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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #347104

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

item KIM, H. - University Of South Carolina
item PARINUSSA, R.M - Collaborator
item KONINGS, A. - Stanford University
item WAGNER, W. - Vienna University Of Technology
item Cosh, Michael
item LAKSHMI, V. - University Of South Carolina
item ZOHAIB, M. - Sung Kyun Kwan University
item CHOI, M. - Sung Kyun Kwan University

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2018
Publication Date: 1/31/2018
Citation: Kim, H., Parinussa, R., Konings, A., Wagner, W., Cosh, M.H., Lakshmi, V., Zohaib, M., Choi, M. 2018. Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products. Remote Sensing of Environment. 204:260-275.

Interpretive Summary: There are a variety of soil moisture remote sensing platforms which are regularly collecting surface soil moisture information around the world. Each product is based on different algorithms with different base assumptions; however, much is learned by comparing these products to each other and it is possible to analyze the variability of the estimates. Error patterns were identified and biases based on satellite platforms were observed which will help to inform future missions and integration into modeling frameworks. This is useful for forecasters who may base forecasts on biased data.

Technical Abstract: 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) m3m-3; and the bias values were (-0.0460, 0.0010, and 0.0418) m3m-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 m3m-3). Overall, SMAP showed a dry bias (-0.0460 m3m-3) and AMSR2 had a wet bias (0.0418 m3m-3); while ASCAT showed the least bias (0.0010 m3m-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.