Abstract
Abstract:To improve the accuracy and universality of the inversion model of the leaf area index, on the one hand, many researchers constantly optimized inversion algorithm, on the other hand, they were committed to analyzing the influence of interference factors such as soil background, soil type, observation geometry and hot spot effected on the inversion process of leaf area index. Band width is generally considered as an important factor affecting the inversion of vegetation parameters. However, there were few studies on the influence of band width on estimating leaf area index. To optimize the selection of vegetation indices based on the type of remote sensing data, the influence of different band widths on the inversion model established by vegetation index was analyzed. Firstly, the spectral reflectance of different band widths was simulated by the measured wheat spectral data set. The initial band width was set to 5 nm and gradually increased to 80 nm in 5 nm steps. On this basis, 28 vegetation indices commonly used for inversion of leaf area indices, such as SR800680, NDCI and Carte2, were calculated. To select the vegetation index with greater potential to estimate the leaf area index, the mean value of the coefficient of determination was used as a prediction accuracy measure, and 14 vegetation indices such as OSAVI2, Carte3 and SR800680 were screened out. Then, by analyzing the sensitivity of 14 indices and variation of coefficient of determination to band widths, the influence of band widths on the accuracy of the leaf area index estimated by vegetation indices was discussed. The results indicated that the band width was one of the important factors that affected the accuracy of the inversion of the leaf area index, and the influence of band width on vegetation indices was inconsistent. According to the trend of coefficient determination, the indices were divided into three categories: (1) coefficient of determination of inversion models built by vegetation indices decreased with the increase of band width. This type of indices included OSAVI2, NDVI, SR752690, SR750700 and Carte2, which was called narrow-band vegetation index. (2) coefficient of determination rose first and then falls with the increase of band width, and the change curve had an obvious peak value, which was called the mid-band vegetation index. This type of indices included Datt3, SR800680 and NDVI705. (3) coefficient of determination rose with the increase of band width, which was called broad-band vegetation index. This type of indices included SR750,550, SR675,700, SR750,710 and RI1dB; (4) coefficient of determination of the models built by Carte3 and Carte4 showed a trend of first decreasing, then rising followed by declining, the accuracy of estimating leaf area index was stable at different band widths, and difference between the maximum and minimum of coefficient of determination was less than 0.003, so the influence of the band width on this type of vegetation indices could be ignored. The results of this study indicated that when using vegetation index for inversion of leaf area index, we should also comprehensively consider channel width and spectral resolution of the sensor to select the best vegetation index. Furthermore, when the band width increased from 5 nm to 80 nm, the precision of the leaf area index inversion model of built by narrow-band vegetation index was higher with the narrower band width, and this type of indices was more suitable for hyperspectral remote sensing data. The optimal band width of the mid-band vegetation index was about 35 nm, and this type of indices was more suitable for remote sensing data with medium resolution. The precision of the leaf area index inversion model built by broad-band vegetation index was higher with the wider band width, and this type of indices had better application potential in multispectral remote sensing data. This research provided the basis for the selection of indices using differe
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