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Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.  相似文献   
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陆表温度是全球气候观测系统的基本变量。由于地表目标多数为具有三维结构、组成成分复杂、温度分布不均一的混合像元,因此相较于平均温度,组分温度具有更明确的物理意义和应用价值,对地—气相互作用过程和水分循环的定量分析研究具有重要意义。本研究针对Sentinel-3 SLSTR数据,基于CBT-P模型和CE-P模型构建了植被—土壤组分温度反演框架,并分析了劈窗算法和LAI对反演误差的影响。使用小汤山、漯河、塞罕坝3地实测数据进行结果验证,结果表明,5个验证点的植被组分温度反演结果的绝对偏差在0.1—1.6 K之间,平均绝对偏差为1.1 K,土壤组分温度反演结果的绝对偏差在0.5—1.4 K之间,平均绝对偏差为0.8 K,在中纬度的稀疏连续植被和垄行植被冠层中取得了较高的精度,初步证明了本研究提出的Sentinel-3 SLSTR双通道双角度地表组分温度反演算法的可行性。  相似文献   
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