| 引用本文: | 徐洪刚,陈 震,程 千,等.无人机多源光谱反演大田夏玉米叶面积指数[J].灌溉排水学报,2021,(8):42-49. |
| XU Honggang,CHEN Zhen,CHENG Qian,,et al.无人机多源光谱反演大田夏玉米叶面积指数[J].灌溉排水学报,2021,(8):42-49. |
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| 摘要: |
| 【目的】研究多源光谱反演大田夏玉米叶面积指数（LAI）的效果。【方法】以大田夏玉米为研究对象，利用无人机获取试验区不同生育期热红外以及多光谱影像，提取热红外冠层温度（TC）以及多光谱植被指数，结合地面实测LAI数据，分析光谱数据与实测LAI之间的相关关系，并将TC与筛选出的11种植被指数作为输入变量，LAI作为输出变量利用多元线性回归、支持向量机和随机森林3个算法模型训练学习，建立了夏玉米LAI的反演模型。【结果】多光谱植被指数以及TC均与夏玉米LAI在P<0.000 1水平上显著相关，相关系数均在0.5以上；RF算法于拔节期、喇叭口期、以及吐丝期3个生育期的LAI预测值与实测值的R2均高于MLR算法和SVM算法，对应的RMSE及NRMSE均低于MLR算法和SVM算法；融合热红外TC后的RF模型反演精度均有不同程度的提升，各生育期LAI预测值与实测值R2均大于同时期未融合TC的LAI反演模型。【结论】多光谱植被指数以及TC均与夏玉米LAI具有较强的相关性，且RF算法构建的夏玉米LAI反演模型精度优于MLR和SVM算法，同时TC的加入可以有效提升夏玉米LAI反演精度。 |
| 关键词: 夏玉米；无人机遥感；多光谱植被指数；热红外图像；叶面积指数；反演模型 |
| DOI：10.13522/j.cnki.ggps.2021038 |
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| Leaf Area Index of Summer Maize Estimated Using UAV-Based Multispectral Imageries |
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XU Honggang, CHEN Zhen, CHENG Qian, LI Zongpeng, LI Peng, FAN Yongshen
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1. Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences/Henan Key Laboratory of
Water-saving Agriculture, Xinxiang 453002, China; 2. Henan Agricultural University, Zhengzhou 450000, China)
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| Abstract: |
| 【Background】Leaf area index (LAI) is an indicator of crop health and controls photosynthesis and transpiration of crops, however, its measurement is nontrivial. The traditional LAI measurement is point-based, time-consuming and laborious, and extrapolating the measured results to large scales could give rise to errors because of crop heterogeneity. The development in unmanned aerial vehicle (UAV) along with imagining technologies over the past decades had open a new avenue to reliably estimate LAI at large scales.【Objective】Taking summer maize as an example, the objective of this paper is to investigate experimentally the feasibility and accuracy of using multispectral UAV imageries to estimate LAI of the maize.【Method】The experiment was conducted in a maize field. Multispectral and thermal infrared imageries of the filed at different growth stages were taken by DJI M210V2 UAV equipped with Micasense Red Edge MX and ZenmuseXT2 dual photothermal imaging sensors. All imageries were first processed using the Pix4D software, and the results were then imported to ArcGIS to extract the thermal infrared canopy temperature (TC) and the multispectral vegetation index. Based on the ground-truth LAI data, we analyzed the correlation between LAI and the spectral data, from which an inversion model was established to estimate LAI using the vegetation index via three models: Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF).【Result】①The multispectral vegetation index and TC were both correlated with the LAI at significant level (P<0.000 1), with the correlation coefficient being more than 0.5. ②The accuracy of the estimated LAI varied with the models and the crop growth stages. On average, the RF model was most accurate, and fitting the field-measured data at jointing, trumpet and silking stages showed that its associated R2 was 0.707, 0.834 and 0.849, respectively. The RMSE and NRMSE of the RF model were also smaller than those of the MLR and SVM models. ③Fusin TC improved the accuracy of all three models for predicting LAI at the jointing stage more than at the other two stages. As the crop grew, the promotion effect gradually decreased while the accuracy of the RF model with the thermal infrared TC integrated was improved though the improvement varied with the growth stage. This indicated that including TC was important to improve LAI estimation.【Conclusion】Multispectral vegetation index and TC are strongly correlated to maize leaves, and the RF model was more accurate than the MLR and SVM models to estimate LAI. In all three models we tested, including TC can improve their LAI estimation. Methods provided in this paper offer an easy and quick way to estimate crop LAI and have implications for precision agriculture. |
| Key words: summer maize; UAV; multispectral vegetation index; thermal infrared image; LAI; inversion model |
