| 砂姜黑土有机质含量高光谱估测模型构建 |
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| 引用本文： | 张娟娟,席磊,杨向阳,许鑫,郭伟,程涛,马新明.砂姜黑土有机质含量高光谱估测模型构建[J].农业工程学报,2020,36(17):135-141. |
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| 作者姓名： | 张娟娟 席磊 杨向阳 许鑫 郭伟 程涛 马新明 |
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| 作者单位： | 河南粮食作物协同创新中心,郑州 450002;河南农业大学信息与管理科学学院,郑州450002;河南农业大学信息与管理科学学院,郑州450002;河南粮食作物协同创新中心,郑州 450002;河南农业大学信息与管理科学学院,郑州450002;河南农业大学农学院,郑州 450002 |
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| 基金项目： | 国家重点研发计划项目（2016YFD0300609）；河南省科技攻关项目（192102110012）；河南省现代农业（小麦）产业技术体系项目（S2010-01-G04） |
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| 摘 要： | 为快速估测砂姜黑土有机质含量,该研究以河南省商水县砂姜黑土为对象,采用光谱指数和遗传算法结合支持向量机构建砂姜黑土有机质估测模型。结果表明,以Savitzky-Golay(SG)平滑后的一阶导数光谱792和1 389 nm两波段组合构建的比值指数表现最好,建模集决定系数为0.81。利用独立的样本验证,预测决定系数和均方根误差分别为0.91和1.56 g/kg。而相同样本经遗传算法筛选敏感波段结合支持向量机回归构建的模型以SG平滑的一阶导数光谱表现最好,建模集和验证集决定系数分别为0.95和0.91,均方根误差分别为1.01和1.69 g/kg。基于遗传算法结合支持向量机回归和光谱指数2种方法构建的有机质含量估测模型均表现出较高的精度,前者稍优于后者,可用于对砂姜黑土有机质含量的有效估测。该研究成果可为砂姜黑土有机质含量的快速定量估算提供依据和参考。
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| 关 键 词： | 土壤 有机质 高光谱 光谱指数 遗传算法 支持向量机 砂姜黑土 |
| 收稿时间： | 2020/3/16 0:00:00 |
| 修稿时间： | 2020/5/6 0:00:00 |
Construction of hyperspectral estimation model for organic matter content in Shajiang black soil |
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| Zhang Juanjuan,Xi Lei,Yang Xiangyang,Xu Xin,Guo Wei,Cheng Tao,Ma Xinming.Construction of hyperspectral estimation model for organic matter content in Shajiang black soil[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(17):135-141. |
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| Authors: | Zhang Juanjuan Xi Lei Yang Xiangyang Xu Xin Guo Wei Cheng Tao Ma Xinming |
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| Institution: | 1. Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China; 2. Science College of Information and Management, Henan Agricultural University, Zhengzhou 450002, China;; 1. Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou 450002, China; 2. Science College of Information and Management, Henan Agricultural University, Zhengzhou 450002, China; 3. College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China; |
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| Abstract: | The objectives of this study were to estimate models for organic matter content of Shajiang black soil, which provided a basis and reference for rapid quantitative estimation of soil organic matter. In this study, Shajiang black soil in Shangshui county of Henan province was collected, the organic matter content and hyperspectral reflectance of Shajiang black soil were analyzed and tested simultaneously, and hyperspectral characteristics of Shajiang black soil were studied. Then, the original reflectance was converted to the first derivative spectral with Savitzky-Golay (SG) smoothing. Difference Spectral Indices (DSI), Normalized Spectral Indices (NSI), and Ratio Spectral Indices (RSI) of these two forms spectral were calculated from all available combinations with the reflectance of two random bands between 350 and 2 500 nm, and correlated to soil organic matter content, then the key spectral index and quantitative models for organic matter content of Shajiang black soil were developed. On the other hand, the sensitive bands of soil organic matter were extracted with the Genetic Algorithm (GA) and quantitative models of soil organic matter using Support Vector Machine (SVM) were established. The results showed that the spectral reflectance of Shajiang black soil under different organic matter levels had the same trend, and organic matter levels had a certain influence on reflectance. Organic matter content was higher, the reflectance would be lower, and on the contrary, the reflectance would be higher. The bands with good correlation between organic matter content and DSI, NSI, RSI based on the original reflectance were mainly concentrated near 650 nm, 1 500 nm and 2 200 nm, DSI composed of reflectance of 995 nm and 1 911 nm, NSI composed of reflectance of 2 067 nm and 2 208 nm, RSI composed of reflectance of 1 037 nm and 1 908 nm had the better fitting degree. The bands with good correlation between organic matter content and DSI, NSI, RSI based on the first derivative spectral after SG smoothing were mainly concentrated in the combined band region of 1 350~2 000 nm and 600~1 000 nm, DSI composed of the first derivative of 792 nm and 1 420 nm, NSI composed of the first derivative of 792 nm and 1 389 nm, RSI composed of the first derivative of 792 nm and 1 389 nm had the better fitting degree. For all the spectral indices that were calculated, RSI composed of the first derivative of 792 nm and 1 389 nm gave a better prediction performance, the coefficient of determination was 0.81. Testing of the monitoring models within dependent data indicated that the coefficient of determination and root mean square error of validation were 0.91 and 1.56, respectively. In addition, the sensitive band ranges based on the original reflectance which selected by GA were 461-470, 611-620, 661-670, 741-750, 1 461-1 470, 1 891-1 900, 1 901-1 910, 2 011-2 020, 2 071-2 080 and 2 141-2 150 nm, and the sensitive band ranges based on the first derivative with SG smoothing which selected by GA were 521-530, 531-540, 671-680, 761-770, 771-780, 831-840, 1 431-1 440, 1 451-1 460, 1 871-1 880 and 1 881-1 890 nm for the same sample. The bands of the first derivative with SG smoothing mentioned above were used as input to SVR, and the quantitative model of soil organic matter performed the best. The coefficient of determination and root mean square error of modeling and validation was 0.95 and 0.91, 1.01, and 1.69, respectively. The compared quantitative model with Support Vector Machine, RSI composed of the first derivative of 792 nm and 1389 nm had a little lower modeling accuracy, but it could meet the need for estimating the organic matter content of Shajiang black soil. It was concluded that both methods based on RSI composed of the first derivative of 792 nm and 1 389 nm and SVM can estimate organic matter content of Shajiang black soil accurately. |
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| Keywords: | soil organic matter hyperspectral spectral index genetic algorithm support vector machine Shajiang black soil |
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