文献类型： 外文期刊

作者： Dai, Shengpei ¹ ; Luo, Hongxia ¹ ; Hu, Yingying ¹ ; Zheng, Qian ¹ ; Li, Hailiang ¹ ; Li, Maofen ¹ ; Yu, Xuan ¹ ; Chen, Bangqian ³ ;

作者机构： 1.Chinese Acad Trop Agr Sci, Inst Sci & Tech Informat, Key Lab Appl Res Trop Crop Informat Technol Hainan, Haikou, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing, Beijing, Peoples R China

3.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou, Peoples R China

关键词： leaf area index; rubber trees (Hevea brasiliensis); Landsat images; empirical model; neural network model; Hainan Island

期刊名称：JOURNAL OF APPLIED REMOTE SENSING （ 影响因子：1.7； 五年影响因子：1.7 ）

ISSN：

年卷期： 2023 年 17 卷 1 期

页码：

收录情况： SCI

摘要： The leaf area index (LAI) is an important parameter for describing the growth status and canopy structure of vegetation. The rapid and accurate acquisition of the vegetation or agroforestry LAI has great scientific significance in agroforestry ecological ecosystems research and a very important practical value for guiding agricultural and forestry production. In this study, the typical tropical crops (rubber forest) in Hainan Island were selected as the research area, the empirical and neural network (NN) LAI estimation models of rubber forest were constructed based on satellite remote sensing vegetation indices and the field LAI measurement data, and the spatiotemporal variation was analyzed. The results showed that, compared with normalized difference vegetation index (NDVI), green NDVI (GNDVI), ratio VI (RVI), normalized near-infrared (NNIR), wide dynamic range VI (WDRVI), and normalized difference water index (NDWI), enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), DVI, renormalized DVI (RDVI), and modified SAVI (MSAVI) have higher correlations with LAI. Among the LAI estimation models of rubber forest based on empirical and artificial NN (ANN) models, the estimation accuracy of ANN achieves the highest value. The linear fitting determination coefficient R-2 of the observed and simulated rubber forest LAI was 0.85 (p < 0.001), the root mean square error (RMSE) was 0.15, and the average relative error (RE) was 1.93%. However, there was underestimation in the middle-value area and overestimation in the high- and low-value areas of LAI. Based on remote sensing mapping of the rubber forest LAI, the high LAI values (4.40 to 6.00 m(2) m( - 2)) were mainly distributed in Danzhou and Baisha (west of Hainan Island); the middle LAI values (3.80 to 4.40 m(2) m( - 2)) were mainly located in Chengmai, Tunchang, and Qiongzhong (middle of Hainan Island); and the low LAI values (<3.80 m(2) m( - 2)) were shown primarily on Ding'an, Qionghai, Wanning, Ledong, and Sanya (east and south of Hainan Island). In summary, the remote sensing estimation model for the rubber plantation LAI based on the vegetation index has high accuracy and good values for application.