Machine learning prediction of future land surface temperature from SAR optical fusion under urban expansion in Changsha, China
Year: 2025
Type: article
Abstract: In the context of growing urbanization and persistent cloud contamination in optical remote sensing, reliable large-scale land surface temperature (LST) monitoring in subtropical regions remains a significant challenge. To address this issue, this study develops an innovative SAR-optical collaborative framework that integrates Sentinel-1 dual-polarization features with Landsat-8 observations for improving land use and land cover (LULC) classification, restoring cloud-covered areas, and enabling high-quality reconstruction of cloud-free LST. Building on this foundation, future LULC dynamics were projected using the PLUS model, and LST variations were predicted with the XGBoost algorithm, which enabled quantification of LULC-specific contributions to urban thermal change. The prediction model achieved high accuracy (RMSE = 0.9940 °C, MAE = 0.4714 °C, R = 0.9819), underscoring the robustness of SAR-optical integration for LST reconstruction. The results further revealed a strong synchrony between built-up expansion and the increase in LST. Between 2024 and 2030, built-up land is projected to expand by 10.8%, accompanied by a 0.18% increase in extreme high-temperature areas. Overall, the proposed cloud-resilient LST retrieval and prediction framework offers practical value for urban climate adaptation, providing quantitative evidence to support heat mitigation planning, the optimization of green-blue infrastructure, and resilience-oriented spatial development.
Source: Scientific Reports
Authors: Peng He, Zhihui Chen, Lin Zhang, Chengjun Ma, Chen Luo
Language: English
Topic: Urban Heat Island Mitigation
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G5281167704, G6943022293
OpenAlex ID: W4416925684