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基于机器学习的估计陆地表面温度在一个大区域的变化:使用单日卫星图像的时间一致的方法

Nyenshu Seb Rengma¹, Manohar Yadav²

¹Geographic Information System (GIS) Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, 211004, Uttar Pradesh, India.

Environmental monitoring and assessment

|July 15, 2024

概括

准确的陆地表面温度 (LST) 检索对于减缓全球变暖至关重要. 这项研究表明,单日卫星图像与机器学习,特别是随机森林,提高了城市热岛分析的LST估计准确度.

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
气候科学气候科学

背景情况:

城市热岛 (UHIs) 和全球变暖需要精确的土地表面温度 (LST) 监测.
大规模的LST检索通常因时间数据不一致而受到阻碍.
卫星图像是LST估计的关键工具,但精度可以提高.

研究的目的:

用机器学习评估光谱指数对LST检索准确性的影响.
提出并验证使用单日卫星图像进行LST估计的方法.
为了比较不同的机器学习算法对LST估计的性能.

主要方法:

利用一天的卫星图像进行LST检索.
使用表面参数的光谱指数来提高准确性.
应用机器学习算法:极端梯度增强 (XGBoost),光梯度增强机器和随机森林 (RF).
在钱迪加和兰奇进行了案例研究.

主要成果:

与XGBoost和光梯度增强机相比,随机森林 (RF) 在LST估计方面表现出更好的表现.
所有机器学习模型都实现了超过0.8.8的R平方值.
在夏季为0.93,冬季为0.85的钱迪加尔,RF实现了最高的R平方值.
在Ranchi的LST变异捕获中,RF显示出强大的准确性.

结论:

单日卫星图像与机器学习相结合,为大规模的LST估计提供了可靠的方法.
随机森林是一种高效的算法,用于准确的LST检索,对于UHI和全球变暖研究至关重要.
该方法为改善LST估计和理解地球动态系统提供了有价值的见解.

关键词:

土地表面的温度 (LST)轻GBMM 轻GBM 轻GBM 轻GBM 这就是为什么RF是RF,RF是RF 这是卫星图像.时间一致性时间一致性在Xgboost中使用.

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