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Retrieving soil and vegetation temperatures from dual-angle and multi-pixel satellite observations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2020-01-01 , DOI: 10.1109/jstars.2020.3024190
Zunjian Bian , Hua Li , Frank M. Gottsche , Ruibo Li , Yongming Du , Huazhong Ren , Biao Cao , Qing Xiao , Qinhuo Liu

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.

中文翻译:

从双角度和多像素卫星观测中获取土壤和植被温度

地表成分温度 (LSCT),即土壤和植被的温度,是许多应用中的重要参数,例如估算蒸散量和监测干旱。然而,由于天底和斜视图之间的空间分辨率不同,多角度算法受到影响。因此,我们提出了一种结合使用双角度和多像素观察的组合检索算法。ESA 的 Sentinel-3 卫星上的海陆表面温度辐射计允许准同步双角度观测,可以使用双角度和多像素算法从中检索 LSCT。使用基于合成数据集的敏感性分析证明了组合算法的更好性能。由于空间分辨率不同,斜视图中的空间误差可达4。5 K 并且对多角算法有很大影响。在一个窗口中引入多像素信息可以减少这种空间误差的影响,通过对一个像素使用多角度信息可以进一步提高LSCT的检索结果。在验证中,所提出的组合算法表现更好,草场土壤和植被的 LSCT 均方根误差分别为 3.09 K 和 1.91 K,稀疏森林场地的相应值分别为 3.71 K 和 3.42 K . 考虑到组件之间的温差可以达到 20 K,结果证实,除了像素平均 LST 之外,组合检索算法还可以提供有关 LSCT 的信息。本文展示了利用额外信息源获得更好 LSCT 结果的潜力,
更新日期:2020-01-01
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