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Open AccessArticle

Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method of SMOS Soil Moisture Data

isardSAT, 08042 Barcelona, Spain
CESBIO, Université de Toulouse, CNES/CNRS/IRD/UPS, 31400 Toulouse, France
Centre National de Lutte Antiacridienne, Ait-Melloul (CNLAA), Inezgane, Agadir 86343, Morocco
Department of Environment and Soil Sciences, Universitat de Lleida, 25003 Lleida, Spain
LP2M2E, Faculté des Sciences et Techniques, Université Cadi Ayyad, Marrakech 40000, Morocco
Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1671;
Received: 31 March 2020 / Revised: 19 May 2020 / Accepted: 20 May 2020 / Published: 23 May 2020
The resolution of current satellite surface soil moisture (SM) estimates is very low, of tens of kilometers, which proves to be insufficient for various agricultural and hydrological applications. Amongst the existing downscaling approaches of remotely sensed SM, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) improves the resolution of SMOS (Soil Moisture and Ocean Salinity) soil moisture data using soil evaporative efficiency (SEE) estimates at high resolution (HR) and a SEE(SM) model implemented at low resolution (LR). Defined as the ratio of actual to potential soil evaporation, SEE can be derived from the remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI). The current version of DISPATCH uses a linear SEE(SM) model. This study aims at improving the SEE(SM) model and testing different calibration strategies, to ultimately have more robust and better downscaled SM products. A nonlinear SEE(SM) model is introduced and its influence on the derived HR SM products is studied over a range of conditions. Each model, linear and nonlinear, is calibrated from remote sensing data on a daily and a multi-date basis. The approaches were tested over two mixed dry and irrigated areas in Catalonia, Spain, and over one dry area in Morocco. When using the linear model, better statistical results were generally obtained using a daily calibration (current version of DISPATCH), most notably over one Spanish site. However, the best results were systematically obtained for an annually calibrated nonlinear model, in terms of all metrics considered: correlation coefficient, slope of the linear regression, bias, unbiased root mean square error. In particular, when using the annually calibrated nonlinear SEE (SM) model, the temporal slope of the linear regression between disaggregated and in situ soil moisture increased to 1.16 and 0.75 for one Spanish site and for the Moroccan site (as opposed to 0.44 and 0.58, respectively, when using the linear model with a daily calibration). The temporal correlation coefficient increased to 0.47 and 0.54 over the Spanish sites (as opposed to 0.18 and 0.27, respectively, when using the linear model with a daily calibration). Those contrasted results indicate compensation effects between the model type and the calibration strategy. Taking into account studies that report the strong nonlinear behavior of the SEE with respect to SM, the introduction of the nonlinear SEE(SM) model in DISPATCH, combined with a multi-date calibration, is proven to perform significantly better under various conditions, leading to more robust disaggregated SM products. The SEE modeling based on the nonlinear SM model, with a multi-date calibration, could be integrated into the CATDS—Centre Aval de Traitement des Données SMOS as a future product, as well as into existing evapotranspiration models, which are based on a combination of thermal and microwave data. View Full-Text
Keywords: soil moisture; downscaling; disaggregation; SMOS; evaporation; MODIS soil moisture; downscaling; disaggregation; SMOS; evaporation; MODIS
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Stefan, V.-G.; Merlin, O.; Escorihuela, M.-J.; Molero, B.; Chihrane, J.; Villar, J.M.; Er-Raki, S. Temporal Calibration of an Evaporation-Based Spatial Disaggregation Method of SMOS Soil Moisture Data. Remote Sens. 2020, 12, 1671.

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