Abstract

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

Highlights

  • Soil moisture (SM) is an important element in the hydrologic cycle, especially influencing precipitation, infiltration, and runoff (Hamlet et al, 2007)

  • The DISPATCHveg–ext algorithm is evaluated over the study sites and its performance is assessed compared to the DISPATCHclassic algorithm and to the Copernicus Sentinel-1based SM retrieval method

  • DISPATCHveg–ext is run in different modes with the use of Moderate resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) instead of MODIS normalized vegetation index (NDVI), and the use of Sentinel-3 land surface temperature (LST) instead of MODIS LST

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Summary

Introduction

Soil moisture (SM) is an important element in the hydrologic cycle, especially influencing precipitation, infiltration, and runoff (Hamlet et al, 2007). Based on L-band radiometer, Soil Moisture and Ocean Salinity (SMOS, Kerr et al, 2012) satellite was launched by European Space Agency (ESA) on November 2, 2009 and Soil Moisture Active Passive (SMAP, Entekhabi et al, 2010) was launched by National Aeronautics and Space Administration (NASA) on January 31, 2015. Both satellites provide SM at a sensing depth of 3–5 cm with a spatial resolution of about 40 km and a revisit cycle of about 3 days on a global basis. Since L-band emission is highly sensitive to SM and relatively less sensitive to soil roughness and vegetation optical-depth (Wigneron et al, 2017), it can be used to derive SM with high precision

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