| 利用高光谱遥感技术监测小麦土壤重金属污染 |
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| 引用本文： | 钟亮,钱家炜,储学远,钱志红,王淼,李建龙.利用高光谱遥感技术监测小麦土壤重金属污染[J].农业工程学报,2023,39(5):265-270. |
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| 作者姓名： | 钟亮 钱家炜 储学远 钱志红 王淼 李建龙 |
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| 作者单位： | 1. 南京大学生命科学学院生态学系，南京 210023;;2. 南京大学物理学院材料学系，南京 210023;;3. 常州大学计算机与人工智能学院，常州 213164;;1. 南京大学生命科学学院生态学系，南京 210023; 2. 南京大学物理学院材料学系，南京 210023; 3. 常州大学计算机与人工智能学院，常州 213164; |
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| 基金项目： | 国家重点研发计划项目（2018YFD0800201） |
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| 摘 要： | 为了探讨基于小麦叶片高光谱间接估测土壤重金属含量的潜力，该研究以江苏省宜兴市徐舍镇为研究区域，于2019－2020年采集农田土壤样品和小麦叶片光谱，经7种不同的光谱变换预处理后，以遗传算法（genetic algorithm，GA）优化的偏最小二乘回归算法（partial least squares regression，PLSR）对预处理后的光谱建立土壤重金属镉（Cd）和砷（As）含量的估测模型，并对模型结果进行精度评价。研究结果表明：1）光谱预处理技术能够突出光谱中的一些隐藏信息，对小麦叶片光谱进行微分变换、多元散射校正、标准正态变换等数学变换后更加有利于提取光谱敏感信息。2）GA-PLSR相较于一般的PLSR方法提高了模型精度，将GA用于光谱波段选择可以优化模型精度和提高稳定性。3）土壤Cd含量的最佳估测模型为标准正态变换预处理光谱与GA-PLSR结合，其外部验证的决定系数为0.87、均方根误差为0.04 mg/kg、相对分析误差为2.72；土壤As含量的最佳估测模型为多元散射校正预处理光谱与GA-PLSR结合，其外部验证的决定系数为0.91、均方根误差为0.32 mg/kg，相对分析误差为3.25。因此，能够利用小麦叶片高光谱间接估测土壤重金属Cd和As含量，该研究为将来实现定量、动态、无损遥感监测大面积农田土壤重金属污染状况提供参考依据。
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| 关 键 词： | 高光谱 遥感 土壤 重金属 小麦农田 光谱变换 土壤-作物场景监测 |
| 收稿时间： | 2022/7/16 0:00:00 |
| 修稿时间： | 2022/11/17 0:00:00 |
Monitoring heavy metal contamination of wheat soil using hyperspectral remote sensing technology |
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| ZHONG Liang,QIAN Jiawei,CHU Xueyuan,QIAN Zhihong,WANG Miao,LI Jianlong.Monitoring heavy metal contamination of wheat soil using hyperspectral remote sensing technology[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(5):265-270. |
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| Authors: | ZHONG Liang QIAN Jiawei CHU Xueyuan QIAN Zhihong WANG Miao LI Jianlong |
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| Institution: | 1. Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China;;2. Department of Materials, School of Physics, Nanjing University, Nanjing 210023, China;;3. School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China;; 1. Department of Ecology, School of Life Sciences, Nanjing University, Nanjing 210023, China; 2. Department of Materials, School of Physics, Nanjing University, Nanjing 210023, China; 3. School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China; |
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| Abstract: | An effective monitoring of soil heavy metal content can greatly contribute to the remediation and treatment of heavy metal pollution. This study aims to explore the potential level in the indirect estimation of soil heavy metal content using wheat leaf hyperspectra. 22 sampling plots of wheat farmland were evenly selected in the study area of Xushe Town, Yixing City, Jiangsu Province, China. Four sample squares were selected for each sample plot, each of which the hyperspectral data was collected from the soil samples and 16 wheat leaves. Firstly, Savitzky-Golay smoothing (SG) was applied to the wheat leaf spectral data, where the SG-smoothed spectra were marked as the original spectra R. Secondly, seven mathematical transformation methods were selected as the first derivative (FD), second derivative (SD), absorbance transformation (AT), first derivative of absorbance (AFD), second derivative of absorbance (ASD), multiple scatter correction (MSC), and standard normal variate (SNV) for the spectral pre-processing of wheat leaf spectra R. Thirdly, the different pre-processing spectra were filtered by genetic algorithm (GA) for the feature bands, and then the heavy metal content was analyzed using partial least squares regression (PLSR). Finally, the accuracy of the estimation model was evaluated to compare the coefficient of determination (R2), root mean square error (RMSE), and relative percent difference (RPD) of cross-validation and external validation. The results show that: 1) The spectral pre-processing technique highlighted some hidden information in the spectra. The differential transformation, multiple scatter correction, and standard normal variate on the wheat leaf spectra were more favorable to extract spectrally sensitive information. 2) The genetic algorithm was used to screen 17-25 characteristic bands of soil Cd, and 16-30 characteristic bands of soil As from 230 full bands, which effectively reduced the band redundancy. Meanwhile, GA-PLSR better improved the model accuracy, compared with the general PLSR. It indicated that the genetic algorithm was used to select the spectral wavelength, and then to optimize the model accuracy and stability for the spectral estimation of soil heavy metal content. 3) The best estimation model for the soil Cd content was the combination of standard normal transform pre-processed spectrum and GA-PLSR, with an external validation R2 of 0.87, RMSE of 0.04 mg/kg, and RPD of 2.72. The best estimation model for the soil As content was a multiple scatter correction pre-processed spectrum of GA-PLSR with an externally validated R2 of 0.91, RMSE of 0.32 mg/kg, and RPD of 3.25. The spectral transform and GA-PLSR performed better to estimate the soil Cd and As content. Therefore, it is possible to indirectly estimate the soil Cd and As contents of heavy metals using hyperspectral wheat leaves. This finding can provide a strong reference for the future realization of quantitative, dynamic and nondestructive remote sensing monitoring of soil heavy metal contamination in a large area of farmland. |
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| Keywords: | hyperspectral remote sensing soil heavy metals wheat farmland spectral transformation soil-crop scene monitoring |
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