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| 基于机载高光谱数据的田间尺度胡敏酸含量估算 | |
| 引用本文： | 郭晗,徐敏贤,徐飞飞,罗明,陆洲,张序.基于机载高光谱数据的田间尺度胡敏酸含量估算[J].浙江农业学报,2021,33(12):2358. |
| 作者姓名： | 郭晗 徐敏贤 徐飞飞 罗明 陆洲 张序 |
| 作者单位： | 1.苏州科技大学 环境科学与工程学院,江苏 苏州 2150092.中国科学院 地理科学与资源研究所,北京 1001013.中亿丰建设集团股份有限公司,江苏 苏州 215131 |
| 基金项目： | 国家重点研发计划(2016YFD0300201);苏州市科技计划(SNG2018100) |
| 摘 要： | 以机载高光谱为数据源,对研究区土壤光谱分别进行去除包络线(CR)、倒数(IR)、对数(LR)、一阶导数(FDR)、二阶导数(SDR)、倒数&一阶导数(IFDR)、对数&一阶导数(LFDR)、倒数&对数(ILR)变换,并分别构建归一化光谱指数(NDSI)(分别相应记为NDSI-CR、NDSI-IR、NDSI-LR、NDSI-FDR、NDSI-SDR、NDSI-IFDR、NDSI-LFDR、NDSI-ILR)。对NDSI与胡敏酸含量的相关性进行分析,筛选出特征光谱,利用多元线性回归(MLR)、偏最小二乘(PLSR)、反向神经网络(BPNN)、支持向量机(SVM)方法构建模型,以决定系数(R2)、均方根误差(RMSE)、相对分析误差(RPD)为评价指标,筛选最佳建模方法,用于田间尺度胡敏酸含量的高效估算。结果表明:NDSI-FDR、NDSI-SDR、NDSI-IFDR、NDSI-LFDR与胡敏酸含量的相关性更高。在396~1 000 nm,有3处与胡敏酸含量敏感的波段密集区域,分别位于480~550 nm与510~570 nm组合处、730~790 nm与740~800 nm组合处、880~930 nm与880~930 nm组合处。基于NDSI-LFDR建立的BPNN模型,建模集和验证集上的R2分别为0.916、0.805,RMSE分别为0.799、1.107,RPD值为2.189,可满足田间尺度胡敏酸含量估算的精度要求。 |
| 关 键 词： | 胡敏酸含量 机载高光谱 特征光谱 |
| 收稿时间： | 2021-03-07 |
Field-scale estimation of humic acid content based on airborne hyperspectral data |
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| GUO Han,XU Minxian,XU Feifei,LUO Ming,LU Zhou,ZHANG Xu.Field-scale estimation of humic acid content based on airborne hyperspectral data[J].Acta Agriculturae Zhejiangensis,2021,33(12):2358. | |
| Authors: | GUO Han XU Minxian XU Feifei LUO Ming LU Zhou ZHANG Xu |
| Institution: | 1. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China 2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China 3. Zhongyifeng Construction Group Co., Ltd., Suzhou 215131, China |
| Abstract: | In the present study, the airborne hyperspectral data were selected as the data source, and the spectra in the study area were transformed by continuum removal(CR), inversion recovery(IR), logistic regression(LR), first derivative reflectance(FDR), second derivative reflectance(SDR), inversion first derivative reflectance(IFDR), logarithm first derivative reflectance(LFDR), inversion logarithm regression(ILR), respectively,to construct normalized difference spectral index (NDSI), and these constructed NDSI data were denoted as NDSI-CR, NDSI-IR, NDSI-LR, NDSI-FDR, NDSI-SDR, NDSI-IFDR, NDSI-LFDR, NDSI-ILR, respectively. The correlation between NDSI and humic acid content was analyzed to identify the characteristic spectra. On this basis, multiple linear regression (MLR), partial least squares (PLSR), back propagation neural network (BPNN) and support vector machine (SVM) models were introduced to construct prediction models. The coefficient of determination (R2), root mean squared error (RMSE) and ratio of performance-to-deviation (RPD) were used as model evaluation indexes to select the best modeling method for the estimation of humic acid content at the field scale. It was shown that NDSI-FDR, NDSI-SDR, NDSI-IFDR, NDSI-LFDR had a higher correlation with humic acid content. In 396-1 000 nm, there were three sensitive band intensive regions with the humic acid content, which were located in the coordinate regions of 480-550 nm and 510-570 nm, 730-790 nm and 740-800 nm, and 880-930 nm and 880-930 nm.For the established BPNN model based on NDSI-LFDR, its R 2 on the modeling set and validation set was 0.916 and 0.805, respectively, its RMSE on the modeling set and validation set was 0.799 and 1.107, respectively, and its RPD was 2.189, which could satisfy the requirement of field-scale estimation of humic acid content. |
| Keywords: | humic acid content airborne hyperspectra characteristic spectrum |
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