干旱区生态环境知识资源中心(Arid): Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

Arid

DOI	10.3389/fenvs.2021.555216
	Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data
	Ojha, Nitu; Merlin, Olivier; Suere, Christophe; Escorihuela, Maria Jose
通讯作者	Ojha, N (corresponding author), Univ Toulouse, CESBIO, CNES, CNRS,INRA,IRD,UPS, Toulouse, France.
来源期刊	FRONTIERS IN ENVIRONMENTAL SCIENCE
EISSN	2296-665X
出版年	2021
卷号	9
英文摘要	DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39-0.80) than for the Copernicus Sentinel-1-based (-0.03 to 0.69) and SMAP/S1 (0.37-0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.
英文关键词	soil moisture DISPATCH TVDI EVI SMAP Sentinel-3
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000634514600001
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	French National Research Institute for Sustainable Development
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/350293
作者单位	[Ojha, Nitu; Merlin, Olivier; Suere, Christophe] Univ Toulouse, CESBIO, CNES, CNRS,INRA,IRD,UPS, Toulouse, France; [Escorihuela, Maria Jose] IsardSAT SL, Parc Tecnol Barcelona Act, Barcelona, Spain
推荐引用方式 GB/T 7714	Ojha, Nitu,Merlin, Olivier,Suere, Christophe,et al. Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data[J]. French National Research Institute for Sustainable Development,2021,9.
APA	Ojha, Nitu,Merlin, Olivier,Suere, Christophe,&Escorihuela, Maria Jose.(2021).Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data.FRONTIERS IN ENVIRONMENTAL SCIENCE,9.
MLA	Ojha, Nitu,et al."Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data".FRONTIERS IN ENVIRONMENTAL SCIENCE 9(2021).