气候变化领域集成服务门户(CCPortal): Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard

CCPortal

DOI	10.1016/j.rse.2019.111622
	Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard
	Lei F.; Crow W.T.; Kustas W.P.; Dong J.; Yang Y.; Knipper K.R.; Anderson M.C.; Gao F.; Notarnicola C.; Greifeneder F.; McKee L.M.; Alfieri J.G.; Hain C.; Dokoozlian N.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field-scale using a data assimilation strategy. © 2020 Elsevier Inc.
英文关键词	Data assimilation; Evapotranspiration; High-resolution; Irrigation management; Radar; Soil moisture; Thermal remote sensing
语种	英语
scopus关键词	Evapotranspiration; Forestry; Irrigation; Kalman filters; Moisture control; Plants (botany); Radar; Remote sensing; Soil moisture; Synthetic aperture radar; Water conservation; Water resources; Water supply; Data assimilation; Data assimilation algorithms; High resolution; Irrigation management; Non-linear relationships; Soil-vegetation-atmosphere transfer models; Surface soil moisture retrieval; Thermal remote sensing; Soil surveys; algorithm; climate conditions; data assimilation; evapotranspiration; infrared radiation; numerical model; radar altimetry; remote sensing; satellite data; soil moisture; soil-vegetation interaction; spatial analysis; spectral resolution; sustainability; synthetic aperture radar; water use efficiency; California; Central Valley [California]; United States
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/179461
作者单位	Hydrology and Remote Sensing Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705, United States; Geosystems Research Institute, Mississippi State University, Starkville, MS 39762, United States; Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy; Earth Science Office, NASA Marshall Space Flight Center, Huntsville, AL 35805, United States; Viticulture, Chemistry and Enology, E. & J. Gallo Winery, Modesto, CA 95354, United States
推荐引用方式 GB/T 7714	Lei F.,Crow W.T.,Kustas W.P.,et al. Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard[J],2020,239.
APA	Lei F..,Crow W.T..,Kustas W.P..,Dong J..,Yang Y..,...&Dokoozlian N..(2020).Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard.Remote Sensing of Environment,239.
MLA	Lei F.,et al."Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard".Remote Sensing of Environment 239(2020).