基于高光谱的矿区复垦农田土壤全氮含量反演

Retrieval of soil total nitrogen content in reclaimed farmland of mining area based on hyperspectral imaging

准确快速估测矿区复垦农田土壤全氮含量是科学评价土地复垦质量的保障。以永城矿区复垦农田为例,对采集的土样进行化学处理和室内高光谱数据测量,对土壤高光谱数据进行3种数学变换,然后与全氮含量进行相关性分析并确定敏感波段。在此基础上,将偏最小二乘法回归(PLSR)模型分别与BP神经网络(BPNN)和随机森林(RF)相结合,建立了PLSR-BPNN和PLSR-RF两种土壤全氮含量高光谱反演模型,并将新建立的模型与传统的PLSR、BPNN和RF进行对比分析。结果表明:与利用单一模型相比,建立的PLSR-BPNN和PLSR-RF两种土壤全氮含量高光谱反演模型精度显著提高,特别是光谱数据经过一阶微分处理并利用PLSR-BPNN模型反演精度最高,验证组决定系数R2达到0.92,相对分析误差RPD为4.01。基于一阶微分光谱建立的PLS-BPNN模型是土壤全氮含量估测模型中的最优方法。研究成果为矿区复垦农田土壤全氮含量反演提供一定的参考价值。

Accurate and rapid estimation of soil total nitrogen( TN) content in reclaimed farmland is the guarantee of land reclamation quality evaluation. Soil samples from reclaimed farmland of Yongcheng mining area were chemically treated and the hyperspectral data were measured indoors. Soil hyperspectral data were transformed by three mathematical methods and then correlated with TN contents,and then the sensitive bands were determined. Subsequently,the partial least squares regression( PLSR) models with BP neural networks( BPNN) and random forest( RF) were combined to establish PLSR-BPNN and PLSR-RF models of inversion of soil TN content based on hyperspectral data. The newly established models were compared with the traditional PLSR,BPNN and RF models. The results showed that the accuracy of the synthetic models( PLSR-BPNN and PLSR-RF) was significantly improved compared to the single model algorithm. In particular,the accuracy of the spectral data processed by the first-order differential method PLSR-BPNN was the highest,with a decision coefficient( R2) of 0.92 and a relative analysis error( RPD) of 4.01. Therefore,the PLS-BPNN model based on the first-order differential spectrum was the best one among the estimation models of soil TN content. The results provide reference for the retrieval of soil TN based on hyperspectral data in reclaimed farmland.