机器学习预测未来的陆地表面温度来自SAR光学聚变在城市扩张下的长沙,中国的城市扩张
Peng He1, Zhihui Chen2,3, Lin Zhang4
1School of Architecture, Changsha University of Science and Technology, Changsha, 410076, Hunan, China.
Scientific reports
|December 2, 2025
概括
本研究介绍了一种SAR-光学框架,以克服亚热带陆地表面温度 (LST) 监测中云层覆盖的挑战. 它增强了土地使用分类和LST重建,帮助城市气候适应和减热规划.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 城市气候学 城市气候学
背景情况:
- 在光学遥感中,持续的云层覆盖阻碍了在亚热带城市地区精确地监测陆地表面温度 (LST).
- 城市化加剧了热挑战,需要先进的监测和预测技术.
研究的目的:
- 开发一个创新的SAR-光学协作框架,用于在亚热带地区对云有弹性的LST监测和预测.
- 改进土地使用和土地覆盖 (LULC) 分类,并重建高质量,无云的LST数据.
- 量化LULC动态对城市热变化的影响,并支持气候适应战略.
主要方法:
- 将Sentinel-1的双极化特征与Landsat-8观测的整合用于增强LULC分类和LST重建.
- 利用PLUS模型来预测未来的LULC动态.
- 采用 XGBoost 算法用于 LST 变化预测和 LULC 特定热贡献的量化.
主要成果:
- SAR光学框架在LST预测中显示出高准确度 (RMSE = 0.9940 °C,MAE = 0.4714 °C,R = 0.9819).
- 在城市建筑土地扩张和增加的LST之间发现了强烈的相关性.
- 预计10.8%的建地扩张和2024年至2030年间极端高温地区增加0.18%.
结论:
- 拟议的云弹性框架有效地整合了SAR和光学数据,以进行强大的LST检索和预测.
- 调查结果为LULC驱动的城市热量变化提供了定量证据,这对气候适应至关重要.
- 该框架支持减热规划,绿蓝基础设施优化和以弹性为导向的城市发展.
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