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...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-3 D(FY-3 D) 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-3 D 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.展开更多
基金Supported by the Science Foundation of Shandong(ZR2017MD018)Key Research and Development Program of Ningxia(2019BEH03008)+3 种基金Open Research Project of the Key Laboratory for Meteorological Disaster MonitoringEarly Warning and Risk Management of Characteristic Agriculture in Arid Regions(CAMF-201701 and CAMF-201803)Arid Meteorological Science Research Fund Project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of China Metrological Administration(IAM201801)Science Foundation of Ningxia(NZ12278)。
文摘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-3 D(FY-3 D) 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-3 D 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.
