4.7 Article

Spatiotemporal Downscaling Method of Land Surface Temperature Based on Daily Change Model of Temperature

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2022.3209012

Keywords

Land surface temperature; Spatial resolution; Data models; Satellites; Land surface; MODIS; Spatiotemporal phenomena; Diurnal temperature cycle (DTC); downscaling; FY-4A; land surface temperature (LST); MODIS

Funding

  1. General Programs of the National Natural Science Foundation of China [42071346]

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This study proposes a novel spatiotemporal fusion model of land surface temperature (LST) based on diurnal variation information (BDSTFM) to predict LST data with high temporal resolution and spatiotemporal continuity. The BDSTFM model achieves a high level of accuracy in downscaling and can obtain realistic and reliable 1-km seamless LST datasets.
Land surface temperature (LST) is one of the most crucial variables of surface energy processes. However, the trade-off between spatial and temporal resolutions of remote sensing data has greatly limited the availability of concurrently high-spatiotemporal resolution LST data for wide applications. Existing downscaling methods are easily affected by null values of LST data and effective time distribution of high-resolution LST data, resulting in large downscaling errors at sometimes. Within this context, this study proposes a novel spatiotemporal fusion model of LST based on diurnal variation information (BDSTFM) to predict LST data with a high temporal resolution and spatiotemporal continuity based on FY-4A and MODIS. Results indicated that the accuracy of the downscaling results was comparable to that of MODIS LST products. The BDSTFM model exhibited the following characteristics: use low-spatial resolution data to establish a diurnal temperature cycle (DTC) model for scale deduction, and retention of the temporal distribution characteristics of LST data; extend the observation time of high-spatial resolution data to improve the accuracy and stability of the model; add an invalid pixel reconstruction step that considers the LST spatiotemporal continuity, and can obtain a realistic and reliable 1-km seamless LST datasets at hourly intervals under clear skies. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), 4-parameter DTC model, and Random Forest model, the BDSTFM model attained a higher downscaling accuracy.

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