Temperature is the main driving force of most ecological processes on Earth, with temperature data often used as a key environmental indicator to guide various applications and research fields. However, collected temperature data are limited by the hardware conditions of the sensors and atmospheric conditions such as clouds, resulting in temperature data that are often incomplete. This affects the accuracy of results using the data. Machine learning methods have been applied to the task of completing missing data, with mixed results. We propose a new data reconstruction framework to improve this performance. Using the MODIS LST map over a span of 9 years (2000–2008), we reconstruct the land surface temperature (LST) data. The experimental results show that, compared with the traditional reconstruction method of LST data, the proportion of effective pixels of the LST data reconstructed by the new framework is increased by 3%–7%, and the optimization effect of the method is close to 20%. The experiment also discussed the influence of different altitudes on the data reconstruction effect and the influence of different loss functions on the experimental results.

中文翻译：

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基于LPRN的地表温度数据缺失信息重建

温度是地球上大多数生态过程的主要驱动力，温度数据通常作为关键环境指标来指导各种应用和研究领域。然而，采集到的温度数据受传感器硬件条件和云等大气条件的限制，导致温度数据往往不完整。这会影响使用数据的结果的准确性。机器学习方法已应用于完成缺失数据的任务，结果喜忧参半。我们提出了一种新的数据重建框架来提高这种性能。使用跨越 9 年（2000-2008 年）的 MODIS LST 地图，我们重建了地表温度 (LST) 数据。实验结果表明，与传统的LST数据重建方法相比，新框架重构的LST数据有效像素比例提高了3%~7%，该方法的优化效果接近20%。实验还讨论了不同海拔高度对数据重建效果的影响以及不同损失函数对实验结果的影响。