气候变化领域集成服务门户(CCPortal): Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation

CCPortal

DOI	10.1016/j.rse.2020.111756
	Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation
	Dong J.; Crow W.T.; Tobin K.J.; Cosh M.H.; Bosch D.D.; Starks P.J.; Seyfried M.; Collins C.H.
发表日期	2020
ISSN	00344257
卷号	242
英文摘要	Climatology (mean seasonal cycle) errors often cause large differences between soil moisture products. However, relatively little work has been done to objectively evaluate soil moisture mean seasonal cycle (SMC) information acquired from different sources. This study evaluates surface (0–10 cm) SMCs obtained from four land surface models (LSMs), two C/X-band and two L-band remote-sensing (RS) products using 5 dense networks and 75 sparse in-situ soil moisture measurement sites located within the contiguous United States. Results show that relative to older C/X-band products derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), newer L-band products derived from the Soil Moisture Ocean and Salinity (SMOS) mission provide more accurate SMC estimates (with an average of 35% root-mean-square-error reduction). In fact, the latest SMOS INRA-CESBIO (SMOS-IC) product provides SMC intra-seasonal variability and dynamic range information that is 3–34% and 2–37% more accurate, respectively, than all four LSM-based SMCs examined here. Hence, SMC validation against SMOS-IC SMC results may improve LSMs' ability to accurately capture SMC characteristics, and the common strategy of scaling remote sensing SMC information to match LSM SMC estimates is likely sub-optimal for assimilating L-band soil moisture retrievals. Although the SMOS-IC product has made significant progresses towards retrieving absolute soil moisture values, a temporally constant dry bias is found in SMOS-IC surface SMCs over all land cover types. Addressing this bias should be a priority for future generations of SMOS retrieval algorithms. © 2020
英文关键词	Data assimilation; Error structure; Land surface model; Remote sensing; Soil moisture climatology
语种	英语
scopus关键词	Climatology; Errors; Mean square error; Remote sensing; Soil moisture; Surface measurement; Advanced microwave scanning radiometer for eos; Data assimilation; Error structures; Intra-seasonal variabilities; Land surface modeling; Microwave remote sensing; Root mean square errors; Soil moisture retrievals; Soil surveys; AMSR-E; climatology; comparative study; data assimilation; error analysis; in situ measurement; land surface; numerical model; remote sensing; SMOS; soil moisture; 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/179357
作者单位	Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD, United States; Center for Earth and Environmental Studies, Texas A&M International University, Laredo, TX, United States; Southeast Watershed Research Laboratory, USDA-ARS, Tifton, GA, United States; Grazinglands Research Laboratory, USDA-ARS, El Reno, OK, United States; Northwest Watershed Research Center, USDA-ARS, Boise, ID, United States; Southwest Watershed Research Center, USDA-ARS, Tucson, AZ, United States
推荐引用方式 GB/T 7714	Dong J.,Crow W.T.,Tobin K.J.,et al. Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation[J],2020,242.
APA	Dong J..,Crow W.T..,Tobin K.J..,Cosh M.H..,Bosch D.D..,...&Collins C.H..(2020).Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation.Remote Sensing of Environment,242.
MLA	Dong J.,et al."Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation".Remote Sensing of Environment 242(2020).