A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province
نویسندگان
چکیده
Currently, soil-moisture data extracted from microwave suffer poor spatial resolution. To overcome this problem, study proposes a method to downscale the soil moisture The proposed establishes statistical relationship between low-spatial-resolution input and land-surface model based on neural network (NN). This is then applied high-spatial-resolution obtain data. include passive (SMAP, AMSR2), active (ASCAT), MODIS data, terrain target were collected CLDAS dataset. results show that addition of such as temperature (LST), normalized difference vegetation index (NDVI), shortwave-infrared bare indices (NSDSI), digital elevation (DEM), calculated slope (SLOPE) improves retrieval accuracy model. Taking benchmark, correlation increases 0.597 0.669, temporal 0.401 0.475, root mean square error decreases 0.051 0.046, absolute 0.041 0.036. Triple collocation was in form [NN, FY3C, GEOS-5] retrieved variance coefficient each product actual Therefore, we conclude NN which have lowest (0.00003) highest (0.811), are most applicable Qinghai Province. obtained NN, SMAP AMSR2 correlated with ground-station respectively, result better quality obtained. analysis demonstrates NN-based promising approach for obtaining
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13081583