文献类型： 外文期刊

作者： Zhang, Chengjian ¹ ; Chen, Zhibo ¹ ; Yang, Guijun ² ; Xu, Bo ² ; Feng, Haikuan ² ; Chen, Riqiang ¹ ; Qi, Ning ¹ ; Zhang, Wenjie ¹ ; Zhao, Dan ² ; Cheng, Jinpeng ⁴ ; Yang, Hao ¹ ;

作者机构： 1.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

3.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China

4.Henan Agr Univ, Coll Agron, Zhengzhou 450046, Peoples R China

关键词： Leaf area index (LAI); Leaf chlorophyll content (LCC); Canopy chlorophyll content (CCC); Broad -band vegetation indexes (VIs); A hybrid inversion model

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 221 卷

页码：

收录情况： SCI

摘要： The structural and chemical characteristics of individual apple tree crowns can indicate the nutritional and growth status of the trees, making them crucial for advancing orchard management practices. In this study, we collected multispectral imagery and ground validation data from two representative apple orchards in Beijing, China. We employed a hybrid inversion method to estimate the Leaf Area Index (LAI), Leaf Chlorophyll Content (LCC), and Canopy Chlorophyll Content (CCC) of individual apple tree crowns. Furthermore, we quantitatively evaluated the impact of canopy shading on the inversion results and mapped these traits at the scale of individual tree crowns (ITCs). To determine the optimal broad -band Vegetation Indices (VIs) for estimating LAI and LCC, we empirically analyzed 22 VIs using the PROSAIL simulation dataset. We constructed two measured datasets of canopy reflectance by masking canopy shadows, one containing shaded pixels and the other consisting of sunlit -only pixels. Using the Artificial Neural Network (ANN) algorithm and PROSAIL model, we developed a hybrid inversion model to assess the performance of the filtered VIs on the two measured datasets. The results demonstrated that TCARI/OSAVI and SR3 were the most accurate VIs for estimating LAI (including shaded pixels: R2 = 0.67, RMSE = 0.31 m2/m2; sunlit -only pixels: R2 = 0.74, RMSE = 0.28 m2/m2) and LCC (including shaded pixels: R2 = 0.70, RMSE = 7.11 mu g/cm2; sunlit -only pixels: R2 = 0.73, RMSE = 6.63 mu g/cm2) in the two measured reflectance datasets, respectively. Removing canopy shadows significantly improved the accuracy of LAI and LCC retrieval, although there was no significant difference in CCC retrieval accuracy (including shaded pixels: R2 = 0.78, RMSE = 31.25 mu g/cm2; sunlit -only pixels: R2 = 0.79, RMSE = 28.48 mu g/cm2). Moreover, we utilized UAV imaging multispectral data to map the estimated variability of leaf and canopy traits. The results revealed trait variability among different apple tree canopies, highlighting the potential of UAV imaging multispectral techniques in characterizing and mapping individual apple tree crown traits while capturing variability among crowns. We recommend performing canopy shading pixel masking to enhance the accuracy of ITCs trait retrieval.