文献类型： 外文期刊
作者： Huang, Linsheng 1 ; Song, Furan 1 ; Huang, Wenjiang 2 ; Zhao, Jinling 1 ; Ye, Huichun 2 ; Yang, Xiaodong 3 ; Liang, Do 1 ;
作者机构： 1.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei 230601, Anhui, Peoples R China
2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100089, Peoples R China
关键词： Vegetation index; Leaf area index; Remote sensing; NDVI; Plant disease forecasting
期刊名称：JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING （ 影响因子：1.563； 五年影响因子：1.485 ）
ISSN： 0255-660X
年卷期： 2018 年 46 卷 11 期
页码：
收录情况： SCI
摘要： Vegetation index-based methods have been widely used to determine the leaf area index (LAI). Nevertheless, under the high canopy coverage, the estimation ability of current inversion models has been profoundly decreased, due to the saturation phenomenon. In this study, the LAI of maize was investigated under various growth conditions. Two new triangular vegetation indices were proposed to improve the inversion ability and estimation accuracy of LAI on maize. The triangle difference vegetation index (TDVI) and triangle ratio vegetation index (TRVI) were constructed, and their accuracies were compared with the present spectral vegetation index models. The result shows that TDVI and TRVI are highly linearly correlated with LAI. The coefficients of determination (R-2) and root-mean-square errors are, respectively, 0.92 and 0.94, and 1.42 and 0.92 using the simulated data, while they are, respectively, 0.83 and 0.77, and 0.98 and 1.05 using the measured data. In comparison with other vegetation indices (e.g. MSR, MTVI2, RTVI), TDVI is better able to estimate the LAI of maize. Conversely, TRVI has better inversion ability when the LAI is more than 3. Overall, TDVI is an accurate and robust approach for estimating the LAI of maize. The proposed TDVI and TRVI can be jointly used to retrieve LAI at various canopy coverages.
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