Ayoub Moradi; Hadiseh Babaei; Abbas Alimohammadi; Soheil Radiom
Abstract
The increasing shortage of the renewable water resources in the country has made the farm water needs estimation to become as one of the important priorities in agricultural water management. Farm water needs are normally controlled by climatologic factors. It equals to the reference evapotranspiration ...
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The increasing shortage of the renewable water resources in the country has made the farm water needs estimation to become as one of the important priorities in agricultural water management. Farm water needs are normally controlled by climatologic factors. It equals to the reference evapotranspiration which is corrected by a scaling factor associated to the crop kind and to local characteristics. In this research, using Landsat satellite imagery, we estimated and compared the crop coefficients for main agricultural crops in the Moghan cultivation industry, from two procedures: the first based on evapotranspiration measuring, and the second based on NDVI measuring. The comparisons in the case of the five main crops showed that the Root Mean Square Errors are within an acceptable range, leass than 0.28. In the following, the evapotranspiration based crop coefficient has been used in order to estimate farm water needs. Farm's water needs are indeed estimated by six methods: a combination of two actual evapotranspiration and three reference evapotranspiration ways. Among the six methods, the Metric/PenmanMonteith method was selected for final step, i.e. farm irrigation needs. The farm irrigation needs is equivalent to farm water need minus effective rain. We compared four different ways for estimating the effective rains but preferred the FAO method assigned for low slopes. Based on our results, farm irrigation needs in the Moghan cultivation industry range from 270 mm (for rainfed barley) to 1500 mm (olive groves). Statistical investigation in three years data revealed a dependency between yield performance and evapotranspiration rate. In addition, it showed that yield performance is correlated with crop spectral indices such as NDVI, LAI and SAVI. The primary goal of this research is to estimate local agricultural crop coefficient in the Moghan cultivation industry. The second goal is to investigate of relationships between crop coefficient and crop spectral indices in order to make the crop coefficient estimable directly from spectral indices.
Farzaneh Hadadi; Hossain Aghighi; Ayoub Moradi
Volume 10, Issue 4 , February 2019, , Pages 99-120
Abstract
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate ...
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The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses.
