Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDRReport as inadecuate


Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR


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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China



These authors contributed equally to the work.





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Academic Editor: Wolfgang Kainz

Abstract In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite VIIRS raw data records RDR were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R ¯ 2 = 0.9875 and a root mean square error RMSE of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive SMAP active radar soil moisture products and the Global Land data assimilation system GLDAS 0–10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale. View Full-Text

Keywords: VIIRS; deep learning; surface soil moisture VIIRS; deep learning; surface soil moisture





Author: Dongying Zhang †, Wen Zhang †, Wei Huang, Zhiming Hong and Lingkui Meng *

Source: http://mdpi.com/



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