Abstract
Leaf area index (LAI) is a valuable indicator used in vegetation growth monitoring. To optimize the index selection according to the type of remote sensing data and to improve the inversion accuracy of LAI, this article analyzes the influence of different bandwidths on the accuracy of the inversion model based on vegetation indices. First, the simulation dataset is generated by the PROSAIL model, and on this basis, 15 vegetation indices with high correlation coefficients with LAI. Then, by analyzing the sensitivity of these 15 indices to the variations in bandwidth, and to the coefficient of determination (R2) of the LAI inversion model with the variations of bandwidth, the influence of different bandwidths on the accuracy of LAI estimation by each index is determined. The results show that bandwidth is one of the most important factors in determining the accuracy of LAI inversion, and the influence on different vegetation indices can be divided into the following three categories. First, narrowband vegetation index, the accuracy of inversion models built by vegetation indices decreases with the increase of bandwidth, including SR[800,680], OSAVI, MTVI2, SR[752,690], RDVI, NDCI, and NVI. Second, middleband vegetation index, the accuracy first increases and then decreases with the increase of bandwidth, including SR[700,670], Carte5, and SR[675,700]. Third, broadband vegetation index, the accuracy increases with the increase of bandwidth, including SPVI, Carte2, OSAVI2, MTVI1, and NDVI705. The study provides a scientific basis for vegetation index optimization in the process of LAI inversion.
Highlights
L EAF area index (LAI) is the sum of the area of a single green leaf per unit surface area
In order to evaluate the capability of different vegetation indices to retrieve LAI, the best fitting models of each vegetation index are selected for curve fitting
The R2 and Root mean square error (RMSE) of the LAI model established by each vegetation index when the bandwidth is between 5 and 80 nm were calculated
Summary
L EAF area index (LAI) is the sum of the area of a single green leaf per unit surface area. It can reflect the physiological and biochemical characteristics of vegetation, and is one of the important structural parameters of vegetation. Due to the different bandwidths of different sensor data, the optimal index can only be selected by exhaustive research methods, a mechanism that is not generally applicable to the actual remote sensing application process [19], [20]. The systematic analysis of the impact of the bandwidth on the vegetation indices during LAI inversion is of great significance for improving the accuracy of LAI inversion
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