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<p>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&#xa0;°C, MAE = 0.4714&#xa0;°C, <i>R</i> = 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.</p>

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Machine learning prediction of future land surface temperature from SAR optical fusion under urban expansion in Changsha, China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Peng He,
  • Zhihui Chen,
  • Lin Zhang,
  • Chengjun Ma,
  • Chen Luo

摘要

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.