Land surface temperature variability across India: a remote sensing satellite perspective

Abstract

Land surface temperature (LST) plays a key role in the surface energy budget computation and land surface process studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua and Terra satellites provide comprehensive global LST estimates at a fine spatial resolution. The MODIS products were recently upgraded to Collection 6, and shown to be more accurate than its predecessor Collection 5 products. In this study, LST and its variability have been examined across India from Collection 6 of the Aqua MODIS data at 0.05°s patial resolution for the period of 2003 to 2017. All-India mean LST characteristics show distinctive features as compared to the well-documented mean characteristics of near-surface air temperature. All land cover types except permanent snow & ice, and cold desert areas exhibit bimodal peaks in seasonal variations of daytime LST. The daytime LST over the coldest and high-altitude regions of northern India shows anomalous positive linear relationship with NDVI at a monthly scale. However, monthly domain-mean daytime LST of cropland regions is largely negatively correlated with NDVI as compared to other land cover types. Results reveal that about 17% of the Indian landmass received its hottest LST during 2010 followed by 2016. Linear trend analysis for the 15-year period of mean annual LST shows a decrease in diurnal temperature range over most parts of the country due to rather rapid increase in nighttime LST than daytime LST, similar as changes in near-surface air temperature across the country.

Interannual Spatiotemporal Variations of Land Surface Temperature in China From 2003 to 2018

“…Yanru Yu, Si-Bo Duan, Zhao-Liang Li, Sheng Chang, Zefeng Xing, Pei Leng, and Maofang Gao R LST [7]- [9]. MODIS LST products have also been used for assessing the urban heat islands effect [10]- [11] and drought monitoring [12].…”

“…It contains the International Geosphere-Biosphere Programme (IGBP) global vegetation classification scheme which identifies 17 classes. In this study, the IGBP scheme was grouped into nine classes [33], and the land cover type of water was not considered. The classes of evergreen needleleaf forest, evergreen broadleaf forest, deciduous needleleaf forest, deciduous broadleaf forest, and mixed forest were categorized as forest regions in IGBP.…”

Remotely Sensed Derived Land Surface Temperature (LST) as a Proxy for Air Temperature and Thermal Comfort at a Small Geographical Scale

“…Both, UHI and SUHI phenomena have been examined in the literature at different geographical scales, including at the level of continents [38][39][40], countries [41], cities and metropolitan areas [13,42] and increasingly at micro-scales [43,44]. To illustrate, [45] examines the impacts of urban functional zones and spatial heterogeneity on LST in Beijing, China; Temporal changes in LST as a result of urban redevelopments in Lyon, France where investigated by [46]; and the impacts of different types of LULC patterns on SUHI variations in Bangkok, Thailand, where evaluated by [47].…”

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

“…The LST values are reaching their maximum between 4:00 p.m. and 5:00 p.m. local solar time, and buildings show higher magnitude of diurnal temperature variability as expected compared to trees and grass surfaces. The considerable dependence of LST variability on LC type is reported in the recent studies e.g., [9,11]. The entangled succession of operations is illustrated in the accompanying flowchart shown in Figure 3.…”

“…Thermal infrared (TIR) sensors provide LST estimates at finer spatial resolution with reasonable accuracy, but they are restricted to clear-sky conditions alone. An example of one of the most widely used TIR-based LST products is obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) derived LST estimates, and it has been successfully used for numerous applications worldwide [3,4,[8][9][10][11]. Microwave sensors provide LST estimates for all-weather conditions, but their spatial resolution is rather coarse.…”