Spectral flowers indices are regularly used to estimate flowers biophysical/biochemical characteristics. In wellknown they were proposed to lessen spectral results as a result of outside elements which includes the environment and the soil background. This observe evaluated slim band flowers indices for estimation of flowers leaf region index. The observe takes benefit of the usage of a dataset accumulated at some stage in a laboratory experiment. The spectral measurements were done the usage

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... a GER spectroradiometer. Leaf region indices have been destructively obtained on the equal time. Vegetation sorts sampled protected 4 different sorts and sizes of leaves. For predicting leaf region index, 5 extensively used flowers indices have been investigated. Narrow band flowers indices regarding all feasible band mixtures of RVI, NDVI, PVI, TSAVI, and SAVI2 have been computed. Cross-validation techniques have been used to evaluate the predictive energy of the regression model. We discovered a substantial dating among the slim band SAVI2 and the leaf region index (R2 =0.78, RMSE=0.57). All different slim band indices respectively: RVI, NDVI, PVI and TSAVI had distinctly decrease R2 values (0.65≤ R2 ≤0.75) and better RMSE values in comparison to SAVI2. Our effects confirmed that bands from the SWIR region contain relevant information regarding to canopy LAI and are crucial for LAI estimation. The observe demonstrates that hyperspectral information may be used to quantify leaf region index with a excessive accuracy.