Abstract
A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.
Highlights
Surface soil moisture (SM), which is one of the most important factors in regional and global water cycle processes, has played an increasingly important role in the earth sciences
The results showed that the three SM products have different advantages and disadvantages, which leads to the conclusion that SM products must be improved if they are to be used in the cold, semi-arid areas of China
AMSR2 TB-X-H and TB-X-V were selected because the Japan Aerospace Exploration Agency (JAXA) and Land Surface Parameter Model (LPRM) SM products adopted in the current study are both obtained from X-band TB
Summary
Surface soil moisture (SM), which is one of the most important factors in regional and global water cycle processes, has played an increasingly important role in the earth sciences (e.g., meteorology, hydrology, agriculture, and biomass analysis). The ongoing operation of these major satellite sensors is intended to estimate parameters that are vital for the hydrological and meteorological sciences, such as surface SM. These SM products have played an important role and have shown great potential in climatic and meteorological studies [8,9,10]
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