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.
Mohammad Hajeb; Saeid Hamzeh; Seyed Kazem Alavipanah; Jochem Verrelst
Abstract
Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. ...
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Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. Launched in 2019, the PRISMA satellite provides one of the most recent hyperspectral data sources which are applicable especially for mapping plant variables. In this study, a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Networkk (BRANN) which applies Bayes' theorem to overcome the overfitting problem of neural networks is used. The model was implemented on a data set consisting of spectrum obtained by PRISMA satellite as an independent variable and sugarcane LAI measurements as a dependent variable. The ground measurements of sugarcane LAI were carried out in 118 elementary sampling units on the fields of Amir Kabir sugarcane cultivation and industry in Khuzestan province and on seven different dates during a sugarcane growth period in 2020. Comparing the performance of BRANN in retrieving sugarcane LAI from PRISMA spectra with that of a conventional ANN trained with the Levenberg-Marquardt algorithm (LMANN) indicates that the retrieval RMSE is reduced from 2.26 m2/m2 applying LMANN to 0.67 m2/m2 applying the BRANN method. In this study, the principle component analysis was also used dimensionality reduction. Retrieving LAI from the first 20 principle components, RMSE was also reduced from 1.41 m2/m2 applying LMANN to 0.71 m2/m2 applying BRANN. Exploiting principal components significantly reduced computational time. By implementing the calibrated BRANN model over the PRISMA image pixel by pixel, the sugarcane LAI map was generated. Evaluating this map showed that this map represents the spatial variations of sugarcane LAI well. The results of this study indicate the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
Eslam galehban; Saeid Hamzeh; Shadman Veysi; Seyed Kazem Alavipanah
Abstract
Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard ...
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Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard method to compute this parameter is to utilize the climate data and experimental equations. The problem with classic methods is that the meteorological station isn’t available in the agricultural lands and usually, we have data limitations. The optimized solution is to utilize remote sensing data. So with the combination of different datasets then the reference evapotranspiration and actual evapotranspiration will be estimated. The goal of the study is to an evaluation of open-source WaPOR and ERA5 to compute daily reference evapotranspiration based on the FAO-Penman Monthis equation at the meteorological stations of Sistan and Baluchestan province. The result has shown that the open-source dataset estimated the reference evapotranspiration as more than 80 percent accurate at the place of the meteorological station and in all of the stations RMSE was less than 2 mm per day. The accuracy assessment of results shown at different crop seasons that ET0 in the autumn season is better than in the spring season. So that the ERA5 combined with the GLDAS Wind data has a better correlation with in situ measurement of ET0 than to the WaPOR. All of the results shown that this dataset can be used in each place in the province to estimate ET0.Therefore, the present study is to investigate the possibility of using the products of WaPOR and ERA5 systems to calculate the amount of daily reference evapotranspiration based on the experimental method of Penman-Monteith and to evaluate and validate its outputs in Sistan and Baluchestan Province of Iran.The results showed that remote sensing systems with an accuracy of over 80% at meteorological stations estimated the amount of reference evapotranspiration and an error of less than 2 mm was reported in all stations. Also, studies during the growing season (June 15 to November 6) compared to the growing season (1 November to 15 May) showed that the reference evapotranspiration obtained from satellite data in the first growing season has a higher (R2). Also, the results of NRMSE index evaluation indicate that the reference evapotranspiration obtained from ERA-GLDAS2.1 data is appropriate.Therefore, since the estimated and validated values had acceptable accuracy, in the next step, these systems can be used anywhere in the province.
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.
