Abstract
Satellite remote sensing has been providing passive microwave soil moisture (SM) retrievals of a global spatial coverage and a high revisit frequency for research and applications in earth and environmental sciences, specifically after the soil moisture active and passive (SMAP) was launched in 2015. But, the spatial resolution of SM data is restricted to tens of kilometers, which is insufficient for regional or watershed scale studies. In this article, an SM downscaling algorithm was developed based on the vegetation modulated apparent thermal inertia relationship between SM and changes in land surface temperature (LST). The algorithm used data sets from the North America Land Data Assimilation System Noah model outputs and the advanced very high resolution radiometer data of the long term data record from 1981 to 2018. Here, the downscaling model was applied to visible/infred LST data from the visible infrared imaging radiometer suite at 400-m and the moderate resolution imaging spectroradiometer at 1 km to downscale the L2 radiometer half-orbit 9 km SMAP SM from 2018 to 2019 for the contiguous United States. The 400-m/1-km downscaled SM products were validated using 125 in situ SM ground measurements acquired from the International Soil Moisture Network. The validation results summarized by SM network show that the overall unbiased RMSE for 400-m of the improved/original downscaling algorithms and 1-km SM outperform 9-km SM by 0.01, 0.007, and 0.012 m3/m3 volumetric soil moisture, respectively, which indicates a fairly good performance of the downscaling algorithm. It is also found that precipitation has an impact on the 9-km SMAP SM.
Highlights
S OIL moisture is described as the water content of the soil layer and generally in the case of remote sensing, this is Manuscript received December 12, 2020; revised February 15, 2021 and April 6, 2021; accepted April 21, 2021
Another reason is we discarded the pixels of land surface temperature (LST) retrievals which are labeled as missing or poorly/badly calibrated, by the associated data quality flag of visible infrared imaging radiometer suite (VIIRS)/moderate resolution imaging spectroradiometer (MODIS) data
In this article, an improved soil moisture (SM) downscaling algorithm which was based on vegetation modulated ATI relationship between
Summary
S OIL moisture is described as the water content of the soil layer and generally in the case of remote sensing, this is Manuscript received December 12, 2020; revised February 15, 2021 and April 6, 2021; accepted April 21, 2021. Soil moisture is an important variable for studying land surface processes, water and heat flux exchanges and interactions between land and atmosphere. It is a key input variable in land surface models (LSMs) for numerous studies in hydrology, agriculture, climatology, weather forecasting, and ecology. Remote sensing techniques have been providing global coverage soil moisture (SM) retrievals from passive microwave satellite sensor observations in recent decades [1]–[3]. The L-band microwave TB observations are favored to provide top surface SM retrievals (0–5 cm depth) with reliable accuracy [23]–[26]. The SMAP SM were produced by the single channel algorithm (SCA), which used TB observations at both horizontal and vertical polarizations [1]
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