Abstract
This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010–June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE). Good global results were obtained with CC = 0.67, RMSE = 0.055 m3/m3 and Bias = −0.0005 m3/m3, particularly over Australia, Central USA, and Central Asia. With these trained networks over each pixel, a global and long-term soil moisture time series, i.e., 2003–2015, was built using AMSR-E TB from 2003 to 2011 and AMSR2 TB from 2012 to 2015. Then, NNsm products were evaluated against in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (SCANsm). The results show that NNsm has a good agreement with in situ data, and can capture the temporal dynamics of in situ SM, with CC = 0.52, RMSE = 0.084 m3/m3 and Bias = −0.002 m3/m3. We also evaluate the accuracy of NNsm by comparing with AMSR-E/AMSR2 SM products, with results of a regression method. As a conclusion, this study provides a promising BPNN method adopting MVI to rebuild a long-term SM time series, and this could provide useful insights for the future Water Cycle Observation Mission (WCOM).
Highlights
Land surface soil moisture (SM), which is the water stored in the upper soil layer, is a key variable to improve our understanding of the energy and water cycles in the Earth system; it is an important parameter in climate, hydrology, and environment [1,2,3,4]
This study investigates the feasibility of a back propagation neural network (BPNN) method to build a long time-term soil moisture time series using SMOSL3sm products and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)/Advanced Microwave Scanning Radiometer 2 (AMSR2) TB observations
We evaluated the quality of the training step over the training period
Summary
Land surface soil moisture (SM), which is the water stored in the upper soil layer, is a key variable to improve our understanding of the energy and water cycles in the Earth system; it is an important parameter in climate, hydrology, and environment [1,2,3,4]. Lack of spatial-temporal-consistent, long time series products, existing SM data with various resolutions, and accuracy cannot provide effective support for the study of the water cycle response mechanism to global climate change, which has been identified as one of scientific objectives of the new Chinese satellite mission of WCOM [9,10]. It is necessary to build space-temporal-consistent, long time series products of SM, to answer the scientific problems in the study of the water cycle and climate change. Reg_sm of the algorithm used by retrieve global time series soil moisture datasets, with SMOSL3sm as a training reference target and Al-Yaari. In the training period, compared with the reference SMOSL3sm, NNsm has a slightly higher CC.
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