9 January 2023 Retrieving leaf area index of rubber plantation in Hainan Island using empirical and neural network models with Landsat images
Shengpei Dai, Hongxia Luo, Yingying Hu, Qian Zheng, Hailiang Li, Maofen Li, Xuan Yu, Bangqian Chen
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Abstract

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 R2 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 m2 m − 2) were mainly distributed in Danzhou and Baisha (west of Hainan Island); the middle LAI values (3.80 to 4.40 m2 m − 2) were mainly located in Chengmai, Tunchang, and Qiongzhong (middle of Hainan Island); and the low LAI values (<3.80 m2 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.

© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shengpei Dai, Hongxia Luo, Yingying Hu, Qian Zheng, Hailiang Li, Maofen Li, Xuan Yu, and Bangqian Chen "Retrieving leaf area index of rubber plantation in Hainan Island using empirical and neural network models with Landsat images," Journal of Applied Remote Sensing 17(1), 014503 (9 January 2023). https://doi.org/10.1117/1.JRS.17.014503
Received: 18 March 2022; Accepted: 19 December 2022; Published: 9 January 2023
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Cited by 3 scholarly publications.
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KEYWORDS
Vegetation

Data modeling

Remote sensing

Atmospheric modeling

Landsat

Correlation coefficients

Agriculture

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