基于地面光谱联合SAR多源数据的农田表土氮磷监测
【目的】建立更为精确的高光谱预测模型，以便更加精准地、快速地测定土壤表土氮磷量，进一步推动多源遥感技术在现代化高标准农业生产与管理中的应用和发展。【方法】以内蒙古河套灌区解放闸灌域为试验区，利用地面实测光谱反射率，联合C波段微波雷达SAR（Synthetic Aperture Radar）四极化后向散射数据，通过对土壤氮磷特征波段的选择，建模评价土壤氮磷量。首先利用光谱反射率，及其对数、一阶与二阶导数4种光谱数据，进行相关性分析而滤选获取了与氮磷相关系数均大于0.4的近红外1 480、2 050、2 314 nm等特征波段，同时利用1~8层小波分析与重构图谱技术去除噪声，排除特异值干扰。小波去...
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| Published in | Guanʻgai paishui xuebao Vol. 39; no. 12; pp. 120 - 127 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese English |
| Published |
Xinxiang City
Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage
01.12.2020
内蒙古农业大学水利与土木建筑工程学院,呼和浩特010018 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1672-3317 |
| DOI | 10.13522/j.cnki.ggps.2019465 |
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| Abstract | 【目的】建立更为精确的高光谱预测模型，以便更加精准地、快速地测定土壤表土氮磷量，进一步推动多源遥感技术在现代化高标准农业生产与管理中的应用和发展。【方法】以内蒙古河套灌区解放闸灌域为试验区，利用地面实测光谱反射率，联合C波段微波雷达SAR（Synthetic Aperture Radar）四极化后向散射数据，通过对土壤氮磷特征波段的选择，建模评价土壤氮磷量。首先利用光谱反射率，及其对数、一阶与二阶导数4种光谱数据，进行相关性分析而滤选获取了与氮磷相关系数均大于0.4的近红外1 480、2 050、2 314 nm等特征波段，同时利用1~8层小波分析与重构图谱技术去除噪声，排除特异值干扰。小波去噪后找到相关性强的特征波段，结合SAR后向散射系数，与氮磷做回归及神经网络输入，形成神经网络模型。【结果】通过对高光谱数据的小波分解和重构，能够有效提高反射率及其3种变换形式与土壤氮磷的相关性，尤其是低频分量的1~3层、高频分量的4~6层的效果更好。反射率一阶导数的神经网络模型为最佳预测模型，其对土壤氮、磷量的预测R2分别为0.749 6、0.759 2，均方差RMSE均为0.110 2，其模型的稳定性和预测精度优于多元线性回归模型。【结论】采用光谱联合SAR可以更好地快速预测土壤全氮、全磷。 |
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| AbstractList | 【目的】建立更为精确的高光谱预测模型，以便更加精准地、快速地测定土壤表土氮磷量，进一步推动多源遥感技术在现代化高标准农业生产与管理中的应用和发展。【方法】以内蒙古河套灌区解放闸灌域为试验区，利用地面实测光谱反射率，联合C波段微波雷达SAR（Synthetic Aperture Radar）四极化后向散射数据，通过对土壤氮磷特征波段的选择，建模评价土壤氮磷量。首先利用光谱反射率，及其对数、一阶与二阶导数4种光谱数据，进行相关性分析而滤选获取了与氮磷相关系数均大于0.4的近红外1 480、2 050、2 314 nm等特征波段，同时利用1~8层小波分析与重构图谱技术去除噪声，排除特异值干扰。小波去噪后找到相关性强的特征波段，结合SAR后向散射系数，与氮磷做回归及神经网络输入，形成神经网络模型。【结果】通过对高光谱数据的小波分解和重构，能够有效提高反射率及其3种变换形式与土壤氮磷的相关性，尤其是低频分量的1~3层、高频分量的4~6层的效果更好。反射率一阶导数的神经网络模型为最佳预测模型，其对土壤氮、磷量的预测R2分别为0.749 6、0.759 2，均方差RMSE均为0.110 2，其模型的稳定性和预测精度优于多元线性回归模型。【结论】采用光谱联合SAR可以更好地快速预测土壤全氮、全磷。 S152.7%P628.2; [目的]建立更为精确的高光谱预测模型,以便更加精准地、快速地测定土壤表土氮磷量,进一步推动多源遥感技术在现代化高标准农业生产与管理中的应用和发展.[方法]以内蒙古河套灌区解放闸灌域为试验区,利用地面实测光谱反射率,联合C波段微波雷达SAR(Synthetic Aperture Radar)四极化后向散射数据,通过对土壤氮磷特征波段的选择,建模评价土壤氮磷量.首先利用光谱反射率,及其对数、一阶与二阶导数4种光谱数据,进行相关性分析而滤选获取了与氮磷相关系数均大于0.4的近红外1480、2 050、2 314nm等特征波段,同时利用1～8层小波分析与重构图谱技术去除噪声,排除特异值干扰.小波去噪后找到相关性强的特征波段,结合SAR后向散射系数,与氮磷做回归及神经网络输入,形成神经网络模型.[结果]通过对高光谱数据的小波分解和重构,能够有效提高反射率及其3种变换形式与土壤氮磷的相关性,尤其是低频分量的1～3层、高频分量的4～6层的效果更好.反射率一阶导数的神经网络模型为最佳预测模型,其对土壤氮、磷量的预测R2分别为0.749 6、0.7592,均方差RMSE均为0.1102,其模型的稳定性和预测精度优于多元线性回归模型.[结论]采用光谱联合SAR可以更好地快速预测土壤全氮、全磷. |
| Author | Yule, SUN QU Zhongyi LIU Quanming |
| AuthorAffiliation | 内蒙古农业大学水利与土木建筑工程学院,呼和浩特010018 |
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| Author_FL | QU Zhongyi LIU Quanming SUN Yule |
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| Copyright | Copyright Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage 2020 Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Monitoring of Nitrogen and Phosphorus in Farmland Topsoil Based on Multi-source Data of Ground Spectrum Combined with SAR |
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| Keywords | 小波变换 土壤氮磷 神经网络 模型 高光谱 |
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| Publisher | Chinese Academy of Agricultural Sciences (CAAS) Farmland Irrigation Research Institute Editorial Office of Journal of Irrigation and Drainage 内蒙古农业大学水利与土木建筑工程学院,呼和浩特010018 |
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| Snippet | 【目的】建立更为精确的高光谱预测模型，以便更加精准地、快速地测定土壤表土氮磷量，进一步推动多源遥感技术在现代化高标准农业生产与管理中的应用和发展。【方法】以内蒙古河套灌区解放闸灌域为试验区，利用地面实测光谱反射率，联合C波段微波雷达SAR（Synthetic Aperture... S152.7%P628.2; [目的]建立更为精确的高光谱预测模型,以便更加精准地、快速地测定土壤表土氮磷量,进一步推动多源遥感技术在现代化高标准农业生产与管理中的应用和发展.[方法]以内蒙古河套灌区解放闸灌域为试验区,利用地面实测光谱反射率,联合C波段微波雷达SAR(Synthetic Aperture... |
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| SubjectTerms | Agricultural land Agricultural production Backscattering C band Correlation analysis Infrared analysis Infrared filters Microwave radar Near infrared radiation Neural networks Nitrogen Nutrients Phosphorus Prediction models Radar Reconstruction Reflectance Regression models Remote sensing Root-mean-square errors Soil improvement Soil layers Soil nutrients Soils Spectral reflectance Synthetic aperture radar Technology assessment Topsoil Wavelet analysis Wavelet transforms |
| Title | 基于地面光谱联合SAR多源数据的农田表土氮磷监测 |
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