A MACHINE LEARNING METHOD TO CORRECT THE TERRAIN EFFECT ON LAND SURFACE TEMPERATURE IN MOUNTAINOUS AREAS
Wei Zhao1; Fengping Wen1,2; Ainong Li1
2018
会议日期2018
会议地点Valencia, SPAIN
关键词Land surface temperature terrain effect random forest MODIS
页码2539-2542
国家SPAIN
英文摘要In mountainous areas, land surface temperature (LST) shows significant terrain effect, which can be directly reflected by the spatial distribution associated with the change of topographic factors (elevation, slope, and aspect). By the way, the terrain effect diminishes the impacts from the differences in surface water and heat fluxes, and influences their comparison or estimation over complex terrain. In this study, a practical way to reduce the terrain effect is proposed based on the random forest method with datasets from MODIS products, which is used to build a LST prediction model instead of the previous model developed based on some numerical model or empirical method. The results indicates that the constructed LST model shows a good performance in predicting LST with the R-2 of 0.93 and the RMSE lower than 2.0 K for four selected days. Corrected LST maps are compared with the original LST map, which presents a preliminary correction results with an obvious correction on pixels with significant terrain effect.
源文献作者IEEE
产权排序1
会议录IEEE International Symposium on Geoscience and Remote Sensing IGARSS
语种英语
ISSN号2153-6996
ISBN号978-1-5386-7150-4
WOS记录号WOS:000451039802163
内容类型会议论文
源URL[http://ir.imde.ac.cn/handle/131551/24498]  
专题成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者Wei Zhao
作者单位1.Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, 610041, China;
2.University of Chinese Academy of Sciences, Beijing, 10049, China
推荐引用方式
GB/T 7714
Wei Zhao,Fengping Wen,Ainong Li. A MACHINE LEARNING METHOD TO CORRECT THE TERRAIN EFFECT ON LAND SURFACE TEMPERATURE IN MOUNTAINOUS AREAS[C]. 见:. Valencia, SPAIN. 2018.
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