Missing Information Reconstruction of Land Surface Temperature Data Based on LPRN

Abstract

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.