Maedeh Behifar; Hossein Aghighi; Aliakbar Matkan; Hamid Salehi shahrabi
Abstract
Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically ...
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Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize. For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin. Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with outperformed other VIs to estimate LAI of silage maize. Moreover, the values of non-linear regression models were higher that the liner ones.
Davoud Ashourloo; Hamid Salehi Shahrabi; Hamed Nematollahi
Abstract
Spectral vegetation indices have been used as a useful tool in remote sensing to estimate the yield of agricultural crops. However, one factor, which reduces the capability of indices for crop yield estimation, is the limited number of available satellite images. Furthermore, in cases when there are ...
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Spectral vegetation indices have been used as a useful tool in remote sensing to estimate the yield of agricultural crops. However, one factor, which reduces the capability of indices for crop yield estimation, is the limited number of available satellite images. Furthermore, in cases when there are not enough Landsat images, the capabilities of spectral indices in yield estimation using a fusion of MODIS and Landsat data, have been less investigated. The aim of this paper is, first, to introduce the most efficient index/indices for estimating the canola yield and, second, to try to use data fusion techniques in order to increase the efficiency of the selected index/indices. Due to flowering in the growth period, canola has special spectral features. In this research, to estimate the yield of canola, a yield database along with the time series of the Landsat and MODIS data of Moghan Agro-Industry Company fields were provided. Then, 10 spectral indices were evaluated for estimating the canola yield. The relations between the canola yield and the candidate indices were investigated and it was revealed that, during the flowering period, the NDYI index obtained a higher accuracy compared with other indices (r = 0.73). The fusion of the Landsat and MODIS time series data based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), resulted in a 7%-increase and an 11%-decrease in correlation and RMSE (kg/ha), respectively. This research indicated that data fusion techniques are able to improve the performance of spectral indices and hence increase the accuracy of crop yield estimation.
Soheil Radiom; hossein Aghighi; Hamid Salehi Shahrabi
Abstract
Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of ...
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Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of reference evapotranspiration and thus measuring air temperature, humidity and wind speed. Calculation of evapotranspiration is usually based on obtained information from the nearest weather stations to the study area, which can be error-prone. Therefore, in this study, IoT sensors were used to accurately measure air temperature at 2 m above the ground, as well as air humidity and wind speed in the study area. The study area is the farms of Moghan Agricultural Company in Ardabil province. In this study, 23 nodes were installed in a number of farms. The ground-based energy balance algorithm (SEBAL) was used to calculate the evapotranspiration using Landsat 8 images in 2015.
Farzaneh hadadi; Mohsen m_azadbakht; Maedeh Behifar; Hamid Salehi Shahrabi; amir moeinirad
Volume 10, Issue 3 , January 2019, , Pages 53-76
Abstract
Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, ...
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Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of Magsal Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the index had the highest correlation () and the lowest root-mean-square-error (RMSE = 0.316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.
