Timely and accurate monitoring of soil respiration (Rs) in desertified mining areas is helpful to understand its spatial and temporal distribution and changes, which is crucial for assessing the carbon cycle of ecologically fragile open-pit mining ecosystems. In this study, we acquired multispectral and thermal infrared images of five experimental sites in Hongshaquan mining area including Dump reclamation area, Plantation forest, Tamarisk forest, Southern line, and Hongsha spring by unmanned aerial vehicle (UAV) and collected ground soil respiration data by gas chamber method. The spectral indices were constructed based on the UAV spectral information, and the relevant combinations of independent variables affecting soil respiration were identified by Pearson correlation analysis and Random Forest (RF) importance assessment. Multiple Linear Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Particle Swarm Optimization Support Vector Regression (PSO-SVR) were used to construct soil respiration remote sensing inversion model, and through determination coefficient (R²), root mean square error (RMSE) and Akaike’s information criterion (AIC) to evaluate the accuracy. The results indicated that the accuracy of MLR was weaker than machine learning methods, and the highest accuracy model was the PSO-SVR soil respiration inversion model based on the combination of vegetation indices (VIS): Green band chlorophyll vegetation index (CI_green), Red-edge band chlorophyll vegetation index (CI_rededge) and Green wave atmospheric resistivity index (GARI), surface temperature (ST), and salinity index (SI) variables (R² = 0.959, RMSE = 0.497, AIC = -0.561). High-resolution UAV multispectral and thermal infrared systems combined with machine learning methods can better estimate soil respiration in desertification mines and provide reference information and basic data for soil respiration and ecosystem carbon cycle monitoring in mining areas.

及时准确地监测荒漠化矿区土壤呼吸（Rs）有助于了解其时空分布和变化，对于评估生态脆弱的露天采矿生态系统的碳循环至关重要。本研究利用无人机采集了红沙泉矿区堆填区、人工林、柽柳林、南线、红沙泉等5个试验点的多光谱热红外图像，并通过无人机采集了地面土壤呼吸数据。气室法。基于无人机光谱信息构建光谱指数，并通过皮尔逊相关分析和随机森林（RF）重要性评估确定影响土壤呼吸的自变量的相关组合。²）、均方根误差（RMSE）和Akaike信息准则（AIC）来评估准确性。结果表明，MLR精度弱于机器学习方法，精度最高的模型是基于植被指数（VIS）组合的PSO-SVR土壤呼吸反演模型：绿带叶绿素植被指数（CI green _）、红边带叶绿素植被指数 (CI _rededge ) 和绿波大气电阻率指数 (GARI)、地表温度 (ST) 和盐度指数 (SI) 变量 (R ² = 0.959，RMSE = 0.497，AIC = -0.561）。高分辨率无人机多光谱和热红外系统结合机器学习方法，可以更好地估算荒漠化矿山土壤呼吸，为矿区土壤呼吸和生态系统碳循环监测提供参考信息和基础数据。