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Open AccessArticle

Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture

1
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
2
Institution of Ecological and Agricultural Meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3
College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
4
Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
5
Faculty of Environment, University of Science, Vietnam National University, Ho Chi Minh City 700000, Vietnam
6
Shandong General Station of Agricultural Technology Extension, Jinan 250013, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1038; https://doi.org/10.3390/rs12061038
Received: 22 January 2020 / Revised: 19 March 2020 / Accepted: 21 March 2020 / Published: 24 March 2020
Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results. View Full-Text
Keywords: soil moisture estimation; remote sensing; FengYun-3C VSM product; quantile regression; multivariate linear regression; back-propagation neural network soil moisture estimation; remote sensing; FengYun-3C VSM product; quantile regression; multivariate linear regression; back-propagation neural network
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MDPI and ACS Style

Wang, L.; Fang, S.; Pei, Z.; Zhu, Y.; Khoi, D.N.; Han, W. Using FengYun-3C VSM Data and Multivariate Models to Estimate Land Surface Soil Moisture. Remote Sens. 2020, 12, 1038.

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