Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature
文献类型：期刊论文
| 作者 | Gao, Lun1; Zhan, Wenfeng1,2; Huang, Fan1; Quan, Jinling3; Lu, Xiaoman1; Wang, Fei1; Ju, Weimin1; Zhou, Ji4,5 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2017 |
| 卷号 | 55期号:1页码:477-490 |
| 关键词 | Disaggregation global regression strategy (GWS) land surface temperature (LST) local regression strategy (LWS) moving-window size (MWS) thermal remote sensing |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2016.2608987 |
| 通讯作者 | Zhan, Wenfeng(zhanwenfeng@nju.edu.cn) |
| 英文摘要 | The past decade has witnessed the disaggregation of remotely sensed land surface temperature (DLST), which aims for the generation of high temporal and spatial resolution land surface temperature (LST) and which has steadily evolved into a relatively independent subfield of thermal remote sensing. Limited by Tobler's first law of geography, DLST methods require a regression between LSTs and scaling factors using image pixels within a globalized or a localized regression window. Recommendations regarding the selection of the regression window have been provided, but they are mainly subjective and based on highly specific examples. In this context, 100 DLST samples with diversified land cover types and climates were employed to assess the global window strategy (GWS) and the local window strategy (LWS). To optimize disaggregation accuracy and computational complexity, the assessments show that the optimal moving-window size (MWS) for the LWS can be estimated by the resolution ratio between pre- and postdisaggregated LSTs. To identify the better strategy between the GWS and the LWS, an indirect criterion based on aggregation-disaggregation (ICAD) was formulated, which determines the better strategy from medium to high resolution according to the associated performances from low to medium resolution. Validations demonstrate that the accuracy predicted by the ICAD achieves 72%, and in cases in which predictions are incorrect, the performances of the GWS and the LWS are similar. Further evidences indicate that the use of historical high-resolution LSTs improves the LWS by using a locally varying MWS. These findings are able to guide researchers in choosing the most suitable regression window for any particular DLST. |
| WOS关键词 | URBAN HEAT-ISLAND ; THERMAL-INFRARED DATA ; WATER TEMPERATURE ; SOIL-MOISTURE ; ASTER DATA ; IMAGERY ; ALGORITHM ; MODIS ; CHINA ; EVAPOTRANSPIRATION |
| 资助项目 | National 863 Plan Grant[2013AA122801] ; National Natural Science Foundation of China[41301360] ; National Natural Science Foundation of China[41671420] ; Natural Science Foundation of Jiangsu Province[BK20130566] ; Natural Science Foundation of Jiangsu Province[BK20130568] ; DengFeng Program-B of Nanjing University |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000391527900038 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National 863 Plan Grant ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; DengFeng Program-B of Nanjing University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/65052]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhan, Wenfeng |
| 作者单位 | 1.Nanjing Univ, Int Inst Earth Syst Sci, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Univ Elect Sci & Technol, Sch Resources & Environm, Chengdu 611731, Peoples R China 5.Univ Elect Sci & Technol, Informat Geosci Res Ctr, Chengdu 611731, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Lun,Zhan, Wenfeng,Huang, Fan,et al. Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2017,55(1):477-490. |
| APA | Gao, Lun.,Zhan, Wenfeng.,Huang, Fan.,Quan, Jinling.,Lu, Xiaoman.,...&Zhou, Ji.(2017).Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,55(1),477-490. |
| MLA | Gao, Lun,et al."Localization or Globalization? Determination of the Optimal Regression Window for Disaggregation of Land Surface Temperature".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 55.1(2017):477-490. |
入库方式： OAI收割
来源：地理科学与资源研究所
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