气候变化领域集成服务门户(CCPortal): Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

CCPortal

DOI	10.1016/j.rse.2020.111740
	Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates
	Vergopolan N.; Chaney N.W.; Beck H.E.; Pan M.; Sheffield J.; Chan S.; Wood E.F.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. © 2020 Elsevier Inc.
英文关键词	Brightness temperature; Data merging; Field-scale; Hyper-resolution; Land surface modeling; SMAP; Soil moisture
语种	英语
scopus关键词	Decision making; Information management; Land surface temperature; Luminance; Merging; Microwave sensors; Radiative transfer; Satellites; Soil moisture; Surface measurement; Water resources; Brightness temperatures; Data merging; Field scale; Hyper-resolution; Land surface modeling; SMAP; Soil surveys; brightness temperature; data acquisition; estimation method; modeling; satellite mission; soil moisture; watershed; 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/179363
作者单位	Department of Civil and Environmental Engineering, Princeton University, United States; Department of Civil and Environmental Engineering, Duke University, United States; School of Geography and Environmental Science, Southampton University, United Kingdom; NASA Jet Propulsion Laboratory, California Institute of Technology, United States
推荐引用方式 GB/T 7714	Vergopolan N.,Chaney N.W.,Beck H.E.,et al. Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates[J],2020,242.
APA	Vergopolan N..,Chaney N.W..,Beck H.E..,Pan M..,Sheffield J..,...&Wood E.F..(2020).Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates.Remote Sensing of Environment,242.
MLA	Vergopolan N.,et al."Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates".Remote Sensing of Environment 242(2020).