Inversion of Net Photosynthetic Rate in Winter Rapeseed Based on UAV Multispectral Vegetation Indices and Texture Features.
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| Title: | Inversion of Net Photosynthetic Rate in Winter Rapeseed Based on UAV Multispectral Vegetation Indices and Texture Features. |
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| Authors: | Ni, ChengYang1,2 (AUTHOR), Zhang, HaiNa1,2 (AUTHOR) zhanghaina@nit.edu.cn, Lu, XiangHui1,2 (AUTHOR), Zhang, Yue1,2 (AUTHOR), Luo, Xin1,2 (AUTHOR), Ma, Fan2 (AUTHOR), Wan, HaoLong1,2 (AUTHOR), Feng, XiaoYing1,2 (AUTHOR) |
| Source: | Food & Energy Security. Mar/Apr2026, Vol. 15 Issue 2, p1-17. 17p. |
| Subject Terms: | *Photosynthetic rates, *Vegetation monitoring, Rape (Plant), Remote sensing, Surface texture, Plant indicators, Machine learning |
| Abstract (English): | Net photosynthetic rate (Pn) serves as a key indicator for evaluating plant growth and yield. In order to explore an effective method for monitoring winter rapeseed Pn using unmanned aerial vehicle (UAV) multispectral technology, the winter rapeseed was taken as the research object and the multispectral images were obtained through UAVs in this study, combined with field measured Pn data in different growth periods, and Pearson correlation analysis was used to screen vegetation indices (VIs) and texture features (TFs) that were strongly correlated with Pn, and then screened again through recursive feature elimination (RFE), least absolute shrinkage and selection operator (LASSO), and maximum relevance minimum redundancy (MRMR). Pn inversion models of winter rapeseed were constructed based on back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), and multiple linear regression (MLR), and SHapley Additive exPlanations (SHAP) was used to reveal the importance of features. The analysis results showed that the RF model had the highest accuracy in inverting Pn in different growth stages, with the coefficient of determination (R2) of the test sets in the bolting stage and flowering stage being 0.911 and 0.881, respectively, while the root mean square error (RMSE) was 0.727 μmol·m−2·s−1 and 0.917 μmol·m−2·s−1, respectively. Moreover, the R2 of the test set in the whole stage was 0.954, and the RMSE was 0.715 μmol·m−2·s−1. SHAP analysis showed that the red‐edge chlorophyll index 1 (CIrededge1) and the red‐edge chlorophyll index 2 (CIrededge2) played an important role in the RF model inversion of VIs+TFs based on RFE. The research results can provide a theoretical basis and technical support for the inversion of winter rapeseed Pn using multispectral remote sensing by UAV. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 摘要: 净光合速率(Pn)是评价植物生长发育和产量的关键指标。为探索利用无人机(UAV)多光谱技术监测冬油菜Pn的有效方法，本研究以冬油菜为研究对象，通过无人机获取多光谱影像，结合田间实测Pn数据，利用皮尔逊相关性分析筛选与Pn强相关的植被指数(VIs)和纹理特征(TFs)，再通过递归特征消除(RFE)、最小绝对收缩与选择算子(LASSO)和最大相关最小冗余度(MRMR)进行筛选。构建了基于反向传播神经网络(BPNN)、随机森林(RF)、支持向量回归(SVR)和多元线性回归(MLR)的冬油菜Pn反演模型，并利用SHapley加性解释(SHAP)揭示特征重要性。分析结果表明，RF模型在不同时期反演Pn的精度最高。蕾薹期和花期测试集的R2分别为0.911和0.881，RMSE分别为0.7271 μmol·m−2·s−1和0.917 μmol·m−2·s−1；全生育期测试集的R2为0.954，RMSE为0.715 μmol·m−2·s−1。SHAP分析表明，红边叶绿素指数1(CIrededge1)和红边叶绿素指数2(CIrededge2)在基于RFE的VIs+TFs的RF模型反演中起着重要作用。研究结果可为利用UAV多光谱遥感反演冬油菜Pn提供理论基础和技术支持。 [ABSTRACT FROM AUTHOR] |
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| Abstract: | Net photosynthetic rate (Pn) serves as a key indicator for evaluating plant growth and yield. In order to explore an effective method for monitoring winter rapeseed Pn using unmanned aerial vehicle (UAV) multispectral technology, the winter rapeseed was taken as the research object and the multispectral images were obtained through UAVs in this study, combined with field measured Pn data in different growth periods, and Pearson correlation analysis was used to screen vegetation indices (VIs) and texture features (TFs) that were strongly correlated with Pn, and then screened again through recursive feature elimination (RFE), least absolute shrinkage and selection operator (LASSO), and maximum relevance minimum redundancy (MRMR). Pn inversion models of winter rapeseed were constructed based on back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), and multiple linear regression (MLR), and SHapley Additive exPlanations (SHAP) was used to reveal the importance of features. The analysis results showed that the RF model had the highest accuracy in inverting Pn in different growth stages, with the coefficient of determination (R2) of the test sets in the bolting stage and flowering stage being 0.911 and 0.881, respectively, while the root mean square error (RMSE) was 0.727 μmol·m−2·s−1 and 0.917 μmol·m−2·s−1, respectively. Moreover, the R2 of the test set in the whole stage was 0.954, and the RMSE was 0.715 μmol·m−2·s−1. SHAP analysis showed that the red‐edge chlorophyll index 1 (CIrededge1) and the red‐edge chlorophyll index 2 (CIrededge2) played an important role in the RF model inversion of VIs+TFs based on RFE. The research results can provide a theoretical basis and technical support for the inversion of winter rapeseed Pn using multispectral remote sensing by UAV. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20483694 |
| DOI: | 10.1002/fes3.70206 |
