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Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery
Cai, Yulin1,2,3; Chen, Gang3; Wang, Yali1; Yang, Li1
2017-03-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:3Pages:14
Corresponding AuthorCai, Yulin(caiyl@sdust.edu.cn)
AbstractDaily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.
Keywordmaximum surface air temperature land surface temperature statistical modeling MODIS
DOI10.3390/rs9030233
WOS KeywordPOYANG LAKE BASIN ; SURFACE-TEMPERATURE ; LST DATA ; TEMPORAL VARIABILITY ; YANGTZE-RIVER ; HEAT-ISLAND ; CHINA ; MINIMUM ; PRODUCTS ; PRECIPITATION
Indexed BySCI
Language英语
Funding ProjectGeomatics College of Shandong University of Science and Technology ; National Natural Science Foundation of China[41471331] ; National Natural Science Foundation of China[41601408] ; Shandong Provincial Education Association for International Exchanges ; North Carolina Space Grant ; University of North Carolina at Charlotte
Funding OrganizationGeomatics College of Shandong University of Science and Technology ; National Natural Science Foundation of China ; Shandong Provincial Education Association for International Exchanges ; North Carolina Space Grant ; University of North Carolina at Charlotte
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000398720100047
PublisherMDPI AG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/64624
Collection中国科学院地理科学与资源研究所
Corresponding AuthorCai, Yulin
Affiliation1.Shandong Univ Sci & Technol, Geomat Coll, Qingdao 266590, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ N Carolina, Dept Geog & Earth Sci, LRSEC, Charlotte, NC 28223 USA
Recommended Citation
GB/T 7714
Cai, Yulin,Chen, Gang,Wang, Yali,et al. Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery[J]. REMOTE SENSING,2017,9(3):14.
APA Cai, Yulin,Chen, Gang,Wang, Yali,&Yang, Li.(2017).Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery.REMOTE SENSING,9(3),14.
MLA Cai, Yulin,et al."Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery".REMOTE SENSING 9.3(2017):14.
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