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.

地表温度（LST）是全球生态环境和气候变化的重要指标。最近发射的Sentinel-3卫星上的海陆表面温度辐射计（SLSTR）为估算全球LST提供了高质量的观测结果。官方SLSTR LST产品的算法是拆分窗口算法（SWA），它隐式地假定并利用了地表发射率（LSE）的知识。这项研究的主要目的是研究明确使用LSE的替代SLSTR LST检索算法。选择了十七种被广泛接受的，明确使用LSE的SWA作为候选算法。首先，使用全面的全球模拟数据集对SWA进行了培训。然后，使用仿真数据以及原位LST和SWA根据其敏感性和准确性进行了评估：11种算法显示出良好的训练准确性，其中9种算法对LSE和色谱柱水蒸气含量不确定性的敏感性较低。基于两个全球模拟数据集评估和区域模拟数据集显示，这九个SWAS也有类似的准确性可以忽略不计的系统误差和基于超过1.0 K.验证RMSEs降低原位从六个地点获得的LST进一步证实了SWA的准确性，戈巴贝和康斯坦茨湖的最低RMSE范围分别为1.57–1.62 K和0.49–0.61K。虽然最好的两个SWA通常会产生较高的精度，但官方的SLSTR LST通常具有较低的精度。在这项研究中确定和描述的SWA可以用作从SLSTR数据检索LST产品的替代算法。