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Updated: Jan 13, 2026

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在Landsat交叉轨道区域使用深度合奏学习与不确定性量化进行每日陆地表面温度重建
Shengjie Liu1, Siqin Wang1, Lu Zhang2
1Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA 90089, USA.
概括
我们开发了DELAG,一种深度学习方法,使用Landsat数据重建城市地区的高分辨率地表温度 (LST). 这种方法提高了数据的可用性和准确性,即使在阴天条件下.
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:
- 高时空分辨率的土地表面温度 (LST) 对城市环境研究至关重要.
- 陆地卫星数据提供高空间分辨率,但受到不经常的重新访问和云干扰的影响,限制了其在复杂的城市景观中的实用性.
研究的目的:
- 开发和验证DELAG,这是一个深度集体学习方法,用于复杂的城市地区重建Landsat LST.
- 通过Landsat的交叉轨道能力和双卫星运行来提高LST数据的可用性.
- 评估重建的LST的可靠性和不确定性及其在估计近地表空气温度中的应用.
主要方法:
- 实施了DELAG,这是一种集体深度学习方法,结合了年度温度周期和高斯过程.
- 利用Landsat数据,增强交叉轨道特性和双卫星操作,将数据频率提高到每16天4个场景.
- 在不同云覆盖条件下,在纽约市,伦敦和香港验证了DELAG.
主要成果:
- 德拉格成功地在RMSE值低的复杂城市地区重建了Landsat LST (0.73-0.96 K晴天,0.84-1.62 K阴天).
- 该方法提供了不确定性量化,提高了LST重建的可靠性.
- 重建的LST给出了准确的近地气温估计 (RMSE = 1.48-2.11 K),与清天空LST结果相似.
结论:
- 德拉格为复杂的城市环境中的Landsat LST高分辨率重建提供了一种新而实用的解决方案.
- 增强的数据可用性和准确性解决了传统LST数据采集的局限性.
- 这种方法提高了研究复杂气候事件的潜力,并提高了在高时空分辨率下对空气温度的估计.
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