Evaluation of the effect of leaf spatial aggregation on chlorophyll content retrieval in open-canopy apple orchards
文献类型： 外文期刊
作者： Cheng, Jinpeng 1 ; Yang, Hao 1 ; Qi, Jianbo 3 ; Han, Shaoyu 1 ; Sun, Zhendong 4 ; Feng, Haikuan 1 ; Chen, Riqiang 1 ; Zhang, Chengjian 1 ; Li, Jingbo 1 ; Yang, Guijun 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China
3.Beijing Forestry Univ, State Forestry & Grassland Adm, Key Lab Forest Resources & Environm Management, Beijing 100083, Peoples R China
4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430079, Peoples R China
5.Changan Univ, Coll Geol Engn & Geomatics, Xian 710054, Peoples R China
关键词： 3D radiative transfer model; Hyperspectral vegetation index; Chlorophyll content; Leaf spatial aggregation; Open-canopy plantation
期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：7.5； 五年影响因子：7.2 ）
ISSN： 1569-8432
年卷期： 2023 年 121 卷
页码：
收录情况： SCI
摘要： The real-world crowns of broadleaf tree species feature green leaves surrounding branches, resulting in leaf spatial aggregation effect in the crown. However, the impact of such leaf spatial aggregation on chlorophyll content retrieval has not yet been determined. This study investigated the effect of leaf spatial aggregation on chlorophyll content retrieval in two distinct apple orchards with open canopies. The "PROSPECT + LESS" model was used for canopy reflectance simulation, and 25 hyperspectral vegetation indices (VIs) were analyzed to identify universal VIs for various leaf aggregations. Sensitivity analysis was conducted to evaluate the impact of leaf aggregation on the relationships between VIs and chlorophyll content. An artificial neural network regression algorithm was used to retrieve chlorophyll content by reversing the radiative transfer model (RTMs). The results show that leaf aggregation significantly affects the relationships between VIs and chlorophyll content as a result of the variability in the ratio of photosynthetic vegetation pixels to background pixels captured by the sensor at the top of the canopy. TCARI/OSAVI was found to be resistant to confounding factors (e.g., leaf area index and dry matter content) and maintained stable relationships with chlorophyll content. Leaf spatial aggregation had a significant impact on chlorophyll content retrieval, especially when leaves were highly aggregated. In such cases, the spectral variation driven by the photosynthetic vegetation was masked by the background, leading to a large divergence between simulated and observed spectra. Low to moderate levels of leaf aggregation, on the other hand, provided accurate chlorophyll content retrieval in both apple orchards (R2 = 0.49 to 0.67). In conclusion, when using 3D RTMs to retrieve chlorophyll content, it is recommended to configure low to moderate levels of leaf aggregation to ensure high accuracy and efficiency.
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