摘要
遥感反演已广泛应用于区域土壤理化性质的动态监测,但是鲜有针对有机碳含量低、下垫面不均一等土壤光谱特性不显著区域的研究。黄土高原褐土带地形多样,丘陵广布,有机碳含量低。采煤活动引起大面积土壤退化,土壤光谱特性受到强烈干扰,制约了区域尺度土壤有机碳(soil organic carbon)含量遥感反演精度。以山西省褐土带典型采煤沉陷区为例,借助地表反射率和室外实地采集的样本数据对褐土带煤矿开采沉陷区土壤有机碳含量进行反演。采用结合高空间、时间分辨率辅助气象数据的6SV(second simulation of a satellite signalin the solar spectrum-vector)模型和FLAASH(fast line-of-sight atmospheric analysis of spectral hypercubes)模型对研究区Landsat8 OLI影像的大气校正方法进行对比实验,分析其对褐土带采煤沉陷区土壤光谱曲线及有机碳含量的影响,识别敏感波段。选择原始光谱反射率(R)和平方根■、倒数的对数log(1/R)、一阶微分(R′)等数学变换形式,利用多元线性回归(MLR)、 BP神经网络(BP neural net)和偏最小二乘回归(PLSR)建立土壤有机碳反演模型。结果表明:6SV模型大气校正的效果要优于FLAASH模型,可以有效消除大气、地形对于反射率的干扰,可见光波段反射率降低而近红外波段明显上升,不同有机质含量等级土壤反射光谱特性分明; 640～670, 850～880, 1 570～1 600和2 110～2 290 nm波段对土壤有机碳含量指示性强;相较于多元线性回归(决定系数R^2为0.765)、 BP神经网络(R^2为0.767),偏最小二乘回归模型反演精度最高(R^2为0.778);结合高空间、时间分辨率辅助气象数据的6SV大气校正模型与偏最小二乘回归建模能显著提高褐土带采煤沉陷区土壤有机碳的反演精度。在此基础上预测研究区2013年—2015年土壤有机碳含量,研究发现:研究区土壤有机碳含量中部高,两侧低,复垦使土壤有机碳含量得到恢复。
Remote sensing retrieval has been widely used for dynamic monitoring of the physical and chemical properties of regional soil,but there are few studies on the areas with low organic carbon content and uneven underlying surface which have unremarkable soil spectral characteristics.The cinnamon soil belt in Loess Plateau has multiplicity topography,widely distributed hills and low organic matter content.Large areas soil degradation caused by mining activities has resulted in the fact that soil spectroscopy characteristics are strongly disturbed,which has some inhibiting effect on the remote sensing retrieval accuracy of soil organic carbon content at the regional scale.Based on cinnamon soil belt with typical coal mining subsidence area in Shanxi Province as an example,this research used the surface reflectance and outdoor sample data from the field of coal mining subsidence area to retrieve soil organic carbon content.Conducting comparative experiments on the atmospheric correction methods of the Landsat8 OLI image in the study area by the FLAASH model and the 6SV model combined with high spatial and temporal resolution aided meteorological data to analyze the effect on soil spectral curve and organic carbon content in the mining subsidence area of the cinnamon soil belt and recognize sensitive bands.Multiple linear regression(MLR),BP neural network(BP)and partial leas squares regression(PLSR)model were established to retrieve soil organic carbon content by using the original spectral reflectance R and mathematical transformation forms such as R,log(1/R)and R′.The results showed that the atmospheric correction effect of the 6SV model was better than that of the FLAASH model which could effectively eliminate the interference of atmosphere and topography to reflectance.The reflectance of visible light decreased and the near-infrared rose obviously.The soil reflectance spectra of different organic matter content was clear.The bands of 640~670,850~880,1 570~1 600,2 110~2 290 nm were highly indicative of soil organic
作者
赵鑫
徐占军
尹建平
毕如田
冯俊芳
刘培
ZHAO Xin;XU Zhan-jun;YIN Jian-ping;BI Ru-tian;FENG Jun-fang;LIU Pei(College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China;Energy-Saving and Environment Protection Department, China Coal Pingshuo Group Co., Ltd., Shuozhou 036006, China;School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2019年第3期886-893,共8页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(51304130)
山西省青年科技研究基金项目(2015021125)
国土资源部公益性行业科研专项经费课题(201411007)资助
