Soil respiration (Rs) is influenced by various factors, including soil temperature (ST), soil moisture (SM), and vegetation growth. Accurately and quantitatively estimating Rs from remote sensing data is essential for understanding the carbon cycle in desertification ecosystems. However, selecting appropriate vegetation representation factors poses a significant challenge during the remote sensing inversion. Sun-Induced Chlorophyll Fluorescence (SIF) is used extensively to monitor vegetation diseases and pests, assess drought conditions, and estimate Gross Primary Production (GPP). Nevertheless, the applicability of SIF for estimating Rs from remote sensing data and whether Rs modeling surpasses traditional vegetation indices requires further investigation. This study focuses on the Hongshaquan mining area, utilizing UAV hyperspectral, thermal infrared, and in situ monitoring data, combined with four machine learning methods: Random Forest (RF), Partial Least Squares (PLS), Support Vector Machine (SVM), and Back Propagation Neural Network Algorithm (BP) to establish a model for estimating Rs from remote sensing data. The determination coefficient (R-2) and root mean square error (RMSE) were used to assess the performance of Rs inversion models characterized by SIF, Normalized Difference Vegetation Index (NDVI), and Near-Infrared Reflectance of Vegetation (NIRv) improved by radiance. The feasibility and modeling potential of estimating Rs from remote sensing data using SIF were explored. The results indicate that vegetation significantly impacts Rs in desertification mining area ecosystems, and the inversion accuracy of Rs improved by 26.8% after incorporating vegetation factors. The RF model displayed the best overall performance among the four machine learning methods. When the Salinity Index (SI) and Temperature Vegetation Dryness Index (TVDI) were treated as fixed components of the modeling independent variable, the modeling accuracy of the various vegetation representation factors ranked from highest to lowest as follows: SIF > NIRv > NDVI, with corresponding R-2 values of 0.63, 0.58, and 0.57, and RMSEs of 0.08 mu mol & centerdot;m(-2)& centerdot;s(-1), 0.12 mu mol & centerdot;m(-2)& centerdot;s(-1), and 0.13 mu mol & centerdot;m(-2)& centerdot;s(-1), respectively. The research findings suggest that SIF holds significant promise for remote sensing estimation of Rs. The use of SIF can enhance the accuracy of Rs estimation.