Abstract
Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R2) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively.
Highlights
IntroductionSoil moisture (SM) is a crucial factor in hydrology, climate, and ecology models [1,2,3], and it plays an important role in the global terrestrial water, energy, and the carbon cycle [4]
Soil moisture (SM) is a crucial factor in hydrology, climate, and ecology models [1,2,3], and it plays an important role in the global terrestrial water, energy, and the carbon cycle [4].Soil moisture information is a key variable for guiding in-season management decisions in rainfed and irrigated agricultural systems [5]
Bare soil backscatter coefficient, terrain elevation, latitude, and longitude information obtained in the previous steps as the deep belief network (DBN) model input data, and use the measured data as the label data to establish the DBN soil moisture inversion model
Summary
Soil moisture (SM) is a crucial factor in hydrology, climate, and ecology models [1,2,3], and it plays an important role in the global terrestrial water, energy, and the carbon cycle [4]. Soil moisture information is a key variable for guiding in-season management decisions in rainfed and irrigated agricultural systems [5]. It is an important variable in the earth’s ecosystem because SM affects the precipitation infiltration, the distribution of surface runoff, and the control of vegetation growth [6]. Accurate acquisition of SM information is crucial for understanding the mechanisms of climate change, surface hydrological processes, and
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