Impacts of Land Cover and Seasonal Variation on Maximum Air Temperature Estimation Using MODIS Imagery
文献类型：期刊论文
| 作者 | Cai, Yulin1,2,3; Chen, Gang3; Wang, Yali1; Yang, Li1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2017-03-01 |
| 卷号 | 9期号:3页码:14 |
| 关键词 | maximum surface air temperature land surface temperature statistical modeling MODIS |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs9030233 |
| 通讯作者 | Cai, Yulin(caiyl@sdust.edu.cn) |
| 英文摘要 | Daily 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. |
| WOS关键词 | POYANG LAKE BASIN ; SURFACE-TEMPERATURE ; LST DATA ; TEMPORAL VARIABILITY ; YANGTZE-RIVER ; HEAT-ISLAND ; CHINA ; MINIMUM ; PRODUCTS ; PRECIPITATION |
| 资助项目 | Geomatics 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 |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000398720100047 |
| 出版者 | MDPI AG |
| 资助机构 | Geomatics 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 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/64623]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Cai, Yulin |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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