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Modeling Urban Land Surface Temperature Using Physics-Informed Neural Networks (PINNs)
Journal article   Open access   Peer reviewed

Modeling Urban Land Surface Temperature Using Physics-Informed Neural Networks (PINNs)

Ronak Ghanbari, Marc Linderman, Hossein Arefi, Hossein Torabzadeh and Morteza Heidari Mozaffar
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, Vol.X-4/W8-2025, pp.277-283
05/29/2026
DOI: 10.5194/isprs-annals-X-4-W8-2025-277-2026
url
https://doi.org/10.5194/isprs-annals-X-4-W8-2025-277-2026View
Published (Version of record) Open Access

Abstract

A compact physics-informed neural network (PINN) is developed to (i) quantify city-scale accuracy of 30 m urban land surface temperature (LST) maps, (ii) identify influential predictors, and (iii) contrast climate-dependent patterns between New York City (NYC) (humid to sub-humid) and Austin, Texas (humid subtropical). Inputs combine selected Landsat-8 spectral indices, a digital elevation model, and meteorological covariates. LST targets are retrieved from Landsat-8 thermal band 10 (single-channel), quality-screened, and resampled to 30 m for May–September 2023. The loss combines data mean squared error term with a lightweight temporal smoothness prior implemented as a finite-difference term (Δ ⁄Δ ) on same-pixel pairs to reflect heat storage behaviour and discourage unrealistically rapid day to day changes. On the study pixels (in-sample), performance reaches R² = 0.88 (RMSE = 1.2 °C) in NYC and R² = 0.91 (RMSE = 0.9 °C) in Austin; errors are approximately Gaussian with minimal bias. Feature patterns differ by climate: vegetation-related signals dominate cooling in NYC, whereas shortwave-radiation and impervious-surface proxies (e.g., NDBI/NDISI) are strongest in Austin. These findings show that a shallow PINN with a minimal temporal constraint yields accurate, interpretable LST maps suitable for urban-heat-island assessment and climate-sensitive heat-mitigation planning.
 Physics Remote Sensing Climate Digital Elevation Models Errors Heat Heat storage Land surface temperature Landsat Neural networks Pixels Short wave radiation Smoothness Urban Heat Island (UHI) Physics-Informed Neural Network (PINN) Spectral Indices Meteorological Variables

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