气候变化领域集成服务门户(CCPortal): Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability

CCPortal

DOI	10.1016/j.rse.2020.112126
	Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability
	Zhang R.; Kim S.; Sharma A.; Lakshmi V.
发表日期	2021
ISSN	00344257
卷号	252
英文摘要	This study evaluates the relative strengths of three remotely sensed soil moisture (SM) products to capture temporal variability at a global scale, the products being the Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity INRA-CESBIO (SMOS-IC) and Advanced Scatterometer (ASCAT). For this, the conventional reference-based Pearson correlation (R) and a statistical technique called Triple Collocation (TC)-based R are calculated. In addition, two alternatives for linear combination of the three data sources for maximizing R against the truth are evaluated, the first using a reference product (i.e. assumed truth) and the second based on TC where three data sources are combined without the need for an underlying reference or assumed truth. The estimated optimal combination weights represent quantitative contributions of the three products in forming the new combined product having the maximized R. Two reanalysis products: the European Centre for Medium-Range Forecast (ECMWF) Interim product (ERA-Interim) and the Modern-Era Retrospective Analysis for Research and Application Land version 2 reanalysis product (MERRA2), are used as the references as well as data alternatives to calculate the conventional reference-based R and the TC-based R combinations. Both types of R, and their derived optimal weights are then compared globally and analyzed under various climate, land cover, and vegetation conditions. Despite the differences between the conventional R and the TC-based R, both metrics displayed consistent spatial distributions and can reflect the temporal variations of each studied dataset without considerable impact from adopted references. All products had difficulty in retrieving SM over arid and polar regions while exhibiting good performance in areas such as South America and Australia. While ASCAT presented higher R values over tropical, savannas, and the vegetation water content interval of 2–5 kg/m2, SMAP and SMOS-IC displayed overall comparable and continually high temporal performances across almost all conditions. In the case of the derived optimal weights, a global complementarity of the areas was observed where each satellite-based observation product showed its respective advantage in capturing SM variations in different geographic areas. © 2020 Elsevier Inc.
英文关键词	ASCAT; Correlation coefficients; Optimal weights; SMAP; SMOS-IC; Soil moisture
语种	英语
scopus关键词	Correlation methods; Meteorological instruments; Soil moisture; Timing circuits; Vegetation; Optimal combination weight; Remotely sensed soil moisture; Research and application; Retrospective analysis; Satellite based observations; Soil moisture active passive (SMAP); Soil moisture ocean salinities; Vegetation water content; Integrated circuits; ASCAT; complementarity; correlation; optimization; remote sensing; satellite data; savanna; SMOS; soil moisture; spatial distribution; temporal variation; vegetation dynamics; water content; Australia; South America
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179078
作者单位	School of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, United States; School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia
推荐引用方式 GB/T 7714	Zhang R.,Kim S.,Sharma A.,et al. Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability[J],2021,252.
APA	Zhang R.,Kim S.,Sharma A.,&Lakshmi V..(2021).Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability.Remote Sensing of Environment,252.
MLA	Zhang R.,et al."Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability".Remote Sensing of Environment 252(2021).