Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2018.07.010 |
A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data | |
Li, Tianyu; Meng, Qingmin | |
2018-11-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING |
ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2018 |
卷号 | 179页码:63-71 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Land surface temperature (LST) retrieval from satellite imagery is one of the most practical ways to consistently monitor urban thermal environment. Given the heterogeneous nature of urban landscape, an implicit assumption should be considered in remotely sensed LST determinations that a mixed urban land cover aggregation is the combination of its constituent components. Currently, the common LST retrieval method which utilize emissivity measures estimated by NDVI threshold method (NDVITHM), including mono window (MW), single channel (SC), and split window algorithms (SW), does not take into account heterogeneity of pixels. While in this study, a new approach, the mixture analysis of emissivity (MAoE), is proposed to calculate temperature by estimating pixel emissivity from mixed land cover classes. We conduct a comparison of six approaches by the combinations of three LST retrieval algorithms with NDVITHM and MAoE respectively. The differences among strategies are characterized and analyzed by comparing LST estimates from Landsat 8 thermal images. The LST gradients derived from transect analysis are found consistently similar for combinations of two LST algorithms (MW and SC) and the two emissivity estimation algorithms (MAoE and NDVITHM). LSTs derived from SW algorithms using band 10 have the highest mean values, while the SC algorithms have moderate mean values and the MW algorithms have the lowest values. Standard deviations of estimated LST from MAoE are smaller compared with NDVITHM methods, SC retrieval algorithm with MAoE has the smallest standard deviation, and NDVITHM temperature estimation could be more impacted by different land use land cover types. |
英文关键词 | Urban land surface Heterogeneous landscape Mixture analysis of emissivity Landsat 8 imagery |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000444927200006 |
WOS关键词 | SPLIT WINDOW ALGORITHM ; INFRARED-SENSOR DATA ; HEAT-ISLAND ; SATELLITE DATA ; IMPERVIOUS SURFACE ; IMAGERY ; CLASSIFICATION ; TIRS ; SPACE ; CITY |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/24792 |
专题 | 资源环境科学 |
作者单位 | Mississippi State Univ, Dept Geosci, Mississippi State, MS 39762 USA |
推荐引用方式 GB/T 7714 | Li, Tianyu,Meng, Qingmin. A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data[J]. LANDSCAPE AND URBAN PLANNING,2018,179:63-71. |
APA | Li, Tianyu,&Meng, Qingmin.(2018).A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data.LANDSCAPE AND URBAN PLANNING,179,63-71. |
MLA | Li, Tianyu,et al."A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data".LANDSCAPE AND URBAN PLANNING 179(2018):63-71. |
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