气候变化领域集成服务门户(CCPortal): A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

CCPortal

DOI	10.1016/j.rse.2020.111864
	A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP
	Sadri S.; Pan M.; Wada Y.; Vergopolan N.; Sheffield J.; Famiglietti J.S.; Kerr Y.; Wood E.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	Soil Moisture (SM) is a direct measure of agricultural drought. While there are several global SM indices, none of them directly use SM observations in a near-real-time capacity and as an operational tool. This paper presents a near-real-time global SM index monitor based on integrated SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) remote sensing data. We make use of the short period (2015–2018) of SMAP datasets in combination with two approaches—Cumulative Distribution Function Mapping (CDFM) and Bayesian conditional process—and integrate them with SMOS data in a way that SMOS data is consistent with SMAP. The integrated SMOS and SMAP (SMOS/SMAP) has an increased global revisit frequency and a period of record from 2010 to the present. A four-parameter Beta distribution was fitted to the SMOS/SMAP dataset for each calendar month of each grid cell at ~36 km resolution for the period from 2010 to 2018. We used an asymptotic method that guarantees the values of the bounding parameters of the Beta distribution will envelop both the smallest and largest observed values. The Kolmogorov-Smirnov (KS) test showed that more grids globally will pass if the integrated dataset is from the Bayesian conditional approach. A daily global SM index map is generated and posted online based on translating each grid's integrated SM value for that day to a corresponding probability percentile relevant to the particular calendar month from 2010 to 2018. For validation, we use the Canadian Prairies Ecozone (CPE). We compare the integrated SM with the SMAP core validation and RISMA sites from ISMN, compare our indices with other models (VIC, ESA's CCI SM v04.4 integrated satellite data, and SPI-1), and make a two-by-two comparison of candidate indices using heat maps and summary CDF statistics. Furthermore, we visually compare our global SM-based index maps with those produced by other organizations. Our Global SM Index Monitor (GSMIM) performed, in many tests, similarly to the CCI's product SM index but with the advantage of being a near-real-time tool, which has applications for identifying evolving drought for food security conditions, insurance, policymaking, and crop planning especially for the remote parts of the globe. © 2020 Elsevier Inc.
英文关键词	Bayesian conditional process; Beta distribution; Canadian prairies; Cumulative distribution function mapping; Data integration; ESA's CCI SM; Global; Near-real-time; Remote sensing; SMAP; SMOS; Soil moisture; VIC
语种	英语
scopus关键词	Agricultural robots; Computational complexity; Distribution functions; Drought; Food supply; Maps; Moisture control; Remote sensing; Statistical tests; Agricultural drought; Beta distributions; Cumulative distribution function; Kolmogorov-Smirnov test; Operational tools; Remote sensing data; Soil Moisture and Ocean Salinity (SMOS); Soil moisture index; Soil moisture; Bayesian analysis; drought; food security; index method; integrated approach; Internet; model validation; observational method; parameterization; probability; real time; SMOS; soil moisture; Prairie Provinces
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/179284
作者单位	Princeton University, 59 Olden St, Princeton, NJ 08540, United States; International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg, A-2361, Austria; University of Southampton, University Road, Southampton, SO17 1BJ, United Kingdom; Centre d'Etudes Spatiales de la BIOsphère (CESBIO), Toulouse, 31400, France; University of Saskatchewan, Global Institute for Water SecuritySK, Canada
推荐引用方式 GB/T 7714	Sadri S.,Pan M.,Wada Y.,et al. A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP[J],2020,246.
APA	Sadri S..,Pan M..,Wada Y..,Vergopolan N..,Sheffield J..,...&Wood E..(2020).A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP.Remote Sensing of Environment,246.
MLA	Sadri S.,et al."A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP".Remote Sensing of Environment 246(2020).