MODIS陆地表面温度的先进时空缩小:利用Sentinel-1和Sentinel-2数据与机器学习技术在伊朗卡兹温省,伊朗
Zohreh Faraji1, Abbas Kaviani2, Leila Khosravi3
1Department of Water Science and Engineering, Imam Khomeini International University, Qazvin, Iran.
Environmental monitoring and assessment
|July 23, 2025
概括
这项研究开发了一个机器学习框架,使用Google Earth Engine上的Sentinel数据每天创建10米陆地表面温度 (LST) 地图. 该方法实现了高精度,为环境监测提供了可扩展的解决方案.
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
- 地理空间分析的研究.
背景情况:
- 准确的陆地表面温度 (LST) 监测对于了解气候动态,农业实践和城市热岛效应至关重要.
- 现有的LST产品往往缺乏详细的局部分析所需的空间和时间分辨率.
- 高分辨率卫星数据和先进分析平台的整合为克服这些局限性提供了机会.
研究的目的:
- 开发和验证MODIS陆地表面温度 (LST) 数据的时空缩放框架.
- 在谷歌地球引擎 (GEE) 平台上每天使用Sentinel-1和Sentinel-2数据生成10米分辨率的LST地图.
- 评估LST产品的精度,并识别缩小规模的空间和季节性变化.
主要方法:
- 采用机器学习方法,特别是随机森林回归用于空间缩小 (1公里到10米) 和线性回归用于时间重建.
- 在谷歌地球引擎平台内集成多光谱,雷达,植被和海拔数据.
- 与Landsat衍生LST和地面观测相对应,验证生成的LST地图.
主要成果:
- 在LST缩小时实现了高精度,根平均平方误差 (RMSE) 低至1.25K.
- 在各种景观中表现出强的表现,在灌农田中发现了一些不确定性,并在极端季节性条件 (雪地覆盖,强烈灌) 中存在局限性.
- 在2022年成功地为伊朗卡兹文省每天生成了1000万张LST地图.
结论:
- 开发的自动化管道为生成高分辨率LST数据提供了高效和可扩展的解决方案.
- 该框架支持气候监测,精准农业和城市热岛分析等关键应用.
- 这项研究证实了在各种地理环境中使用易于获得的卫星数据和云计算生成准确的每日LST数据的可行性.
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