Abstract
Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6 . 2 % and 2 . 5 % on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.
Highlights
Soil moisture (SM) is an essential climate variable [1] and one of the most important drivers of the hydrological cycle, as it governs the redistribution of precipitation between infiltration and runoff [2]
We compute the temporal correlation between different SM retrievals (DCNN SM, neural network (NN) SM and ERA-Interim SM) and the in situ measurements in the testing phase
We compare the performance of deep convolutional neural network (DCNN) and NN in estimating SM from satellite observations
Summary
Soil moisture (SM) is an essential climate variable [1] and one of the most important drivers of the hydrological cycle, as it governs the redistribution of precipitation between infiltration and runoff [2]. It is a key variable in better understanding of the land–atmosphere interactions [3]. SM data can be obtained from in situ measurements and satellite observations. In situ measurements provide accurate SM information and enable frequent acquisitions of data each day, but only for individually discrete locations. Satellite observations can acquire the large-scale SM dynamics at reasonable temporal intervals. Microwave observations have proven to be one of the most promising remote sensing approaches to monitor SM at the global scale [5,6,7]
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