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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.
Highlights
As a key element of biological survival, temperature is an important subject in the field of climate research
We mainly examine the change of a single land surface temperature (LST) value before and after reconstruction. e experiment randomly selected 2000 LST original samples from the cloud-free image, so as to avoid selecting too many similar regions. en, we randomly select 5% of the “good quality” pixels from each original sample and modify them as missing data and record the location of the modified pixels
At the same time, compared with traditional reconstruction methods, in the LST data reconstructed by LST palindrome reconstruction network (LPRN), the proportion of “good quality” pixels will be further increased, and the maximum increase can reach 7%
Summary
As a key element of biological survival, temperature is an important subject in the field of climate research. Researchers can determine the water-heat balance between the Earth’s surface and the atmosphere based on surface temperature data. Erefore, it is important to obtain high-precision, continuous surface temperature data. E collection of surface temperature data was initially carried out on mobile phones through discrete observation stations, obtaining high-precision, all-weather data through a large number of ground observation stations. Technological advances have led to Earth observation satellites as the mainstream data collection method, with remote sensing technology the only surface temperature observation method able to guarantee thorough coverage at all times in all areas on a global scale [12]. E collection of surface temperature using remote sensing technology is an effective method for obtaining surface temperatures at a large scale. Thermal infrared remote sensing cannot penetrate clouds, leaving areas under cloud cover inaccessible and affecting data collection
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