Abstract
High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.
Highlights
Soil moisture (SM) is a critical indicator of the water and energy budgets at the land surface, and a key variable that links the land surface and atmosphere [1]
The most widely used operational passive microwave remotely sensed SM products originate from soil moisture active passive (SMAP), soil moisture and ocean salinity (SMOS), FY3B, and advanced microwave scanning radiometer 2 (AMSR2), with AMSR2 mainly including the Japan Aerospace Exploration Agency (JAXA) and land parameter retrieval model (LPRM) products
The corresponding SM-retrieval algorithms of these five SM products include the single-channel algorithm [10] for SMAP, the L-band microwave emission of the biosphere (L-MEB) (L-band Microwave Emission of the Biosphere) [11] inversion retrieval algorithm for SMOS, a two-channel algorithm [12] based on the Qp model [13] for FY3B, a look-up-table algorithm [14] for JAXA, and a land parameter retrieval model [15] for LPRM
Summary
Soil moisture (SM) is a critical indicator of the water and energy budgets at the land surface, and a key variable that links the land surface and atmosphere [1]. Microwave remote sensing has become increasingly essential and is widely used in soil moisture monitoring. This approach allows both daytime and nighttime observations under all weather conditions and can effectively penetrate vegetation and observe the underlying surfaces. The most widely used operational passive microwave remotely sensed SM products originate from SMAP, SMOS, FY3B, and AMSR2, with AMSR2 mainly including the Japan Aerospace Exploration Agency (JAXA) and land parameter retrieval model (LPRM) products. The corresponding SM-retrieval algorithms of these five SM products include the single-channel algorithm [10] for SMAP, the L-MEB (L-band Microwave Emission of the Biosphere) [11] inversion retrieval algorithm for SMOS, a two-channel algorithm [12] based on the Qp model [13] for FY3B, a look-up-table algorithm [14] for JAXA, and a land parameter retrieval model [15] for LPRM. A new L-band (1.41 GHz) SM product (2010 to present) is provided by SMOS-IC
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