Evaluation of Soil As Concentration Estimation Method Based on Spectral Indices
NING Jing1, 2, ZOU Bin1, 2*, TU Yu-long1, 2, ZHANG Xia3, WANG Yu-long1, 2, TIAN Rong-cai1, 2
1. School of Geosciences and Info-physics, Central South University, Changsha 410083, China
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha 410083, China
3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Abstract：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 R2p 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 (R2p>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.
宁 京，邹 滨，涂宇龙，张 霞，王玉龙，田容才. 土壤As含量光谱指数反演方法评估[J]. 光谱学与光谱分析, 2024, 44(05): 1472-1481.
NING Jing, ZOU Bin, TU Yu-long, ZHANG Xia, WANG Yu-long, TIAN Rong-cai. Evaluation of Soil As Concentration Estimation Method Based on Spectral Indices. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1472-1481.
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