Applicability of Downscaling Land Surface Temperature by Using Normalized Difference Sand Index

Land surface temperature (LST) in coarse spatial resolution derived from thermal infrared satellite images has limited use in many remote sensing applications. In this study, we improve our previous approach (multiple remote-sensing index approach of random forest) to downscale LST derived from Landsat 8 and MODIS in an arid oasis - desert ecotone of Zhangye city by designing a normalized difference sand index (NDSI), by the removal of land cover datasets and by the input of SAVI, NDBI and NDWI to downscale LST. Our result demonstrates that NDSI can determine the characteristic of the desert region, and that the distribution of downscaled LST matches those of oasis-desert ecosystems. Relative to the ground observation of HiWATER, our approach also produces relatively satisfactory downscaling results at July 21 (2013), with R² and root-mean-square error of 0.99 and 1.25 K, respectively. Compared with other methods, our approach demonstrates higher accuracy and minimization of the retrieved Landsat 8 LST in the desert region. Optimal availability occurs in the vegetation and desert region. Our approach is suitable to LST downscaling in all seasons, especially in spring and summer. The model can further be applied in middle-high and middle-low spatial resolutions. The usefulness of the model is relatively satisfactory in the humid region (Nanjing city) but less accurate in the arid region.