文献类型： 外文期刊

作者： Cheng, Jinpeng ¹ ; Yang, Hao ² ; Qi, Jianbo ³ ; Sun, Zhendong ² ; Han, Shaoyu ² ; Feng, Haikuan ² ; Jiang, Jingyi ³ ; Xu, Weimeng ² ; Li, Zhenhong ⁴ ; Yang, Guijun ² ; Zhao, Chunjiang 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.Beijing Forestry Univ, State Forestry & Grassland Adm Key Lab Forest Reso, Beijing 100083, Peoples R China

4.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China

5.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China

关键词： UAV multispectral imagery; Chlorophyll content; Prior information; Radiative transfer model; Apple orchard

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 202 卷

页码：

收录情况： SCI

摘要： Chlorophyll content is a key trait for understanding the functioning of agroforestry ecosystems and has important implications for leaf and canopy photosynthesis. However, fine-scale monitoring of canopy chlorophyll content (CCC) of individual fruit trees is rather challenging. This study aims to use a 3D radiative transfer model (RTM) and proposes a joint inversion model based on prior knowledge to estimate the CCC of individual tree crowns (ITCs) in apple orchards. The widely recognized 3D RTM LESS (large-scale remote sensing data and image simulation framework over heterogeneous 3D scenes) was adopted for large-scale apple orchard 3D scenes radiative transfer computing and image simulation. LESS was first evaluated with unmanned aerial vehicle (UAV) multispectral imagery and the results showed that it reasonably characterized the reflectance of apple tree canopies (RMSE=0.02). An original look-up table (LUT) with reflectance was then produced using LESS, and the final vegetation indices LUT (VI LUT) including Normalized Difference Vegetation Index (NDVI), Green Chlo- rophyll Index (CIgreen), Red edge Chlorophyll Index (CIred edge) and Green NDVI (GNDVI) was generated from the original LUT form VI interpolation. A physically-based joint inversion model coupling prior knowledge of leaf pigments and leaf area index (LAI) was developed to estimate the CCC of ITCs from high-resolution UAV images. The solution first used linear interpolation to produce a weighted VI LUT corresponding to the sample based on estimated LAI. Linear interpolation was then adopted to screen multiple combinations of leaf chlorophylla+b (Cab) and leaf carotenoids (Cxc) contents from the VI LUT. A prior relationship between Cab and Cxc was finally used to regularize the constraints on multiple VI combinations and determine the estimation of Cab and CCC. The joint inversion model demonstrated an accurate estimation of CCC of ITCs. The model driven by GNDVI yielded the highest result for CCC estimation (R2=0.84, RMSE=24.12 mu g/cm2). In addition, CIgreen (R2=0.82, RMSE=32.22 mu g/cm2) and CIred edge (R2=0.81, RMSE=34.05 mu g/cm2) also achieved satisfactory results. The proposed model facilitates CCC estimation of ITCs from high-resolution imagery in heterogeneous orchard canopies, which is important for advancing the precise nutrition management of fruit trees.