Ali Sadeghi; saham Mirzaei; Saghar Chakherlou; Mehdi Gholamnia; Hossein Ali Bahrami
Abstract
Leaf chlorophyll and nitrogen, due to their important role in photosynthesis are among the major biological parameters of plant physiological status. The ability to quantify chlorophyll and nitrogen can provide important information for precision agricultural activities, plant and agricultural resource ...
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Leaf chlorophyll and nitrogen, due to their important role in photosynthesis are among the major biological parameters of plant physiological status. The ability to quantify chlorophyll and nitrogen can provide important information for precision agricultural activities, plant and agricultural resource management planning, and modeling ecosystem services and production capabilities. This study aimed to assess the capability of indices for estimating the amount of chlorophyll and nitrogen in wheat using spectral data at the canopy level and also determine the most suitable spectral regions and absorption features for this purpose. This research was carried out in a greenhouse environment and the spectroscopic measurements were performed using ASD Fieldspec-3 full-range spectral spectroradiometer. Four plant band indices were classified into two groups of ratio- (NDVI, RVI, and DVI) and soil-based indices (SAVI2) for the raw spectrum and the first derivative of the spectrum for the total samples, and the results were compared. The parameters of position, depth, area, asymmetry and width were calculated for seven absorption features extracted from continuum-removed spectra, and the correlation of these indices with chlorophyll and nitrogen content of wheat was examined. The results showed that SAVI2 had a stronger correlation (RMSE = 0.12, R2 = 0.85) with the chlorophyll content NDVI (RMSE=0.30, R2=0.69) had a higher correlation with the nitrogen content, while using the first derivative with NDVI provided better results. Moreover, area and depth parameters of 430-760 nm absorption spectrum were the best indicators for estimating the amount of chlorophyll and nitrogen in wheat, respectively.
Ali Sadeghi; Ali Darvishi Boloorani; ataolah abdolahi kakroodi; seyed kazem Alaipana; Saeid Hamzeh
Abstract
The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, ...
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The presence of dry and green vegetation in pixels containing spectral information is essential in geological and mineralogical studies. Thus, retrieving sub-pixel information, including estimation of a mineral’s quantity in a single hyperspectral RS image pixel is very important. In this study, the vegetation corrected continuum depth (VCCD) method was trained and its results were validated using spectrometry, laboratory mineralogy, and Hyperion image to reduce the effect of vegetation on the estimation of minerals. The study was conducted in Oghlansar region located in northwestern Iran. SAVI and absorption depth (2102 μm) were used for the estimation of the green and dry vegetation, respectively. Meanwhile, the trained models do not have a high sensitivity to the presence of noise in the spectrum and vegetation type changes. The correction of continuum removed band depth (CRBD) analysis was possible up to 60% for maximum green vegetation cover threshold, 56-60% for dry vegetation, and 72-76% for both dry and green vegetation. Effect of noise and different vegetation types on model capability was examined and the result shows that VCCD is not highly sensitive to random noise and changes in vegetation types. After correction of the coefficients and confirmation of its efficiency, the model was used to correct CRBD and reduce the effect of vegetation on Hyperion image. In the estimation of kaolinite and muscovite, the presence of green and dry vegetation led to the underestimation of the minerals present in the study area. The results showed that VCCD was able to increase the prediction accuracy (R2) by 0.25 and 0.13 and reduce RMSE by 0.0108 and 0.125 for kaolinite and muscovite, respectively.
