M. Abiyat; M. Abiyat; M. Abiyat
Abstract
Introduction Agriculture is the essential sector for promoting food security. Crop area estimation (CAE) can meet the requirements of the crop monitoring plan. The organizing basis of the cultivation pattern is recognizing the types of crops and examining the condition of their crop area. Shush ...
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Introduction Agriculture is the essential sector for promoting food security. Crop area estimation (CAE) can meet the requirements of the crop monitoring plan. The organizing basis of the cultivation pattern is recognizing the types of crops and examining the condition of their crop area. Shush county in Khuzestan Province has 300,000 hectares of the crop area. It is one of the agricultural hubs of Iran because it has a record annual production of more than two million tons of strategic crops such as wheat, sugar beet, and corn. CAE affects the amount of net production and shortage or surplus of produce for market steadiness. Traditional approaches for CAE are time-consuming and costly and are not widely enforceable. Remote sensing (RS) data provide good information for decision-makers by determining the crop type and the crop area. RS data has made it possible to avoid continuous reference to agricultural lands with less time and cost than another usual method and accurate CAE. Also, the use of multi-time images during the growing season of agricultural products allows the use of spectral curves when related to the crop calendar of each crop. This spectral curve is almost separate for each product and increases the ability to distinguish between products. Therefore, multi-temporal images support segregation based on multispectral images of products. The current study follows a speedy method with appropriate accuracy established on satellite image classification algorithms and spectral indices to identify and separate crops with RS data in Shush County.Materials and Methods Landsat-8 data with path/row coordinates 166/38 extracted from the USGS website were used to identify and separate the cultivated lands of the region. The reason for choosing Landsat images is the relatively suitable temporal and spatial resolution, availability, and the appropriate time distribution with the product growth period. The Landsat 8 carries 2-sensors, OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor). The OLI sensor with a spatial resolution of 30 meters has 8-bands in the visible spectrum, near-infrared (NIR), short-wavelength infrared (SWIR), and a panchromatic band with a spatial resolution of 15 meters. The TIRS sensor can record thermal infrared radiation with a spatial resolution of 100 meters with the help of 2-bands in atmospheric windows of 10.6 to 11.2 micrometers for band 10 and 11.5 to 12.5 micrometers for band 11. This research used bands 1-7 of the Landsat-8 OLI sensor with a spatial resolution of 30 meters after the initial corrections of satellite images. The spectral similarity between the region's dominant crops has made it impossible to select a single image to differentiate and extract the cultivation pattern. Wheat and barley have a high spectral similarity. The peak of the greenness of these products is in the first four months of the year, which has high NDVI values at this time. Therefore, choosing a good time to separate the crops was feasible by referring to the Khuzestan Organization Agriculture-Jihad (KOAJ) and receiving the regional crops calendar in 2018-19. Then, the low-level cloud cover images on April 24, June 27, and August 30, 2019, were selected for classification based on the crop calendar. Planting, harvesting, maximum greenness, and ripening information of the dominant crops in the area were pivotal in obtaining image dates. In dates selected related to the images were considered planting, harvesting, maximum greenery, and ripening information of the region's dominant crops.Results and Discussion According to the results, from total crop area in Shush county (163313.7 hectares) is allocated about 103513.2 hectares (63.4% of the county's crop area) to the ANN, about 102875.1 hectares (63.0% of the county's crop area) to the SVM, and about 102,277.3 hectares (62.6% of the county's crop area) to the NDVI, which in comparison with the KOAJ statistics, has an error of 0.11, 6.2 and 1.8%, respectively.This difference is the similarity of the reflective spectrum in some places, which affects the separability and recognition of phenomena and increases the error in estimating the area under cultivation of different crops. The highest and lowest errors in estimating the area under cultivation in the artificial neural network method were in barley and rice crops, respectively, in the support vector machine method were in wheat and rice crops, respectively, and in NDVI index were in wheat and barley crops, respectively. The difference between the cropped area obtained from classification methods and NDVI index with cropped area statistics of Agricultural-Jihad Organization may be due to the following: First, the cultivation history of different has caused problems such as reflections of diverse agricultural lands in one image. Second, the agricultural lands in this area are small. Most of them are under one hectare. Also, the crops in this area are diverse. Third, the smallest region that the image used in the present study can distinguish is about 900 square meters, which is a large number for the agricultural lands of the study area and causes errors.Conclusion The study results showed that the support vector machine method had the lowest error in CAE than the artificial neural network method, which indicates the higher accuracy of the support vector method in identifying and separating crops in the region. Comparing the area obtained from the NDVI index with the statistics of the Agricultural-Jihad Organization of Khuzestan province and evaluating the accuracy of this method indicated the higher efficiency of spectral indices in CAE for the region compared to classification methods. The NDVI index minimizes the error values of the results due to having a threshold and better identification of vegetation density. Therefore, based on the accuracy assessment results and comparing the cropped area with the KOAJ statistics, the utilization of the NDVI index provides the best CAE in the region.
Agricultural Meteorology
S.M. Ebnehejazi; H. Yazdanpanah; S. Movahedi; M.A. Nasr-Esfahani; M. Moradizadeh
Abstract
Introduction Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature ...
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Introduction Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature fluctuations and rise and fall which are usually unexpected will cause shock and heavy damages. Therefore taking into consideration the agricultural products frost and offering an approach would be of great importance for reducing relevant damages. In studies carried out by Omidvar and Dehghan Banadoki (2012) and Hesari et al. (2015) characteristics and different types of frosts have been considered in relation to the agricultural products. Different models were introduced to predict flowering date in different investigated regions. In more studies, in addition to determining the best model for predicting the date of occurance of flowering stage, probable date of last frost has been estimated as well. Investigating long term temperature changes is a method which applied by Martínez-Lüscher (2017) and Vitasse et al. (2018) to find out about established changes in flowering date and also changes in the last frost date. Nasr Esfahani and Yazdanpanah (2019) realized that 48-hour early warning for frost occurrence can be performed with adequate precision. Despite all studies in the field of products frost particularly during flowering date, it seems a rapid frost warning system must be established and provided to make early warning for each orchard. In this essay, since our goal is to make such early warning three days before frosting, so we have to investigate accuracy and validity of 72-hour minimum temperature simulation using WRF model. On the other hand, we must know phonological stage of each product in each orchard to inform the farmer about frost hazards based on critical temperature, therefore the second goal of this research is to detect phonological stages through Landsat 7 and Landsat 8 images.Materials and MethodsIn order to achieve the aim of current study, 72-hour minimum temperature simulation through the Weather Research and Forecasting (WRF) model was investigated and values of vegetation index were derived for a 30 meters pixel at an experimental orchard in Kahriz, West Azerbaijan Province, in 2016-2107. Computational grid for 2 meters temperature simulation using WRF model contains of three nested grid with horizontal resolution of 27, 9 and 3 kilometers. Horizontal resolution of terrain height and land use data is equal to 30 second (about 1 km). The initial and 3-h boundary conditions with 0.5º horizontal resolution from the Global Forecast System (GFS) were obtained from National Centers for Environmental Information (NCEI). Based on the previous research KFMYJ physical scheme configuration for WRF model were used in this research. Model's hindcasts at 03:00 UTC hour for each of 51 synoptic weather stations of northwest of Iran in internal computational grid were interpolated by MATLAB software with interp 3 function using linear method, then the obtained values were compared to minimum temperature observed in the stations by using MAE, MSE, RMSE and MSSS indicators. Phenological statistics, the time of beginning and end of growth stages were obtained from Iran Meteorological Organization. Besides, 77 Landsat 7 satellite images of ETM+ sensor, and 41 Landsat 8 images of OLI sensor were downloaded from United States Geological Survey website from March to September 2007-2016 with a spatial resolution of 30 meters. In this research, atmospheric and radiometric correction were performed with the FLAASH method on the metadata file in the ENVI software environment and then vegetation index was calculated using NDVI index.Results and DiscussionExamining the evaluation indicators of the WRF model, results revealed a significant correlation and regression model between 2 meters temperature variable from WRF model output and minimum temperature variable observed in the entire stations for 72-hour simulation. As a result WRF model can be applied in 72-hour temperature simulation in the area of study. Another finding of this research indicated that in comparison to the field-recorded data, NDVI values gained from Landsat images properly indicates changes of phenology stages in the relevant apple orchard. In this study, the indicators used to evaluate the model error showed model hindcasts are more accurate for 24-hour and then 48-hour simulations than for 72-hour simulation, but the 72-hour simulation accuracy is not much different from 24-hour and 48-hour simulations. In northwestern Iran, which is a mountainous region, it is very difficult to simulate airflow in areas with complex topography, therefore the total correlation coefficient of all stations in all three simulations is in the range of 0.5, and the error rates of MAE and RMSE, respectively reaches about 2.8 and 3.8 Celsius. According to the second finding of this research, the NDVI indicator obtained from Landsat 7 and Landsat 8 satellite images can show the progress and changes in the phenological stages of apple trees.Conclusion This study showed the efficiency of the WRF model for 72-hour simulation of the minimum temperature as well as the potential of Landsat 7 and Landsat 8 images in detecting apple phenological stages in the study area. Therefore, by using the WRF model for 72-hour minimum temperature simulation and recognizing the phenological stages from Landsat images, if the temperature in any orchard reaches a critical level in the next 72 hours due to the phenological stage, frost warning can be announced and then frost mitigation should be done by the farmer.
B. Bahmanabadi; A. Kaviani
Abstract
Introduction: The exact estimation of evapotranspiration has significant importance in the programming of irrigation development and other distribution systems and water usage. Since the main user of water in the country is the agriculture sector, therefore, the exact estimation of plants’ water demand ...
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Introduction: The exact estimation of evapotranspiration has significant importance in the programming of irrigation development and other distribution systems and water usage. Since the main user of water in the country is the agriculture sector, therefore, the exact estimation of plants’ water demand has been adverted extensively. The assessment methods of reference evapotranspiration are classified in two types of direct and indirect. The calculation of reference evapotranspiration in scientific and in vitro form and with high accuracy is possible by using lysimeter but in comparison to the indirect methods that are based on the climatic data of weather stations, the use of lysimeter is unfortunately inefficient. This is not just for the time consuming and high cost of lysimeter but it is for the limitation of weather stations and spottiness of the estimated values; in this way it is not possible to expand the obtained results to the large scale. Remote sensing is an authentic technique for the assessment of evapotranspiration in large scale which do not consume much time and money. The existence of different satellites by having different spatial and temporal resolution, redouble the importance and usability of this technique
Material and Methods: Actual evapotranspiration assessment in the region were done based on SEBAL, SSEB and TSEB algorithms on 46 imageries of MODIS, seven imageries of Landsat7 (ETM+) and seven imageries of Landsat5 (TM) in years of 2001-2003. Multiplicity of imageries of MODIS show the proper time resolution of this sensor and is a reason for less errors in the assessment of reference evapotranspiration. In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites.
Result and Discussion: In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites, MODIS shows the least errors (respectively, RMSE=0.856, 1.385 and 2.7 mm/day), then Landsat7 is placed in the second class by having higher spatial resolution (respectively, RMSE=1.042, 1.56 and 2.76 mm/day) and Landsat5 has the highest errors (respectively, RMSE = 1.14, 1.97 and 3.06 mm/day). NDVI was found at the lowest amount in the beginning of cultivation period because of germination and sparseness of vegetation, and increase respectively by increasing temperature and crop canopy. L factor has a significant importance in the assessment of SAVI which is related to the area crop coverage percentage. Amount of L has been estimated as L=0.6 that has the least errors in comparison to the others.
Conclusion: In this study, the proper amount for L factor in estimation of the SAVI amount was about 0.6 which was based on the investigations on soil correction factor, the results of statistical indexes and the type and dispersal of vegetation in the region. The accuracy estimation of evapotranspiration of two single-source algorithms of SEBAL and SSEB and one two-source algorithm of TSEB in Bushehr province were evaluated. SEBAL algorithm presented more exact results based on statistical indexes among two single-source algorithms and the obtained results in 95% level of this algorithm showed significant differences with lysimetric measurements. This algorithm was chosen as the premier algorithm in the region. Two-source algorithm of TSEB showed the highest amount of errors. Satellite imageries by having higher spatial resolution estimated evapotranspiration with higher accuracy, the reason of which is proper choosing of cold and hot pixels. Although, because of having proper time resolution and variation of image numbers and also presenting of more time series in comparison to ETM+ and TM, MODIS was more adverted. ETM+ which is located on Landsat satellite was lied in the second place because of its resolution and having higher spatial resolution.
