荒漠化矿区土壤碳排放高光谱遥感反演

Remote Sensing Inversion of Soil Carbon Emissions in Desertification Mining Areas

随着我国“碳达峰、碳中和”政策的提出,矿区碳排放成为关注焦点,然而目前缺少矿区土壤碳排放高光谱遥感探测的有效方法。基于矿区实测土壤样品,将6种光谱数学变换方法(R、■、Log(1/R)、1st、MSC、SNV)与光谱特征筛选方法(CC-SPA)相结合,探究新疆红沙泉露天煤矿不同土地利用类型土壤碳排放的高光谱响应特征;结合土壤温度(ST)、土壤湿度(SM)及6种光谱指数(NDVI、RVI、NGLI、SMMI、SI-T、ATI),利用偏最小二乘(PLSR)、支持向量机(SVM)、随机森林(RF)、遗传优化神经网络(GA-BP)算法得到土壤碳排放最优遥感反演模型。主要结论如下:(1)自然条件下非采矿影响区土壤反射率明显高于采矿影响区,其中南线受煤炭开采影响最大,反射率最低,证明采矿活动对矿区土壤产生了影响;(2)光谱特征筛选方面,基于相关系数-连续投影算法(CC-SPA)提取的碳排放特征波段数远小于单一方法,且筛选结果呈现聚集式分布,主要集中于1600~2200 nm波长范围内,白天特征波段数远高于夜晚,相较于白天,夜晚特征波段具有明显向长波移动的特征。(3)添加基于反射率构建的光谱指数及ST、SM的反演模型估测土壤碳排放速率的精度明显提升,基于一阶微分变换(1st)的支持向量机模型(SVM)模型反演矿区综合土地利用类型土壤碳排放效果最好(验证集R2=0.813、RMSE=0.116);5种不同土地利用类型土壤碳排放最佳指数组合方式存在差异,引入不同的光谱指数对土壤碳排放速率的估测精度均有不同程度的提升(验证集R2均在0.8以上),其土壤碳排放最优反演模型均可较为准确地估算红沙泉矿区不同土地利用类型土壤的碳排放速率。本研究可为荒漠化矿区土壤碳排放遥感反演提供依据,定量识别不同土地利用类型下土壤的碳源汇效应,并实现了矿区碳排放的无损探测,为我国“30·60”双碳目标提供数据支撑。

With the policy of“carbon peaking and carbon neutrality”put forward in China,carbon emissions in the mining area have become the focus of attention.The study was based on soil samples taken from the mine areas,combined with 6 mathematical transformation methods(R,R,Log(1/R),1st,MSC,SNV)and spectral feature screening methods(CC-SPA).This study explored the hyperspectral response characteristics of soil carbon emissions under different land use types in Hongshaquan Open-pit Coal Mine in Xinjiang;combined with soil temperature(ST),Soil moisture(SM)and 6 kinds of spectral indexes(NDVI,RVI,NGLI,SMM,SI-T,ATI),using partial least squares(PLSR),support vector machine(SVM),random forest(RF),genetic optimization neural network(GA-BP)algorithm to obtain the optimal remote sensing of soil carbon emissions inversion model.The main conclusions are as follows:(1)The reflectance of soil in the non-mining affected area is higher than that in the mining affected area under natural conditions,and the southern line is the most affected by coal mining and has the lowest reflectance,which proves that mining activities have an impact on the mining area soil;(2)Spectral characteristics In terms of screening,the number of carbon emission characteristic bands extracted based on the correlation coefficient-continuous projection algorithm(CC-SPA)is much smaller than that of the correlation coefficient method(CC)and the continuous projection algorithm(SPA),and the characteristic bands present a certain clustered distribution.In the wavelength range of 1600~2200 nm,the number of characteristic bands during the day is much higher than at night.Compared with the daytime,the characteristic bands at night have the characteristics of obviously shifting to long waves.(3)Adding the spectral index based on reflectivity and the inversion model of ST and SM can significantly improve the accuracy of estimating soil carbon emission rate.The support vector machine(SVM)model based on the first-order differential transformation(1st)can invert the mining area.Comprehensive land use types have the best effect on soil carbon emissions(validation set R2=0.813,RMSE=0.116);the optimal combination of soil carbon emission indices for five different land use types is different,and the introduction of different spectral indices has a significant effect on soil carbon emission rates.The estimation accuracy has been improved to varying degrees(the verification set R2 is above 0.8),and the optimal soil carbon emission inversion model can more accurately estimate the carbon emission rate of different land use types in the Hongshaquan mining area.This study can provide a basis for the remote sensing inversion of soil carbon emissions in desertified mining areas,quantitatively identify the carbon source-sink effect of soil under different land use types and realize the non-destructive detection of carbon emissions in mining areas,providing support for my country's“30·60”double carbon goal.Data support.