An Algorithm for Downscaling SMAP Soil Moisture to 3 Km Using CYGNSS Observations
IEEE Trans Geosci Remote Sens（2025）
School of Geodesy and Geomatics
Abstract
Global soil moisture (SM) mapping at high spatial and temporal resolution contributes significantly to various hydrologic and meteorological researches. This work presents an algorithm for combining fine-resolution Global Navigation Satellite System Reflectometry (GNSS-R) observations from the Cyclone Global Navigation Satellite System (CYGNSS) and coarse-resolution SM estimates from the Soil Moisture Active Passive (SMAP) mission to estimate SM at 3 km resolution. In practice, the expression for downscaled 3 km SM is derived from the mathematical double-scale SM equations based on the linear assumption between SM and reflectivity, and the CYGNSS signal-to-noise ratio data are leveraged to compensate the differences in heterogeneity between the two scales. Experimental validation over 150 in-situ sites shows strong consistency between the SMAP/CYGNSS 3 km SM estimates and the in-situ measurements, with a median correlation coefficient of 0.806 and a median unbiased root-mean-square error of 0.038 cm3/cm3. The contributions of this work are twofold: 1) introducing the normalized signal-to-noise ratio to account for the deviations in double-scale coefficients, which depends on the variations in vegetation and surface roughness between 36 km and 3 km scales, without relying on any ongoing knowledge of ancillary data; and 2) achieving daily 3 km SM estimations at a quasi-global scale, and providing a new way for enhancing the temporal and spatial resolution of SMAP SM.
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Key words
SMAP,CYGNSS,High spatial and temporal resolution,Soil moisture
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