气候变化领域集成服务门户(CCPortal): Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach

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DOI	10.1016/j.jag.2020.102113
	Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach
	Ambrosone M.; Matese A.; Di Gennaro S.F.; Gioli B.; Tudoroiu M.; Genesio L.; Miglietta F.; Baronti S.; Maienza A.; Ungaro F.; Toscano P.
发表日期	2020
ISSN	15698432
卷号	89
英文摘要	Surface soil water content plays an important role in driving the exchange of latent and sensible heat between the atmosphere and land surface through transpiration and evaporation processes, regulating key physiological processes affecting plants growth. Given the high impact of water scarcity on yields, and of irrigated agriculture on the overall withdrawal rate of freshwater, it is important to define models that help to improve water resources management for agricultural purposes, and to optimize rainfed crop yield. Recent advances in satellite-based remote sensing have led to valuable solutions to estimate soil water content based on microwave or optical/thermal-infrared data. This study aims at improving soil water content estimation at high spatial and temporal resolution, by means of the Optical Trapezoid Model (OPTRAM) driven by Copernicus Sentinel-2 data. Two different model variations were considered, based on linear and nonlinear parameters constraints, and validated against in situ soil water content measurements made with time domain reflectometry (TDR) on irrigated maize in central Italy and on rainfed maize and pasture in northern Italy. For the first site the non-linear model shows a better correlation between measured and estimated soil water content values (r = 0.80) compared to the linear model (r = 0.73). In both cases the modeled soil moisture tends to overestimate the measured values at medium to high water content level, while both models underestimate soil moisture at low water content level. Estimated versus measured normalized surface soil water for rainfed pasture plots from nonlinear OPTRAM parametrized based on irrigated maize parameterization (SIM1), and site-specific parametrization for rainfed pasture (SIM2), indicate that both models (SIM1 and SIM2) are comparable for rotational grazing pasture (RMSEsim1 = 0.0581 vs. RMSEsim2 = 0.0485 cm3 cm-3) and the continuous grazing pasture (RMSEsim1 = 0.0485 vs. RMSEsim2 = 0.0602 cm3 cm-3), while for the rainfed maize plots SIM1 shows lower RMSE (average for all plots RMSE = 0.0542 cm3 cm-3) compared to the site-specific calibration model (SIM2 – average for all plots RMSE = 0.0645 cm3 cm-3). Finally, OPTRAM estimations are close to in situ measurement values while Surface Soil Moisture at 1 km (SSM1 km) tends to underestimate the measurements during maize crop growing season. Soil moisture retrieval from high-resolution Sentinel-2 optical images allows water stress conditions to be effectively mapped, supporting decision making in irrigation scheduling and other crop management. © 2020 The Authors
英文关键词	Irrigated; Maize; Pasture; Rainfed; Sentinel-2; Soil moisture
语种	英语
scopus关键词	agricultural application; calibration; estimation method; irrigation; maize; rainfed agriculture; resource scarcity; sensible heat flux; Sentinel; soil moisture; soil water; water column; water management; Italy; Zea mays
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156426
作者单位	Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, Florence, 50145, Italy; European Space Agency (ESA), ESTEC, Keplerlaan 1, Noordwijk, 2200 AG, Netherlands
推荐引用方式 GB/T 7714	Ambrosone M.,Matese A.,Di Gennaro S.F.,et al. Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach[J],2020,89.
APA	Ambrosone M..,Matese A..,Di Gennaro S.F..,Gioli B..,Tudoroiu M..,...&Toscano P..(2020).Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach.International Journal of Applied Earth Observation and Geoinformation,89.
MLA	Ambrosone M.,et al."Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach".International Journal of Applied Earth Observation and Geoinformation 89(2020).