Improving the AMSR-E/NASA Soil Moisture Data Product Using In-Situ Measurements from the Tibetan Plateau

Qiuxia Xie,Li Jia,Massimo Menenti

doi:10.3390/rs11232748

Abstract

The daily AMSR-E/NASA (the Advanced Microwave Scanning Radiometer-Earth Observing System/the National Aeronautics and Space Administration) and JAXA (the Japan Aerospace Exploration Agency) soil moisture (SM) products from 2002 to 2011 at 25 km resolution were developed and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) and JAXA archives, respectively. This study analyzed and evaluated the temporal changes and accuracy of the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM product. The accuracy of both AMSR-E/NASA and JAXA SM was low, with RMSE (root mean square error) > 0.1 cm3 cm−3 against the in-situ SM measurements, especially the AMSR-E/NASA SM. Compared with the AMSR-E/JAXA SM, the dynamic range of AMSR-E/NASA SM is very narrow in many regions and does not reflect the intra- and inter-annual variability of soil moisture. We evaluated both data products by building a linear relationship between the SM and the Microwave Polarization Difference Index (MPDI) to simplify the AMSR-E/NASA SM retrieval algorithm on the basis of the observed relationship between samples extracted from the MPDI and SM data. We obtained the coefficients of this linear relationship (i.e., A0 and A1) using in-situ measurements of SM and brightness temperature (TB) data simulated with the same radiative transfer model applied to develop the AMSR-E/NASA SM algorithm. Finally, the linear relationships between the SM and MPDI were used to retrieve the SM monthly from AMSR-E TB data, and the estimated SM was validated using the in-situ SM measurements in the Naqu area on the Tibetan Plateau of China. We obtained a steeper slope, i.e., A1 = 8, with the in-situ SM measurements against A1 = 1, when using the NASA SM retrievals. The low A1 value is a measure of the low sensitivity of the NASA SM retrievals to MPDI and its narrow dynamic range. These results were confirmed by analyzing a data set collected in Poland. In the case of the Tibetan Plateau, the higher value A1 = 8 gave more accurate monthly AMSR-E SM retrievals with RMSE = 0.065 cm3 cm−3. The dynamic range of the improved retrievals was more consistent with the in-situ SM measurements than with both the AMSR-E/NASA and JAXA SM products in the Naqu area of the Tibetan Plateau in 2011.

Highlights

Soil moisture is a key variable for energy balance research and climate change analysis
We have shown that a simple linear relationship (Equation (17)) with a slope equal to 8 gives accurate soil moisture (SM) retrievals when the brightness temperature is calculated from in-situ SM measurements (Case d in Figure 8 and Table 3)
We have evaluated the AMSR-E/National Aeronautics and Space Administration (NASA) SM just in one study area, i.e., Naqu on the Tibetan Plateau, but we concluded that the reason leading to the unrealistically narrow dynamic range of AMSR-E/NASA SM was the very low value, i.e., 1 instead of 8, of the parameter A1 = a1· exp(a2·g∗) which is related as shown to the parameters in the AMSR-E/NASA SM algorithm

Summary

Introduction

Soil moisture is a key variable for energy balance research and climate change analysis. Long time series of soil moisture at a global level is very useful to understand the land-atmosphere exchange of energy and water [1]. Since the 1970s, with the development of passive microwave remote sensing, it has been possible to generate long time series of soil moisture data at a global level. By applying multiple SM retrieval methods, such as semi-empirical regression and a single channel algorithm, the long time series of SM data have been generated (Table 1), there are no publicly available SM products generated with the data acquired by the SMMR and SSM/I sensors. Observations by AMSR-E are widely used to retrieve SM, and a variety of SM products has been developed by applying, e.g., the land parameter retrieval method (LPRM), single-channel algorithm (SCA), and the look-up table (LUT) algorithm [4,5,6,7] (Table 1)

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