Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example

Abstract

Abstract. 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.

Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements

“…The CCI SM has comparable performance to ERA-Interim. The accuracy levels are also similar to that reported by validation studies over other regions [59,61,62]. Additionally, the seasons and land use have little effect on the performance of these products.…”

Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method

“…Over the past few years, vegetation index and surface temperature have been widely used for downscaling satellite soil moisture data (Fang & Lakshmi, 2014;Im et al, 2016;Merlin et al, 2008Merlin et al, , 2015Peng et al, 2015;Piles et al, 2011;Song et al, 2014;Srivastava et al, 2013). These land surface parameters are available from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument.…”

A New Soil Moisture Downscaling Approach for SMAP, SMOS, and ASCAT by Predicting Sub-Grid Variability

“…In addition to the general investigation of sub-grid heterogeneity for identifying adequate (homogeneous) validation sites and evaluating the representativeness of single soil moisture sensors for large areas covered by a satellite pixel, the estimated σ θ θ function in the final data set can be used for soil moisture disaggregation using proxy information which describes the spatial patterns within each coarse scale satellite pixel. Proxy data can be higher resolution Earth observation data with a proven relationship to soil moisture variability such as surface temperature [101,102], vegetation [103], a combination of both [104], radar backscatter [84], or even soil texture [105]. The proxy data need to be normalized for each individual grid cell.…”