A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
This chapter introduces a new fusion algorithm for thermal infrared data to predict daily land surface temperature (LST) at 120‐m resolution by blending Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, i.e. Spatiotemporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). The implementation of SADFAT requires preprocessing of Landsat and MODIS data, selection of spectrally similar pixels, and computation of the conversion coefficient. A long‐term LST data set of high quality can benefit analyses of impact of urbanization on thermal characteristics. Therefore, the chapter introduces an algorithm that allows reconstructing historical LST measurements at daily interval based solely on irregularly spaced Landsat imagery. Instead of blending data among different satellite sensors, this algorithm takes advantage of unevenly distributed time series Landsat imagery and goes through the modules of data filter, temporal segmentation, periodic and trend modeling, and Gaussian (DELTA).
This chapter introduces a new fusion algorithm for thermal infrared data to predict daily land surface temperature (LST) at 120‐m resolution by blending Landsat Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) LST data, i.e. Spatiotemporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT). The implementation of SADFAT requires preprocessing of Landsat and MODIS data, selection of spectrally similar pixels, and computation of the conversion coefficient. A long‐term LST data set of high quality can benefit analyses of impact of urbanization on thermal characteristics. Therefore, the chapter introduces an algorithm that allows reconstructing historical LST measurements at daily interval based solely on irregularly spaced Landsat imagery. Instead of blending data among different satellite sensors, this algorithm takes advantage of unevenly distributed time series Landsat imagery and goes through the modules of data filter, temporal segmentation, periodic and trend modeling, and Gaussian (DELTA).
Land Surface Temperature Data Generation
Weng, Qihao (author)
2019-11-01
37 pages
Article/Chapter (Book)
Electronic Resource
English
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