Variation of the land surface temperature field in rare-Earth ore mining areas based on temperature downscaling.

During the mining process of ionic rare earth elements (REE), mining disturbances, such as land surface excavation, stripped vegetation, injection holes, and tailings discharge, have led to large-scale vegetation degradation and land desertification, ultimately resulting in land surface temperature (LST) differentiation. The surface thermal anomaly is an important ecological parameter, and its spatiotemporal variation has become a powerful tool for identifying surface ecological disturbance. Because the REE mining areas have the characteristics of scattered ore and small areas, it is of great value to obtain LST data with strong practicality and high spatial resolution for sustainable ecological environment monitoring. In this study, the Lingbei mining area in Dingnan County, Ganzhou, China was selected as the study area. Via the use of Landsat and GaoFen-1 (GF-1) remote sensing images, and based on data fusion and the mixed-pixel decomposition method, the temperature downscaling with image fusion and spectral unmixing (TDIFSU) model and the temperature downscaling with data fusion and spectral unmixing (TDDFSU) model were constructed. In addition, they were compared with the commonly used temperature-downscaling disaggregation procedure for radiometric LST (DisTrad) model to explore the accuracy and applicability of different downscaling models for REE mining areas. Finally, based on remote sensing data from the past 20 years, a suitable downscaling model was selected and the temperature difference index (TDI) was used to analyze the spatiotemporal evolutions of ecological disturbances caused by REE mining. The results showed TDDFSU model had the highest accuracy and the smallest mean absolute error (MAE) and root mean square error (RMSE), which were 1.062 K and 1.386 K in February and 0.877 K and 1.094 K in October, respectively, but it had limitations in spatiotemporal study. The accuracy of TDIFSU model was the second, and TDIFSU model based on Landsat data has more advantages in long time series research. DisTrad model has the lowest accuracy. The most suitable model can be selected according to the time span and accuracy; The LST obtained by downscaling can reflect the actual land surface in more detail, and the boundaries between different ground objects were clearer; The high-temperature areas were mainly concentrated in the vicinity of long-term ore occurrences covered with a large amount of tailings. Medium-temperature areas were mainly bare soil and in situ leaching vegetation areas, and low-temperature areas were mainly natural and reclaimed vegetation areas; During the monitoring of the LST in Lingbei mining area for nearly 20 years, the temperature differences caused by different mining activities were revealed, and the land surface disturbance situation has improved after the state actively carried out REE mining environmental control. [ABSTRACT FROM AUTHOR]