The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

doi:10.1080/01431161.2019.1677969

	The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region
	Liu, Kai 2,3; Wang, Shudong 3; Li, Xueke 1; Li, Yao 4; Zhang, Bo 1; Zhai, Ruiting 1
刊名	INTERNATIONAL JOURNAL OF REMOTE SENSING
	2019-10-20
页码	20
ISSN号	0143-1161
DOI	10.1080/01431161.2019.1677969
通讯作者	Wang, Shudong(wangsd@radi.ac.cn)
英文摘要	Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20?K and 0.16?K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.
资助项目	Natural Science Foundation of China[41671362]
WOS关键词	LAND-SURFACE TEMPERATURE ; URBAN HEAT-ISLAND ; SOIL-MOISTURE ; RANDOM FOREST ; ENERGY FLUXES ; RESOLUTION ; SATELLITE ; EVAPOTRANSPIRATION ; FUSION ; LST
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000490969100001
资助机构	Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/129745]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Shudong
作者单位	1.Univ Connecticut, Dept Geog, Mansfield, CT USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 4.Texas A&M Univ, Zachry Dept Civil & Environm Engn, College Stn, TX USA
推荐引用方式 GB/T 7714	Liu, Kai,Wang, Shudong,Li, Xueke,et al. The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2019:20.
APA	Liu, Kai,Wang, Shudong,Li, Xueke,Li, Yao,Zhang, Bo,&Zhai, Ruiting.(2019).The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region.INTERNATIONAL JOURNAL OF REMOTE SENSING,20.
MLA	Liu, Kai,et al."The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region".INTERNATIONAL JOURNAL OF REMOTE SENSING (2019):20.