Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-II Imagery by Using a Deep Belief Network
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Advanced Applications of Meteorological Satellite Observations in Ecological Remote Sensing
AUGUST 2020
Extracting Soil Moisture from Fengyun-3D Medium Resolution Spectral Imager-II Imagery by Using a Deep Belief Network Wenwen WANG1, Chengming ZHANG1,2*, Feng LI3, Jiaojie SONG1, Peiqi LI1, and Yuhua ZHANG1 1 College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271000 2 Shandong Technology and Engineering Center for Digital Agriculture, Tai’an 271000 3 Shandong Provincial Climate Center, Jinan 25000 (Received December 4, 2019; in final form April 27, 2020)
ABSTRACT Obtaining continuous and high-quality soil moisture (SM) data is important in scientific research and applications, especially for agriculture, meteorology, and environmental monitoring. With the continuously increasing number of artificial satellites in China, the acquisition of SM data from remote sensing images has received increasing attention. In this study, we constructed an SM inversion model by using a deep belief network (DBN) to extract SM data from Fengyun-3D (FY-3D) Medium Resolution Spectral Imager-II (MERSI-II) imagery; we named this model SM-DBN. The SM-DBN consists of two subnetworks: one for temperature and the other for SM. In the temperature subnetwork, bands 1, 2, 3, 4, 24, and 25 of the FY-3D MERSI-II imagery, which are relevant to temperature, were used as inputs while land surface temperatures (LST) obtained from ground stations were used as the expected output value when training the model. In the SM subnetwork, the input data included LSTs generated from the temperature subnetwork, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI); and the SM data obtained from ground stations were used as the expected outputs. We selected the Ningxia Hui Autonomous Region of China as the study area and used selected MERSI-II images and in-situ observation station data from 2018 to 2019 to develop our dataset. The results of the SM-DBN were validated by using in-situ SM data as a reference, and its performance was also compared with those of the linear regression (LR) and back propagation (BP) neural network models. The overall accuracy of these models was measured by using the root mean square error (RMSE) of the differences between the model results and in-situ SM observation data. The RMSE of the LR, BP neural network, and SM-DBN models were 0.101, 0.083, and 0.032, respectively. These results suggest that the SM-DBN model significantly outperformed the other two models. Key words: deep learning, deep belief network (DBN), Fengyun-3D (FY-3D), Medium Resolution Spectral Imager-II (MERSI-II) Imagery, data fitting, soil moisture (SM), Ningxia Citation: Wang, W. W., C. M. Zhang, F. Li, et al., 2020: Extracting soil moisture from Fengyun-3D Medium Resolution Spectral Imager-II imagery by using a deep belief network. J. Meteor. Res., 34(4), 748–759, doi: 10.1007/s13351-020-9191-x.
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Introduction
Variations in surface soil moisture (SM) reflect the processes of surface energy
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