Downscaling SMAP Soil Moisture Products With Convolutional Neural Network
نویسندگان
چکیده
Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new method convolutional neural network (CNN) is proposed solve problem. Furthermore, weight layer designed input, and residual treated as output CNN accuracy. This applied downscale Moisture Active Passive (SMAP) products (i.e., 36-km L xmlns:xlink="http://www.w3.org/1999/xlink">3 xmlns:xlink="http://www.w3.org/1999/xlink">_ xmlns:xlink="http://www.w3.org/1999/xlink">SM xmlns:xlink="http://www.w3.org/1999/xlink">P 9-km xmlns:xlink="http://www.w3.org/1999/xlink">E ) from January 1, 2018 December 30, 2018. Compared with , result satisfactory obtained correlation coefficient (R), root mean square error (RMSE), unbiased RMSE (ubRMSE) values 95.81%, 2.77%, 2.67%, respectively. Moreover, SMAP (36 9 km) (3 1 validated by situ data, collected 109 stations Oklahoma Mesonet monitoring network. Mean R, RMSE, ubRMSE 67.92%, 7.94%, 4.87% ; 67.78%, 8.35%, 4.95% 67.28%, 8.34%, 4.97% 3-km SM; 65.90%, 8.40%, 5.18% 1-km SM, The generated this can while preserving its will remarkably increase SM. Therefore, provides strategy obtains results practice. Additional studies be conducted future.
منابع مشابه
Soil Moisture Active Passive (SMAP) Algorithm Theoretical Basis Document SMAP L2 & L3 Radar Soil Moisture (Active) Data Products
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ژورنال
عنوان ژورنال: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
سال: 2021
ISSN: ['2151-1535', '1939-1404']
DOI: https://doi.org/10.1109/jstars.2021.3069774