Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG SEVIRI (METEOSAT Second Generation – Spinning Enhanced Visible and Infrared Imager). Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.
Soil moisture (SM) is known to be an important state variable that determines the partitioning of surface net energy into latent and sensible heat fluxes, as well as the partitioning of precipitation into infiltration and runoff (e.g., Porporato et al., 2004; Vereecken et al., 2014). In the context of global climate change, accurate information of soil moisture is of great importance for advancing our understanding of the energy and mass exchanges between the atmosphere, hydrosphere, and biosphere (Petropoulos et al., 2015; Seneviratne et al., 2010). In addition, soil moisture is important for numerous practical applications such as irrigation water management (Bastiaanssen et al., 2000), ecological modeling (Nemani et al., 2009), vegetation productivity estimation (Reichstein et al., 2003), and numerical weather prediction (Douville et al., 2000). However, quantifying the spatially and temporally distributed soil moisture properties is still challenging due to dynamic meteorological forcing and surface heterogeneity (Njoku et al., 2003; Loew, 2008). Traditionally, the ground-based measurements of soil moisture are interpolated to a large scale through geostatistical techniques such as kriging (Bárdossy and Lehmann, 1998; Qiu et al., 2001). Such method is however limited to areas where dense soil moisture observational networks are available.
The advent of satellite remote sensing over the past decades provides an opportunity to obtain soil moisture estimates at global and regional scales without the need of ground-based measurements. Tremendous efforts have been devoted to retrieve soil moisture with measurements from passive and active microwave remote sensing sensors/satellites, including the Advanced Microwave Scanning Radiometer E (AMSR-E) for the Earth observing system, the Advanced Microwave Scanning Radiometer-2 (AMSR2), the Advanced Scatterometer (ASCAT), the Soil Moisture and Ocean Salinity (SMOS), and the recently launched Soil Moisture Active Passive (SMAP) mission. Theoretical and experimental results suggest that both the passive and active sensors are reliable for estimating soil moisture from space (Owe et al., 2008; Petropoulos et al., 2015). The significant advantages of the microwave remote sensing techniques are that (1) dielectric constant measurement can be related directly to soil moisture; (2) soil moisture can be retrieved regardless of the atmospheric conditions (Hain et al., 2011; Loew et al., 2006). To date, several global microwave-based soil moisture products are available, such as the ASCAT soil moisture product (Wagner et al., 1999; Naeimi et al., 2009), the AMSR-E and AMSR2 soil moisture products (Owe et al., 2008; Parinussa et al., 2014a), the SMOS soil moisture product (Kerr et al., 2001; Jacquette et al., 2010), as well as the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture product. The CCI SM product is a merged product based on six microwave products (Liu et al., 2011, 2012; Wagner et al., 2012). These soil moisture products normally have a spatial resolution on the order of tens of kilometers, which serves well for global scale applications. However, this spatial resolution is often too coarse for regional and local applications such as agriculture monitoring and drought prediction, which normally require a spatial resolution of 1–10 km (Crow et al., 2000; Piles et al., 2011).
Optical/thermal infrared (TIR) sensors can provide complementary information of soil moisture patterns at higher spatial resolutions (tens of meters to several kilometers) (Zhang et al., 2014). The surface reflectance observed by optical sensors can be used to explore the state of soil moisture indirectly through empirical spectral vegetation index (Gao et al., 2013; Lobell and Asner, 2002). The common method used by thermal infrared remote sensing to estimate soil moisture is to calculate thermal inertia (Qin et al., 2013; Verstraeten et al., 2006). Yet, the observations from optical/thermal infrared sensors are only available under clear-sky conditions. To take the advantages of microwave and optical/TIR remote sensing, more and more studies try to develop synergistic techniques that use multi-sensors to estimate soil moisture at different spatial resolutions. These approaches have a wide range of complexity from empirical regression methods to physically based models (Fang and Lakshmi, 2014; Kim and Hogue, 2012; Merlin et al., 2009; Sahoo et al., 2013). A number of these methods are based on the relationship between land surface temperature (LST) and vegetation index. When the remote sensed surface temperature and vegetation index over heterogeneous areas are plotted, the shape of the scatter plot generally resembles a physically meaningful triangular or trapezoidal feature space, due to different sensitivity of surface temperature to soil moisture variations over bare soil and vegetation covered areas (Carlson et al., 1994; Peng et al., 2013b). Based on this feature space, several indexes such as vegetation temperature condition index (VTCI) (Wan et al., 2004) and temperature vegetation dryness index (TVDI) (Sandholt et al., 2002) have been widely used for assessing the status of soil moisture and monitoring drought conditions (Patel et al., 2008; Karnieli et al., 2010; Mallick et al., 2009; Peng et al., 2013a). Similarly, Chauhan et al. (2003) proposed a soil moisture downscaling scheme that links the soil moisture with surface temperature, vegetation index, and surface albedo through linear regression equation. Following this idea, some other studies have tried to improve the regression models by including other inputs such as brightness temperature and surface emissivity (Piles et al., 2014; Sobrino et al., 2012). Recently, Peng et al. (2016) proposed a new and simple method to improve the spatial resolution of microwave soil moisture with VTCI as the unique downscaling factor. They demonstrated the feasibility of the proposed method via validation against limited ground-based soil moisture measurements and spatial comparison with a land cover map. However, to further investigate the robustness of the proposed method, Peng et al. (2016) suggested that more validation work against dense soil moisture observational networks is required.
Quick view of the study area in central Spain and the location of the REMEDHUS observation network.
Therefore, the present study focuses mainly on investigating the validity and robustness of this simple downscaling scheme through comparison with ground-based soil moisture measurements from a dense observational network (REMEDHUS) in Spain (Martínez-Fernández and Ceballos, 2003). The REMEDHUS site has already been widely used for validation of soil moisture estimates from remote sensing (Brocca et al., 2011; Ceballos et al., 2005; Sánchez et al., 2012). This study has two major objectives. First, it explores and analyzes the sensitivity and robustness of VTCI on LST, vegetation index, clouds, terrain condition, and heterogeneity of land cover and validates the accuracy of soil moisture downscaling using VTCI over the REMEDHUS site. Second, it investigates the merit of using of geostationary satellite data for downscaling soil moisture. Normally the polar orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) are in general used for downscaling microwave soil moisture, while the geostationary satellite data are rarely applied. As geostationary observations can provide more cloud-free observations due to their high temporal resolution, they have the potential of estimating the thermal inertia at higher frequencies (Fensholt et al., 2007; Shu et al., 2011; Stisen et al., 2008). Hain et al. (2011) successfully used the Atmosphere-Land Exchange Inverse (ALEXI) model together with thermal infrared observations from geostationary satellites to estimate soil moisture at a relatively high spatial resolution of 3 km. Parinussa et al. (2014b) further inter-compared the geostationary satellite-based soil moisture with microwave-based soil moisture products at various spatial scales over the Iberian Peninsula. They found that all these products agree well with ground-based observations. Thus, the results from both the polar orbit satellite (MODIS Terra/Aqua) as well as geostationary satellite (MSG SEVIRI) were used in the current study to downscale the ESA CCI soil moisture product. To the best of our knowledge, this is also the first study to inter-compare the performances of geostationary and orbit satellites for downscaling soil moisture.
The current study is carried out in Spain, where a central area is selected
for downscaling CCI SM due to its relatively flat characteristic (Fig. 1).
The land cover of this region is dominated by croplands and shrublands, and
the mean elevation of the area is about 650 m a.s.l. (above sea level). The region
has a continental semiarid Mediterranean climate, which is characterized by
dry and warm summers and cool to mild and wet winters (Castro et al.,
2004; Ceballos et al., 2004). The REMEDHUS soil moisture observation network
is in the central part of the study area and shown in Fig. 1 as well. The
network covers a 35 km
Descriptions of the 19 soil moisture stations used in the study.
Several satellite platforms with different temporal and spatial resolutions are used in this study. They provide different land surface products such as soil moisture, LST, and vegetation indexes. Table 2 gives an overview of these satellite products used in our study.
Descriptions of the satellite-based products used in this study.
The ESA CCI soil moisture is a unique multi-decadal (35 years from 1978 to
2013) satellite-based soil moisture data set on a daily basis and at a
spatial resolution of 0.25
The MODIS is the primary instrument in the NASA Earth Observing System (EOS) Terra and Aqua satellites, which were launched in December 1999 and May 2002, respectively. With 36 discrete spectral bands ranging from visible to near infrared to thermal infrared, the MODIS has been widely used for land, ocean, and atmosphere research (Salomonson et al., 1989; Huete et al., 2002). The Terra and Aqua satellites have different overpass times of 22:30/10:30 LT for Terra and 01:30/13:30 LT for Aqua in ascending/descending modes. The MODIS data from either Terra or Aqua have been used for downscaling soil moisture (e.g., Srivastava et al., 2013; Choi and Hur, 2012). The surface temperature normally has strong diurnal variation. Therefore, the surface temperature products provided by Terra and Aqua have different values due to the different overpass times of Terra and Aqua. Since surface temperature is one of the most important inputs in downscaling methods, both MODIS/Terra and MODIS/Aqua are used to downscale soil moisture in this study. The MODIS products used in this study are Collection 5 MODIS LST and vegetation indexes (MOD11C1, MYD11C1, MYD11A1 MOD13C1, MCD15A3). Among them, MOD11C1 and MYD11C1 provide daily LST at 0.05 spatial resolution, while MYD11A1 provides daily surface temperature at 1 km resolution. MOD13C1 contains 16-day composite of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). MCD15A3 provides the combined (Terra and Aqua) MODIS leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation products every 4 days at 1 km resolution. The above products have all been validated against a wide range of in situ observations, and applied in many scientific studies (Coll et al., 2009; Fensholt et al., 2004; Tian et al., 2002).
The Spinning Enhanced Visible and Infrared Imager (SEVIRI) is the main
instrument on board Meteosat Second Generation (MSG) geostationary
satellites (Schmetz et al., 2002). The SEVIRI has 12
separate channels from visible to thermal infrared and can provide
observations at very high temporal resolution (every 15 min), which makes it
possible to resolve the diurnal cycle of environmental variables such as
LST (Peres and DaCamara, 2004). So far,
geostationary satellite data have not been used for soil moisture
downscaling. Compared to the polar orbit satellites, the geostationary
satellites normally provide measurements with relatively low spatial
resolution. As for SEVIRI, its spatial resolution is approximately 4.8 km
with spatial sampling of 3 km for nadir view. But the major advantage of the
SEVIRI over MODIS is the high temporal frequency (96 times per day), which
can highly increase the possibility of obtaining cloud-free measurements.
Furthermore, the downscaling approach used in this study can further benefit
from the increased observation frequency through obtaining thermal inertial
information from the surface temperature diurnal cycle. The 15 min 4.8 km LST product generated by the Land Surface Analysis Satellite
Applications Facility (LSA-SAF;
It should be noted here that the satellite-based products used in this study
have different data formats, spatial resolutions, and projections. Therefore,
a preprocessing is required to make them consistent in space. The surface
temperature and vegetation index products from MODIS and SEVIRI are all
resampled to a regular latitude–longitude grid with 0.05
The methodology used in this study includes a soil moisture downscaling scheme and evaluation strategies. The details are described in the following sections.
Conceptual diagram of the triangular/trapezoidal feature space that is constructed by land surface temperature and vegetation index.
The soil moisture downscaling scheme used in this study was proposed by
Peng et al. (2016). It uses VTCI as the only scaling factor to
improve the soil moisture from coarse to high spatial resolution. The
theoretical basis of this approach is that the VTCI can represent the status
of soil moisture, and has been widely used for estimating soil moisture and
monitoring drought conditions (Petropoulos et al., 2009). The estimation
of VTCI is based on the triangular or trapezoidal feature space that is
constructed by LST and vegetation index (Fig. 2) over
the study area. It is calculated by rescaling the surface temperature of
each pixel between two extreme surface temperature values for each
vegetation index interval:
In order to downscale the coarse-resolution CCI soil moisture, the spatially
average VTCI (
Different combinations of the five influential factors (surface temperature, vegetation index, cloud, topography and land cover heterogeneity) for the estimation of VTCI.
Since VTCI is the key variable in the downscaling scheme, it determines the
accuracy of the downscaled soil moisture field. The performance of VTCI
mainly depends on the accuracy of surface temperature and type of vegetation
index. Furthermore, clouds, terrain, and land cover heterogeneity might be
also sources of the errors for the estimation of VTCI (de
Tomás et al., 2014). Therefore, to optimize the performance of VTCI as
the proxy of soil moisture, a sensitivity analysis of VTCI to five
influential factors (surface temperature, vegetation index, cloud,
topography, and land cover heterogeneity) was conducted before downscaling
soil moisture. The VTCI was firstly calculated with different settings (see
Table 3). Then the estimated VTCI was compared with ground-based soil
moisture measurements from the REMEDHUS network to investigate the influence
of the five factors on the performance of VTCI. The details of these factors
are listed below, and the combinations of these factors for estimating VTCI
are shown in Table 3.
Finally, the optimized VTCI is calculated based on the sensitivity analysis results. The CCI soil moisture is then downscaled with the optimized VTCI.
Two metrics are used to evaluate the performance of the downscaled soil
moisture. The first is a direct comparison between satellite-based products
including CCI SM and downscaled CCI SM, and measured soil moisture at each
REMEDHUS station. In addition, the cross-comparisons between the present
results and reported results from published researches are summarized as
well. In order to investigate the influence of land use on the downscaled
soil moisture, the comparisons between satellite-based results and in situ
measurements are performed per land use of the stations. These land uses are
rainfed, vineyard, and forest–pasture. Furthermore, a seasonal analysis is
also carried out to examine its influence on downscaled soil moisture. The
study period is separated into four seasons to represent different dry/wet
conditions and vegetation growth conditions. The four seasons are
September–October–November (autumn), December–January–February (winter),
March–April–May (spring), and June–July–August (summer). The
widely used statistical metrics including
Figure 3 shows the box plots of
Sensitivity analyses of VTCI to different variables. The box plots
show the
The change of the VTCI when using different types of vegetation index is also
shown in Fig. 3. The LAI gives the best performance in terms of mean
Regarding the influence of clouds, Fig. 3 shows that the 85 % cloud mask gives worse performance for VTCI than the 75 % cloud mask. In theory, the increase of the cloud mask threshold should lead to the improvement of the accuracy of VTCI. The opposite result obtained here is due to the sharply decreased sample days for a 85 % cloud mask. It should be noted that the totally clear sky in the study domain is rare in real conditions. The higher cloud mask threshold normally results in less sample days. To keep the balance between avoiding the influence of clouds and having more sample days, we use a 75 % cloud mask in this study.
Contrary to our expectations, the masked terrain performs quite similar to the non-masked terrain method. It is because the masked out pixels are normally located within the triangular/trapezoidal feature space (see green color points in Fig. 5f and g), which means the dry and wet edges keep almost the same for both methods. Therefore, the terrain has no strong impacts on VTCI in our study area. But for other study areas, the terrain effects still need to be investigated before the estimation of VTCI.
The use of full land cover types gives better performance of the VTCI than the use of croplands. It suggests that although the use of croplands keeps the surface cover more homogeneous in terms of vegetation properties and surface roughness, the range of surface moisture conditions that are required for the estimation of VTCI meanwhile decreases. In arid or semiarid study areas, such as our study area, only one vegetation type cannot represent a wide range of soil moisture that is required by the estimation of VCTI. For these kinds of study areas, the requirements of homogeneous surface cover and wide range of soil moisture conditions cannot be met at the same time. As shown in our results, for the estimation of VTCI, having a wide range of soil moisture is more important than keeping surface cover homogeneous in such areas.
Based on the above results, it can be seen that these factors have strong impacts on performance of VTCI. The optimal configurations in this study for the estimation of VTCI are using Aqua MODIS daytime–nighttime temperature difference, SEVIRI maximum and minimum temperature difference, LAI, 75 % cloud mask, non-masked terrain, and full land cover types.
To further investigate the performance of VTCI as the proxy of soil
moisture, the temporal evolution of station-averaged LAI, surface
temperature, VTCI, and in situ soil moisture over REMEDHUS are presented in
Fig. 4. Meanwhile, the
Time series of the station-averaged LAI, surface temperature, VTCI,
and in situ soil moisture over REMEDHUS during the study period. The
Spatial comparisons between coarse CCI soil moisture
Bar plots for the comparisons between CCI soil moisture, downscaled soil moisture, and in situ soil moisture at each station.
Time series of the in situ soil moisture, CCI soil moisture, downscaled CCI soil moisture, as well as rainfall for stations K13 and M13. The results from MODIS and SEVIRI are shown separately.
On the basis of VTCI, the CCI SM is downscaled to high spatial resolution using the proposed method during the study time period. The spatial distributions of original CCI SM and downscaled soil moisture on 22 May 2010 are shown in Fig. 5. It can be clearly seen that the downscaled soil moisture (Fig. 5b and c) have quite similar spatial patterns as the original CCI SM. High soil moisture is typically presented in northwest and southwest, while low soil moisture appears in northeast and southeast. Meanwhile, the spatial details of the soil moisture are highly improved by the downscaling scheme. The downscaled soil moisture map (Fig. 5d) generated from MODIS also exhibits very similar patterns as that (Fig. 5e) from SEVIRI. It is due to the similar VTCI patterns calculated from MOIDS and SEVIRI. The similar shape of the triangular/trapezoidal feature space constructed from MODIS and SEVIRI can also be seen from Fig. 5f and g. These results suggest that the proposed downscaling scheme can capture the spatial pattern of original CCI soil moisture, and similar performance of downscaled soil moisture maps can be obtained from both MODIS and SEVIRI. The following section will further investigate the accuracy of the downscaled soil moisture and quantify the difference between estimates from MODIS and SEVIRI.
The validation results between original CCI SM and in situ soil moisture
measurements at each station are shown in Fig. 6, with mean
The temporal variations of downscaled soil moisture for individual station are also investigated here. The stations K13 and M13 are selected due to their representatives of wet and dry soil moisture conditions (Sánchez et al., 2012). Furthermore, the location of K13 is close to one weather station (Fig. 1), which gives us the chance to investigate the connection between soil moisture and rainfall. Figure 7 displays the time series of the in situ soil moisture, CCI soil moisture, downscaled CCI soil moisture, as well as rainfall for stations K13 and M13. For dry station K13, the CCI soil moisture and downscaled soil moisture both agree well with in situ soil moisture. Regarding the wet station M13, the in situ soil moisture responds well to the rainfall, with high soil moisture occurring during the rainfall period in spring. But the CCI soil moisture and downscaled soil moisture seem to be insensitive to the rainfall, presenting a relatively low value compared to in situ soil moisture during the rainfall period. The results here are similar to that reported by Sánchez-Ruiz et al. (2014); i.e., they found that the downscaled SMOS soil moisture has a limited response to rain events.
Summary of the error statistics from other soil moisture downscaling studies also using REMEDHUS observations for validation. The statistics of the current study are from the comparison of station-averaged soil moisture.
In addition to the validation at each individual station, the performance of
different soil moisture results averaged at the REMEDHUS network scale are also
analyzed and summarized in Fig. 8. Similar to the above results, both
original CCI SM and downscaled soil moisture agree well with the averaged in
situ soil moisture over the network in terms of
Furthermore, the above results are also compared with other published soil
moisture downscaling studies. These studies apply different downscaling
methods to downscale soil moisture product from SMOS and AMSR-E, using
either MODIS or SEVIRI data as inputs. The results are all validated against
the observations from the REMEDHUS network, which makes them ideal for
inter-comparison with our results. Table 4 lists the statistics of the
comparison between downscaled and measured soil moisture from different
studies. It can be seen that the
Scatter plots of the REMEDHUS network-averaged estimates and soil moisture measurements. The corresponding comparison statistics are shown as well.
To investigate the performance of the downscaling method over different
climatic and vegetation growth conditions, seasonal analysis has been
performed based on the comparisons between network-averaged soil moisture
estimates and in situ soil moisture observations for different seasons.
Figure 9 shows the statistical results of the comparisons between CCI SM,
downscaled soil moisture, and in situ soil moisture. It can be seen that the
downscaled soil moisture especially from SEVIRI has a similar performance to
the
original CCI SM, with better performance in summer and winter in terms of
Bar plots for the comparisons between soils moisture estimates and in situ soil moisture over seasons.
Bar plots for the comparisons between soils moisture estimates and in situ soil moisture per land use (vineyard, rainfed, and forest–pasture).
Spatial patterns of the downscaled soil moisture from MODIS at
Bar plots for the comparisons between measured soil moisture and
downscaled soil moisture at 1 km and 0.05
The influence of land use on the downscaling scheme is also investigated and
the results are summarized in Fig. 10. The stations are divided into three
land use groups: vineyard (7), rainfed (9) and forest–pasture (2). Figure 10
shows the performances of original CCI SM and downscaled SM over different
land use categories. It can be seen that vineyard and rainfed have a similar
performance in terms of
Since MODIS has the advantage of providing measurements at high spatial
resolution of 1 km, it gives us the opportunity to evaluate the downscaled
soil moisture at different spatial resolutions. Figure 11 displays the
spatial patterns of downscaled soil moisture at 1 km and 0.05
In this study, a newly developed soil moisture downscaling method was applied to the ESA CCI soil moisture product and validated against the REMEDHUS soil moisture observation network in Spain. In general, agreement between the CCI soil moisture and the in situ soil moisture was observed with a similar accuracy level to the published validation studies. But systematic overestimation of soil moisture was also observed for CCI SM from the network-averaged analysis. Before applying the downscaling scheme, the sensitivity analyses of the downscaling factor VTCI were conducted. The surface temperature difference method performs better than instantaneous surface temperature method due to the integrated information of thermal inertial. Furthermore, the VTCI performance also depends on the type of vegetation index. The LAI performs best for the estimation of VTCI compared to NDVI, EVI, and FPAR. After downscaling the CCI soil moisture from coarse to high spatial resolution, the soil moisture map can replicate the CCI soil moisture spatial patterns and show more spatial details. Comparisons with in situ soil moisture indicate that the downscaled soil moisture can maintain the accuracy of original CCI soil moisture. Further inter-comparisons with published soil moisture downscaling studies suggest that the accuracy level of the proposed method is comparable. Compared with those methods, the advantages of the proposed method are its simplicity, the fewer required inputs, and comparable accuracy level.
In addition, the downscaled soil moisture from MSG SEVIRI performs better than that from MODIS, which is due to the better performance of corresponding VTCI from SEVIRI. It indicates the great potential of applying SEVIRI to downscale soil moisture. To take full advantages of the high temporal resolution of SEVIRI and high spatial resolution of MODIS, combined use of data from both platforms should be considered in soil moisture downscaling applications in the future.
In summary, the present study, together with the work by Peng et al. (2016), demonstrated the feasibility of downscaling soil moisture with the proposed method. The notable advantage of this approach is simplicity in terms of inputs requirement and implementation. Furthermore, the proposed method is independent on satellite platforms, implying that the downscaled soil moisture can be obtained at either very high spatial resolution (500 m for MODIS) or very high temporal resolution (every 15 min for SEVIRI). It has potential to facilitate regional hydrological related studies that require soil moisture information at different spatial and temporal scales. Application of the proposed method in other regions and comparison with other downscaling methods will be conducted in future studies.
The authors would like to thank the Level 1 and Atmosphere Archive and Distribution System (LAADS) and the Land Surface Analysis Satellite Applications Facility (LSA-SAF), as well as European Space Agency (ESA) for providing the satellite-based products. In addition, the authors would like to thank the International Soil Moisture Network (ISMN) for making the in situ measurements over REMEDHUS publicly available. The authors also would like to thank José Martínez-Fernández from Universidad de Salamanca for providing rainfall measurements from weather stations installed in the REMEDHUS observation network. In addition, the authors thank Fabio Cresto Aleina for reviewing a previous version of the manuscript. This research was supported by the Cluster of Excellence CliSAP (EXC177), University of Hamburg, funded through the German Science Foundation (DFG), the MPG-CAS postdoc fellowship, as well as the ESA CCI programme (CMUG). The article processing charges for this open-access publication were covered by the Max Planck Society. Edited by: W. Wagner