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An optimal sampling method for multi-temporal land surface temperature validation over heterogeneous surfaces

文献类型：期刊论文

作者Li, Jing1,2; Wu, Hua1,2; Li, Zhao-Liang3
刊名ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
出版日期2020-11-01
卷号169页码:29-43
关键词Optimal sampling Diurnal land surface temperature Validation Sampling representativeness Heterogeneous surfaces
ISSN号0924-2716
DOI10.1016/j.isprsjprs.2020.08.024
通讯作者Wu, Hua(wuhua@igsnrr.ac.cn) ; Li, Zhao-Liang(lizhaoliang@caas.cn)
英文摘要The development of ground-based sampling strategies is vital for validating medium or coarse-resolution satellite-derived land surface temperature (LST) products. Conventional LST sampling at the satellite pixel scale has been limited to the homogeneous surfaces. In this study, an optimal sampling strategy called spatial and diurnal temperature cycle-constrained sampling (SDCS) is proposed to extend the feasibility of LST validation over heterogeneous surfaces with dramatic diurnal LST changes. SDCS integrates a priori information including land cover, diurnal LSTs data, and spatial distribution characteristics of samples to improve the representativeness of multi-temporal measurements over heterogeneous surfaces. SDCS was applied to four varied study areas using simulated satellite data and was compared with four existing methods including random sampling, systematic sampling, land cover-based stratified sampling, and conditioned Latin hypercube (CLH) sampling. Results showed that SDCS could significantly improve the representativeness of samples with a limited sample size. In homogeneous surfaces with an LST standard deviation (SD) of less than 2 K, the root mean square error (RMSE) of diurnal LSTs estimated by SDCS was less than 0.3 K when using 0.20% of the total pixels. In moderate heterogeneous surfaces (LST SD less than 5 K), 0.32% of the total pixels were required to achieve RMSE less than 0.5 K. In extremely heterogeneous surfaces (LST SD > 6 K), 0.96% of the pixels were needed to achieve the same accuracy. Further, the representativeness of the samples selected by SDCS was stable in diurnal space with uncertainties of LST bias less than 0.27 K. Moreover, the samples exhibited a dispersed spatial distribution with a nearest neighbor index of 1.27-1.54. SDCS can generalize to various regions and dates and can be employed in field campaigns for diurnal LST validation over heterogeneous surfaces.
WOS关键词GROUND MEASUREMENTS ; SATELLITE DATA ; ARID REGION ; RESOLUTION ; PRODUCTS ; RADIOMETER ; CYCLES ; ASTER
资助项目National Key R&D Program of China[2018YFB0504800] ; National Natural Science Foundation of China[41531174] ; National Natural Science Foundation of China[41871267] ; China Scholarship Council (CSC)
WOS研究方向Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000584231200003
出版者ELSEVIER
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; China Scholarship Council (CSC)
源URL[http://ir.igsnrr.ac.cn/handle/311030/156473]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Hua; Li, Zhao-Liang
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing, Beijing 100081, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jing,Wu, Hua,Li, Zhao-Liang. An optimal sampling method for multi-temporal land surface temperature validation over heterogeneous surfaces[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2020,169:29-43.
APA Li, Jing,Wu, Hua,&Li, Zhao-Liang.(2020).An optimal sampling method for multi-temporal land surface temperature validation over heterogeneous surfaces.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,169,29-43.
MLA Li, Jing,et al."An optimal sampling method for multi-temporal land surface temperature validation over heterogeneous surfaces".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 169(2020):29-43.

入库方式： OAI收割

来源：地理科学与资源研究所

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