Publications

Bala, R; Prasad, R; Yadav, VP (2019). Disaggregation of modis land surface temperature in urban areas using improved thermal sharpening techniques. ADVANCES IN SPACE RESEARCH, 64(3), 591-602.

Abstract
Applications of satellite thermal images are usually impeded by the low spatial resolution, leading to the development of various downscaling techniques. The thermal sharpening model based on the relationship between LST and Normalized Difference Vegetation Index (NDVI) was developed which shows good results in agricultural areas but may not be applicable for urban areas. Therefore, the present study focuses on determining improved downscaling techniques that shows good results in different urban regions. Hence, the performance of six different indices, namely NDVI, Enhanced Vegetation Index (EVI), Normalized Difference Built-up Index (NDBI), Urban Index (UI), Normalized Difference Soil Index (NDSI) and Normalized Difference Water Index (NDWI) were compared for thermal sharpening using Disaggregation of Radiometric Temperature (Distrad) Model over four different cities in India i.e. Bikaner, Hyderabad, Vadodara and Varanasi. LST obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors (930 m) were disaggregated to the spatial resolution of Landsat 8 Thermal Infrared Sensor (TIRS) (100 m) and compared with the Landsat LST. The performance of NDBI was found better as compared to other indices in the four cities having Root Mean Square Error (RMSE) = 1.54 K, 1.24 K, 1.10 K and 1.03 K, respectively. Further, NDBI was used for disaggregation using two robust regression techniques i.e. Least Median Square Regression (LMSR) and Bi-square regression which shows better results as compared to that of Distrad model in the four study sites. Bi-square regression method shows RMSE values of 1.30 K, 1.21 K, 0.98 K and 0.97 K, respectively for the four study sites. The LMSR and Bi-square regressions are less sensitive to outliers resulting in increased accuracy of downscaled LST. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.

DOI:
10.1016/j.asr.2019.05.004

ISSN:
0273-1177