Y., 2008. Towards deterministic

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