| 土壤全氮的无人机高光谱响应特征及估测模型构建 |
| |
| 引用本文： | 彭涛,赵丽,张爱军,杨晓楠,周智,常新汉.土壤全氮的无人机高光谱响应特征及估测模型构建[J].农业工程学报,2023,39(4):92-101. |
| |
| 作者姓名： | 彭涛 赵丽 张爱军 杨晓楠 周智 常新汉 |
| |
| 作者单位： | 1. 河北农业大学资源与环境科学学院，保定 071000；;2. 河北农业大学国土资源学院，保定 071000；;3. 国家北方山区农业工程技术研究中心，保定 071000；4. 河北省山区研究所，保定 071000 |
| |
| 基金项目： | 河北省重点研发计划项目（19226421D） |
| |
| 摘 要： | 为更好地体现出光谱与土壤全氮（soil totalnitrogen,STN）含量之间的响应关系，实现以高光谱快速估测土壤全氮含量，该研究以无人机搭载高光谱传感器获取农田土壤高光谱影像，提取光谱反射率并进行数学变换，基于灰色关联度和皮尔逊相关系数提取各光谱中土壤全氮含量的敏感波段，基于敏感波段采用偏最小二乘回归（partialleastsquares regression,PLSR）、岭回归（ridge regression,RR）和随机森林（random forest,RF）构建土壤全氮的高光谱反演模型，筛选出最优模型并对研究区土壤全氮含量进行反演制图。结果表明：1）反射率的倒数光谱中的敏感波段（996～1 003 nm）集中在近红外长波范围内，反射率的一阶微分（first derivative of reflectance,FDR）光谱中的敏感波段（398～459、469和472～1 003 nm）和反射率对数的一阶微分光谱中的敏感波段（398～459、463～973和978～1 003 nm）在可见光和近红外范围内都有分布，反射率的一阶微分光谱中的敏感波段（615～625、632和...
|
| 关 键 词： | 无人机 氮 土壤 高光谱 灰色关联度 相关系数 反演模型 |
| 收稿时间： | 2022/11/2 0:00:00 |
| 修稿时间： | 2023/1/1 0:00:00 |
UAV hyperspectral response characteristics and estimation model construction of soil total nitrogen |
| |
| PENG Tao,ZHAO Li,ZHANG Aijun,YANG Xiaonan,ZHOU Zhi,CHANG Xinhan.UAV hyperspectral response characteristics and estimation model construction of soil total nitrogen[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(4):92-101. |
| |
| Authors: | PENG Tao ZHAO Li ZHANG Aijun YANG Xiaonan ZHOU Zhi CHANG Xinhan |
| |
| Institution: | 1. College of Resources and Environment Sciences, Agricultural University of Hebei, Baoding, 071000, China;;2. College of Land and Resources, Agricultural University of Hebei, Baoding, 071000, China;;3. National Engineering Research Center for Agriculture in Northern Mountainous Areas, Baoding, 071001, China; 4. Hebei Mountain Research Institute, Baoding, 071000, China |
| |
| Abstract: | Abstract: Soil total nitrogen (STN) content can be accurately and rapidly estimated to better reflect the response relationship between spectrum and STN content. In this study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain the soil hyperspectral images in farmland. The original spectral reflectance (R) was then transformed into the reciprocal of reflectance (RR), logarithm of reflectance (LR), the first derivative of reflectance (FDR), the first derivative of reciprocal reflectance (FRR), and the first derivative of logarithm of reflectance (FLR). Grey correlation degree and Pearson correlation coefficient were also selected to extract the sensitive band of STN content in each spectrum. The hyperspectral inversion model of STN was finally constructed using the sensitive band, the partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RF). The determination coefficient R2, root mean square error RMSE, and mean absolute error MAE were used to evaluate the accuracy of the model. After that, the model with the highest accuracy was selected for the inversion mapping of STN content in the study area. The availability of the model was tested, according to the distribution of STN content. The optimal model was selected to invert and map the STN content. The results showed that: 1) The sensitive band (996-1 003 nm) of the RR spectrum was concentrated in the near-infrared long wave range, according to the gray correlation degree and Pearson correlation coefficient. The sensitive bands in the FRR spectrum (39-459, 469, and 472-1 003 nm), and FLR spectrum (398-459, 463-973, and 978-1 003 nm) were distributed in the visible and near-infrared range. The sensitive bands (615-625, 632, and 666-670 nm) in the FDR spectrum were concentrated mainly in the red range of visible light. 2) Pearson correlation coefficient was used to better reflect the response relationship between spectrum and STN content. The STN inversion model R2, RMSE, and MAE were in the range of 0.058-0.693, 0.226-0.477, and 0.171-0.416 g/kg, respectively, in terms of Pearson correlation coefficient. In grey correlation degree, the STN inversion model R2, RMSE, and MAE were within the range of 0.693-0.859, 0.123-0.276, and 0.107-0.209 g/kg, respectively. The accuracy of the model with the Pearson correlation coefficient was higher than that with the grey correlation degree, indicating that the Pearson correlation coefficient performed better on the response relationship between spectrum and STN content. 3) The RF-FDR model was used to estimate the STN content in the field. Among the 12 inversion models of STN content, the RF-FDR model shared the highest accuracy, with R2 of 0.859, RMSE of 0.143 g/kg, and MAE of 0.114 g/kg. The inversion mapping of STN content showed that the STN content in most areas was in the range of 1.50-2.00 g/kg, according to the RF-FDR model. There was the consistency with the average value of STN content in the 72 soil samples, together with the actual situation of planting in one season, low soil fertility consumption, and annual fertilization. As such, the RF-FDR model can be expected to estimate the STN content in fields. Therefore, the Pearson correlation coefficient can be used to extract the sensitive bands for the soil spectrum from UAV hyperspectral. The inversion model of STN content can be constructed with higher accuracy for the effective estimation of STN content. |
| |
| Keywords: | unmanned aerial vehicle (UAV) nitrogen soils hyperspectral grey correlation degree correlation coefficient inversion model |
|
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
