气候变化领域集成服务门户(CCPortal): Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions

CCPortal

DOI	10.1016/j.rse.2020.112052
	Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions
	Kim H.; Wigneron J.-P.; Kumar S.; Dong J.; Wagner W.; Cosh M.H.; Bosch D.D.; Collins C.H.; Starks P.J.; Seyfried M.; Lakshmi V.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in areas such as densely vegetated and irrigated regions, obtaining accurate SSM remains challenging. Before using satellite and model-based SSM estimates over these areas, we should understand the accuracy and error characteristics of various SSM products. Thus, this study aimed to compare the error characteristics of global-scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model-based data: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). We employed triple collocation analysis (TCA) and caluclated conventional error metrics from in-situ SSM measurements. We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA-based numbers in producing robust results. Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets. The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%. © 2020 Elsevier Inc.
英文关键词	AMSR2; ASCAT; ERA5; GLDAS; In-situ soil moisture; Land surface model; Satellite-based soil moisture data; SMAP; SMOS; Soil moisture; Triple collocation analysis
语种	英语
scopus关键词	Errors; Forestry; Irrigation; Meteorological instruments; Soil moisture; Surface measurement; Weather forecasting; Advanced microwave scanning radiometer; Error characteristics; European centre for medium-range weather forecasts; Land data assimilation systems; Land surface models; Soil moisture active passive (SMAP); Soil Moisture and Ocean Salinity (SMOS); Surface soil moisture; Weather satellites; ASCAT; dryland farming; error correction; estimation method; irrigation system; mixed forest; model; satellite data; SMOS; soil moisture; soil-vegetation interaction
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/179123
作者单位	The Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, United States; School of Data Science, University of Virginia, Charlottesville, VA 22904, United States; Interactions Sol Plante Atmosphère (ISPA), Unité Mixte de Recherche 1391, Institut National de la Recherche Agronomique (INRAE), CS 20032, Villenave d'Ornon CEDEX, 33882, France; Hydrological Sciences Laboratory, NASA GSFC, Greenbelt, MD 20771, United States; Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD 20705, United States; Department of Geodesy and Geoinformation, TU Wien, Vienna, 1040, Austria; Southeast Experimental Watershed Research Laboratory, USDA-ARS, Tifton, GA, United States; Southwest Watershed Research Center, USDA-ARS, Tucson, AZ, United States; Grazinglands Research Laboratory, USDA-ARS, El Reno, OK, United States; Northwest Watershed Research Center, USDA-ARS, Boise, ID, United States
推荐引用方式 GB/T 7714	Kim H.,Wigneron J.-P.,Kumar S.,et al. Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions[J],2020,251.
APA	Kim H..,Wigneron J.-P..,Kumar S..,Dong J..,Wagner W..,...&Lakshmi V..(2020).Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions.Remote Sensing of Environment,251.
MLA	Kim H.,et al."Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions".Remote Sensing of Environment 251(2020).