Abstract
Validation of satellite-based soil moisture products is necessary to provide users with an assessment of their accuracy and reliability and to ensure quality of information. A key step in the validation process is to upscale point-scale, ground-based soil moisture observations to satellite-scale pixel averages. When soil moisture shows high spatial heterogeneity within pixels, a strategy which captures the spatial characteristics is essential for the upscaling process. In addition, temporal variation in soil moisture must be taken into account when measurement times of ground-based and satellite-based observations are not the same. We applied spatio-temporal regression block kriging (STRBK) to upscale in situ soil moisture observations collected as time series at multiple locations to pixel averages. STRBK incorporates auxiliary information such as maps of vegetation and land surface temperature to improve predictions and exploits the spatio-temporal correlation structure of the point-scale soil moisture observations. In addition, STRBK also quantifies the uncertainty associated with the upscaled soil moisture which allows bias detection and significance testing of satellite-based soil moisture products. The approach is illustrated with a real-world application for upscaling in situ soil moisture observations for validating the Polarimetric L-band Multi-beam Radiometer (PLMR) retrieved soil moisture product in the Heihe Water Allied Telemetry Experimental Research experiment (HiWATER). The results show that STRBK yields upscaled soil moisture predictions that are sufficiently accurate for validation purposes. Comparison of the upscaled predictions with PLMR soil moisture observations shows that the root-mean-squared error of the PLMR soil moisture product is about 0.03 m3·m−3 and can be used as a high-resolution soil moisture product for watershed-scale soil moisture monitoring.
Highlights
Soil moisture is a key variable in controlling the exchange of water and energy fluxes between the hydrosphere, biosphere, and atmosphere [1]
More accurate results can be obtained by using a geostatistical upscaling method, i.e., block kriging (BK), which considers the spatial structure in the data
The main advantages of spatio-temporal regression block kriging (STRBK) are: (1) it is a spatio-temporal upscaling method which can scale up observations to any desired spatio-temporal support; (2) it takes a spatio-temporal trend into account which is treated as a function of environmental covariates; and (3) it uses more observations than a purely spatial upscaling method, and is less sensitive to random measurement errors
Summary
Soil moisture is a key variable in controlling the exchange of water and energy fluxes between the hydrosphere, biosphere, and atmosphere [1]. Understanding soil moisture variation in space and time is, a critical part of many scientific studies and operational applications, such as flood forecasting, weather and climate prediction, and crop growth modeling and monitoring [2]. Soil moisture is generally obtained through in situ measurements, remote sensing technology, or land surface models. A series of global-scale and regional-scale remotely sensed surface soil moisture products have become available. Applications highlight the need to validate satellite-based soil-moisture products against ground-based observations, traditionally via in situ measurements
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