Multi‐Sensor Approach for High Space and Time Resolution Land Surface Temperature
Multi‐Sensor Approach for High Space and Time Resolution Land Surface Temperature
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DOI：
10.1029/2021ea001842
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发表时间：
2021-05
影响因子：
3.1
通讯作者：
A. Desai;A. M. Khan;T. Zheng;S. Paleri;Brian J. Butterworth;Temple R. Lee;J. Fisher;G. Hulley;T. Kleynhans;A. Gerace;P. Townsend;P. Stoy;S. Metzger
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作者：
A. Desai;A. M. Khan;T. Zheng;S. Paleri;Brian J. Butterworth;Temple R. Lee;J. Fisher;G. Hulley;T. Kleynhans;A. Gerace;P. Townsend;P. Stoy;S. Metzger
地表-大气通量及其驱动因素在空间和时间上各不相同。越来越多的关注领域是缩小规模，本地化和/或解决次网格规模的能源，水和碳通量和驱动程序。现有的缩小尺度方法需要在相对高的空间（例如，亚千米）和时间（例如，每小时）分辨率，但许多观测到的陆面驱动器在这些分辨率下并不连续可用。我们评估了一种方法来克服这一挑战的陆地表面温度（LST），世界气象组织的基本气候变量和表面热通量的关键驱动因素。通过高密度广泛阵列探测器（CHEESEHEAD 19）现场实验实现的契瓜密贡异质生态系统能量平衡研究提供了一个可扩展的测试平台。我们从卫星（GOES-16和ECOSYSTEM空间站星载热辐射计实验[ECOSTRESS]）中缩小了LST，并使用机载高光谱图像进行了进一步的改进。时间和空间尺度缩小的LST与来自20个微气象塔和有人驾驶飞机的网络的独立观测结果以及在一个塔站点观察到的基于陆地卫星的LST检索和基于无人机的LST进行了比较。缩小后的50米每小时LST与塔（r2 = 0.79，RMSE = 3.5 K）和机载（r2 = 0.75，RMSE = 2.4 K）观测在空间和时间上表现出良好的关系，在湿地和湖泊上的精度较低，与地球同步卫星相比，在捕捉时空变化方面有所改进。使用高光谱图像进一步缩小到10米，解决了整个景观中的热点和冷点，如独立无人机LST所证明的那样，RMSE显著降低了1.3 K。这些结果表明，一个简单的途径，多传感器检索高空间和时间分辨率LST。
Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r2 = 0.79, RMSE = 3.5 K) and airborne (r2 = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST.