Publications

Hu, PH; Wang, AH; Yang, YB; Pan, X; Hu, XJD; Chen, YC; Kong, XC; Bao, Y; Meng, XJ; Dai, Y (2022). Spatiotemporal Downscaling Method of Land Surface Temperature Based on Daily Change Model of Temperature. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 15, 8360-8377.

Abstract
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.

DOI:
10.1109/JSTARS.2022.3209012

ISSN:
2151-1535