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Anthropogenic impervious surface area (AISA) expansion with its impact on temperature is current research trend. Majority of earlier research focused on land surface temperature (LST) versus AISA relationship in specific year independently. Therefore, present research is a novel attempt to study ΔLST–ΔAISA correlation during 2012–2017 in core urban, semi urban, and rural region of Faridabad. Results demonstrate applicability of object-based image classification (OBIC) over pixel-based image classification (PBIC) to detect AISA with higher accuracy (OBIC: 2012—94%, 2017—93%, PBIC: 2012—79%, 2017—84%). Degree of non-linearity of LST-AISA relationship increases from urban to rural region, evident from the coefficient of AISA% (0.03-core urban, 0.07-semi-urban, 0.08-rural). Absolute temperature is low in land with shallow ground water depth up to 10 m. Higher percentage of land with shallow water depth causes significant increase in (ΔLST/ΔAISA) in rural region. Consequently, authors suggest avoiding acquisition of land with water table depth 3–10 m for future impervious surface development.

Pixel-based vs. object-based anthropogenic impervious surface detection: driver for urban-rural thermal disparity in Faridabad, Haryana, India