Comparative Assessment of Vegetation Indices in Downscaling of MODIS Satellite Land Surface Temperature
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ORIGINAL PAPER
Comparative Assessment of Vegetation Indices in Downscaling of MODIS Satellite Land Surface Temperature Kul Vaibhav Sharma 1
&
Sumit Khandelwal 1 & Nivedita Kaul 1
Received: 26 December 2019 / Revised: 19 May 2020 / Accepted: 24 August 2020 # Springer Nature Switzerland AG 2020
Abstract In thermal remote sensing, the freely available land surface temperature (LST) at high-resolution data is essential. The present study aims to downscale the low-resolution (1000 m) MODIS satellite’s LST data. LST downscaling technique was developed using the statistical relationship between Earth’s surface vegetation indices (VI) and LST. The MODIS satellite’s three fundamental VI [soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and fractional vegetation cover (fc)] were taken as supporting regression variables, in the case study of Jaipur city, India. The NDVI was showing the highest correlation coefficient (R2) of 0.72, 0.62, and 0.82 and outperformed SAVI (0.57, 0.39, 0.65) and fc (0.48, 0.43, 0.69), in winter, summer, and monsoon season, respectively. The (15 ≤ CV ≤ 25%) sampled data has shown much higher R2 (0.74, 0.63, and 0.85) compared with full data (0.58, 0.39, and 0.60), in winter, summer, and monsoon season, respectively. The downscaled LST values were validated by surface temperature recorded by thermal data loggers. The physical regression-based downscaling model was able to predict LST accurately up to 200-m resolution, without significant errors (< 1 °C). The downscaled finer resolution LST can be used for applications such as fire detection, thermal comfort monitoring, soil moisture mapping, and detection/visualization of urban centers. Keywords Downscaling . Land surface temperature . MODIS . NDVI . SAVI . Regression
1 Introduction All substances of the Earth’s floor having a temperature higher than − 273 °C or 0 K (absolute zero) emit radiation after absorbing the energy from the sun by the random movement of particles [14]. The emitted energy passes from the ecosystem and recorded via the thermal infrared (TIR) sensors and gets converted into a digital number (DN) value [18]. The expertise of emissivity identification was crucial to retrieve the land surface temperature (LST) from satellite imagery [37]. The emissivity has been a complex venture to fix due to
* Kul Vaibhav Sharma [email protected] Sumit Khandelwal [email protected] Nivedita Kaul [email protected] 1
Department of Civil Engineering, MNIT Jaipur, Rajasthan 302017, India
heterogeneity of ground and spectral variation of the Earth’s surface material [36]. The land surface emissivity (aside from the ocean) can substantially range with flora [6], soil moisture [22], surface roughness [28], and viewing angle [57]. In classification-based technique, land use/land cover (LU-LC) information was retrieved from satellite image and assigned an emissivity outlay to each LU-LC class [56]. In urban areas, the emissivity value estimation for each LU-LC classes was more problematic due to overlappin
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