Comparison of Remote Sensing Estimation Models for Leaf Area Index of Rubber Plantation in Hainan Island

doi:10.12133/j.smartag.2021.3.2.202106-SA003

Abstract

Abstract:

Leaf area index (LAI) is an important index to describe the growth status and canopy structure of vegetation, is of great theoretical and practical significance to quickly obtain LAI of large area vegetation and crops for ecosystem science research and agricultural & forestry production guidance. In this study, the typical tropical crop rubber tree in Hainan Island was selected as the research area, the LAI estimation model of rubber plantation based on satellite remote sensing vegetation indices was constructed, and its spatiotemporal variation was analyzed. The results showed that, compared with correlations between LAI and the indices of normalized difference vegetation index (NDVI), green NDVI (GNDVI), ratio vegetation index (RVI) and wide dynamic range vegetation index (WDRVI), correlations were higher between LAI and the indices of enhanced vegetation index (EVI), soil adjusted vegetation index (SAVI), difference vegetation index (DVI) and modified soil adjusted vegetation index (MSAVI). Among the LAI estimation models based on different vegetation indices (linear, exponential and logarithmic models), the linear estimation model based on EVI index was the best, and its coefficient of determination (R²) was 0.69. The accuracy of LAI estimation model was high. The linear fitting R² of observed and simulated LAI was 0.67, the root mean square error (RMSE) was 0.16, and the average relative error (RE) was -0.25%. However, there was underestimation in the middle value and overestimation in the high and low value area of LAI. The high LAI values (4.40－6.23) were mainly distributed in Danzhou and Baisha in the west of Hainan Island, the middle LAI values (3.80－4.40) were mainly distributed in Chengmai, Tunchang and Qiongzhong in the middle of Hainan Island, and the low LAI values (2.69－3.80) were mainly distributed in Ding'an, Qionghai, Wanning, Ledong and Sanya in the east and south of Hainan Island. In summary, the linear estimation model for rubber plantation LAI based on EVI index obtained high accuracy, and has good values of popularization and appliance.

Key words: leaf area index (LAI), rubber plantation, remote sensing, estimation model, Hainan Island

CLC Number:

S127

DAI Shengpei, LUO Hongxia, ZHENG Qian, HU Yingying, LI Hailiang, LI Maofen, YU Xuan, CHEN Bangqian. Comparison of Remote Sensing Estimation Models for Leaf Area Index of Rubber Plantation in Hainan Island[J]. Smart Agriculture, 2021, 3(2): 45-54.

Figures/Tables 9

Fig. 1

Fig. 2

Table 1

Fig. 3

Table 2

Vegetation index and their formulas

缩写	英文名称		计算公式
EVI	Enhanced Vegetation Index	增强植被指数^［24］	EVI= $2.5 × (ρ n i r - ρ r e d) / (ρ n i r + 6.0 ρ r e d - 7.5 ρ b l u e + 1) ?$ （1）
NDVI	Normalized Difference Vegetation Index	归一化植被指数^［25］	NDVI= $(ρ n i r - ρ r e d) / (ρ n i r + ρ r e d)$ （2）
GNDVI	Green NDVI	绿色归一化植被指数^［26］	GNDVI= $(ρ n i r - ρ g r e e n) / (ρ n i r + ρ g r e e n)$ （3）
RVI	Ratio Vegetation Index	比值植被指数^［27］	RVI= $ρ n i r / ρ r e d$ （4）
SAVI	Soil Adjusted Vegetation Index	土壤调节植被指数^［28］	SAVI= $1.5 × (ρ n i r - ρ r e d) / (ρ n i r + ρ r e d + 0.5)$ （5）
WDRVI	Wide Dynamic Range Vegetation Index	宽动态范围植被指数^［29］	WDRVI= $(0.2 × ρ n i r - ρ r e d) / (0.2 × ρ n i r + ρ r e d)$ （6）
DVI	Difference Vegetation Index	差值植被指数^［30］	$D V I = ρ n i r - ρ r e d$ （7）
MSAVI	Modified Soil Adjusted Vegetation Index	改良土壤调节植被指数^［31］	$M S A V I = 2 × ρ n i r + 1 - (2 × ρ n i r + 1) 2 - 8 × (ρ n i r - ρ r e d) / 2$ （8）

Table 2

Table 3

Table 4

Fig. 4

Fig. 5

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编号	条带号	行编号	日期	时间
1	123	046	2017年8月11日	10：59：07
2	123	046	2017年9月12日	10：59：12
3	123	047	2017年8月11日	10：59：31
4	123	047	2017年9月12日	10：59：36
5	124	046	2017年8月02日	11：05：14
6	124	046	2017年8月18日	11：05：20
7	124	046	2017年9月03日	11：05：22
8	124	046	2017年9月19日	11：05：25
9	124	047	2017年8月02日	11：05：38
10	124	047	2017年8月18日	11：05：44
11	124	047	2017年9月03日	11：05：46
12	124	047	2017年9月19日	11：05：49
13	124	048	2017年8月02日	11：06：02
14	124	048	2017年9月03日	11：06：10
15	124	048	2017年9月19日	11：06：13
16	125	046	2017年8月09日	11：11：28
17	125	046	2017年8月25日	11：11：32
18	125	046	2017年9月10日	11：11：33
19	125	046	2017年9月26日	11：11：39
20	125	047	2017年8月09日	11：11：52
21	125	047	2017年8月25日	11：11：56
22	125	047	2017年9月10日	11：11：57
23	125	047	2017年9月26日	11：12：03

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