Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

被引:174
作者
Kim, Hyunglok [1 ]
Parinussa, Robert [2 ]
Konings, Alexandra G. [3 ]
Wagner, Wolfgang [4 ]
Cosh, Michael H. [5 ]
Lakshmi, Venkat [1 ]
Zohaib, Muhammad [6 ]
Choi, Minha [6 ]
机构
[1] Univ South Carolina, Sch Earth Ocean & Environm, Columbia, SC 29208 USA
[2] VanderSat, Wilhelminastr 43a, NL-2011 VK Haarlem, Netherlands
[3] Stanford Univ, Dept Earth Syst Sci, Stanford, CA 94305 USA
[4] Vienna Univ Technol, Dept Geodesy & Geoinformat, Vienna, Austria
[5] USDA ARS, Hydrol & Remote Sensing Lab, Beltsville, MD 21032 USA
[6] Sungkyunkwan Univ, Grad Sch Water Resources, Dept Water Resources, Suwon, South Korea
基金
新加坡国家研究基金会;
关键词
Remotely sensed soil moisture retrievals; SMAP; ASCAT; AMSR2; Inter-comparison; Triple collocation error estimator; Combining datasets; VEGETATION OPTICAL DEPTH; IN-SITU OBSERVATIONS; NEAR-SURFACE; ERROR CHARACTERIZATION; TEMPORAL STABILITY; ERS SCATTEROMETER; SATELLITE; SMOS; VALIDATION; MODEL;
D O I
10.1016/j.rse.2017.10.026
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products. The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m(3) m(-3); and the bias values were (-0.0460, 0.0010, and 0.0418) m(3) m(-3) for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438 m(3) m(-3)). Overall, SMAP showed a dry bias (-0.0460 m(3) m(-3)) and AMSR2 had a wet bias (0.0418 m(3) m(-3)); while ASCAT showed the least bias (0.0010 m(3) m(-3)) among all the products. Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD < 0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10 < VOD < 0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD > 0.40) ASCAT showed comparatively better performance than did the other products. Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM values. When the satellite products were combined, R-values of the combined products were improved or degraded depending on the VOD ranges produced, when compared with the results from the original products alone. The results of this study provide an overview of SMAP, ASCAT, and AMSR2 reliability and the performance of their combined products on a global scale. This study is the first to show the advantages of the recently available SMAP dataset for effective merging of different satellite products and of their application to various hydro meteorological problems.
引用
收藏
页码:260 / 275
页数:16
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