遥感陆地表面温度数据与部分卷积的深度互插
Florian Huber1, Stefan Schulz1, Volker Steinhage1
1Department of Computer Science IV, University of Bonn, 53121 Bonn, Germany.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
这项研究引入了一种新的方法,用于从遥感中插入陆地表面温度 (LST) 数据,克服云层覆盖问题. 该方法使用地面空气温度和深度学习来实现100%的数据覆盖率并提高准确性.
科学领域:
- 地球科学 地球科学 地球科学
- 遥感 遥感 遥感 遥感
- 环境监测 环境监测
背景情况:
- 陆地表面温度 (LST) 对各种应用至关重要,但远程传感数据 (例如MODIS LST) 往往被云层遮蔽.
- 现有的统计插值方法难以整合本地知识和依赖关系,以准确填补LST差距.
研究的目的:
- 通过结合当地地面空气温度测量,开发一种新的方法来插入遥感LST数据.
- 解决当前方法在处理云端诱导的数据缺口和改善LST数据实用性的局限性.
主要方法:
- 采用了一种两步方法:首先,从地面站的空气温度学习LST,其次,使用U-Net深度学习架构,为剩余的插值提供部分卷积.
- 这种方法将本地气象数据与先进的深度学习技术相结合,用于增强空间和时间LST重建.
主要成果:
- 与最先进的统计插值技术相比,提出的方法在根平均平方误差 (RMSE) 中实现了44%的改进.
- 通过利用空气温度数据,该方法成功地提供了100%的数据覆盖,即使在没有有效的LST测量区域,也成功.
结论:
- 新的插值技术显著提高了远程传感LST数据的准确性和完整性.
- 这种无间隙,高分辨率的LST数据使环境研究能够更充分地利用宝贵的遥感资源.
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