土壤As含量光谱指数反演方法评估

Evaluation of Soil As Concentration Estimation Method Based on Spectral Indices

为探究基于光谱指数反演土壤砷(As)含量的有效性和适用性,选取河北省保定市某农田为研究区,利用PSR-3500便携式地物光谱仪和电感耦合等离子发射光谱法测定42个土壤样品实验室及野外原位光谱信号和As含量;基于实验室光谱、野外原位光谱及野外原位-直接标准化校正(DS)光谱,计算叶绿素指数(CI)、差值指数(DI)、和值指数(SI)、比值指数(RI)和简化归一化指数(NDI和NPDI),采用相关性分析提取强相关光谱指数参与土壤As含量随机森林回归建模。在此基础上,通过与相同光谱环境全波段建模方法的精度对比评估光谱指数的有效性;对比不同光谱环境中各类光谱指数建模精度的稳定性以评估其适用性;结合典型土壤组分的吸收特征波段尝试解析光谱指数提升土壤As含量反演性能的内在机制。结果表明:(1)相较于全波段建模,光谱指数法在“实验室光谱、野外原位光谱、野外原位-DS光谱”三种光谱环境中均能有效提升土壤As含量反演建模精度,R_(p)^(2)和RPD分别从0.243和1.2提升至0.730和2.009、0.264和1.213提升至0.669和1.809、0.334和1.279提升至0.678和1.841;(2)三种光谱环境中,DI、RI、NDI在野外原位光谱、SI和NPDI在野外原位-DS光谱环境中的适用性较差,CI综合适用性最强,R_(p)^(2)>0.66,RPD>1.8;(3)指数特征波段表现出与铁氧化物、粘土矿物和有机物吸收特征的相关性,但部分指数特征波段缺乏可解释性,无法揭示指数计算通过组合波段放大有效信息和消除噪声的统一规律。该研究可为后续发展基于光谱指数的土壤重金属遥感反演应用甚至卫星有效载荷研制中的波段设计提供科学依据。

To explore the validity and applicability of the estimation of soil arsenic(As)content based on spectral indices,42 soil samples were collected from a farmland in Hebei Province,China.The reflectance spectra and As content were respectively determined by using a PSR-3500 portable ground spectrometer and Inductively Coupled Plasma Atomic Emission Spectrometry.The chlorophyll index(CI),difference index(DI),sum index(SI),ratio index(RI)and simple normalized difference spectral indices(NDI and NPDI)were calculated based onlaboratory spectra,field spectra,and the direct standardization(DS)transferred field spectra.Random forest regression(RFR)models were used to estimate the soil As values using the strongly correlated spectral indices,and indices were evaluated according to the modeling accuracy.Compared with thecharacteristic absorption bands of typical soil components,the internal mechanism of spectral indices improving the inversion accuracy of soil As content was analyzed.The results show that the spectral indices method significantly enhances the correlation between spectra data and As content by combining some low-correlation band information.When compared with the full-band RFR model,the spectral indices method increased the R_(p)^(2)and RPD from 0.243 and 1.2 to 0.730 and 2.009,0.264 and 1.213 to 0.669 and 1.809,0.334 and 1.279 to 0.678 and 1.841 in the lab spectra,field spectra,and field-DS spectra respectively,and CI has the best comprehensive performance(R_(p)^(2)>0.66 and RPD>1.8).However,some of the exponential characteristic bands of the optimal spectra indices lack interpretability and cannot reveal the band combination rules for exponentially amplifying effective information and eliminating noise.The research results can provide a scientific basis for estimating heavy-metal contamination in soil using remote sensing spectroscopy based on spectral indices and even the band design of satellite payloads.