Elaheٍ Akbari; M Hajeb; Mehrdad Jeihouni; Saeid Hamzeh
Abstract
To determine the effect of the leaf biochemical contents on its spectral reflectance behavior via remote sensing (RS) can help to understand the process of the ecosystem and its parameters such as plant water stress. The present study aimed to do a quantitative analysis of the effect of leaf parameters, ...
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To determine the effect of the leaf biochemical contents on its spectral reflectance behavior via remote sensing (RS) can help to understand the process of the ecosystem and its parameters such as plant water stress. The present study aimed to do a quantitative analysis of the effect of leaf parameters, including the amount of leaf chlorophyll, leaf structure, and leaf water content, on the leaf spectral reflectance. To this end, the PROSPECT radiative transfer model which developed to simulate the spectral behavior of plant leaves, was employed. The research results showed that the increase of chlorophyll with the effect of reducing the leaf spectral reflectance leads to the increase of Triangular Vegetation Indices (TVIs). In the visible light spectrum, it is possible to distinguish monocotyledons (monocots), dicotyledons (dicots), and old plants. Also, in the near-infrared (NIR) light spectrum, the amount of reflection decreases in old and unstructured plants, dicotyledonous plants, and monocotyledonous plants, respectively. The drying of the plant does not have much effect on the reflection, but drying more than a certain amount causes a significant increase in the reflection, especially outside the water absorption spectra. Therefore, finding the critical points of the reflectance curve against the water content can contribute to detecting severe water stress in plants. By examining the graphs, it can be observed that the critical point occurs about the water content of 0.03 to 0.04 g⁄〖cm〗^2 . In the PROSPECT radiative model, the effect of soil on the spectral reflectance of plants is not considered. Therefore, it is recommended to use models such as SAIL and SLC that have been upgraded for this purpose.
Elahe Akbari
Abstract
Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing ...
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Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. Field sampling of soil (prior to cultivation) and crop (during the growth season), digital hemispherical photography (DHP) and destructive method for comparison purposes were carried out for silage maize in Qhale-Nou county, South Tehran, in the summer of 2019. Remote sensing data assimilation based on forcing method, by biophysical variable of fCover extracted of remote sensing data was incorporated into the AquaCrop model. Then, the most sensitive model parameters which identified through sensitivity analysis were estimated and the obtained results were then compared with the case where assimilated data were not incorporated. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. The overall findings allude to the advantages of incorporating remote sensing data assimilation by the forcing method as a relatively efficient tool for simulating silage maize yield under variable environmental conditions.
