جستجوی مقالات مرتبط با کلیدواژه « ndvi » در نشریات گروه « زراعت »

Satellite images have a high capability for estimating the area under agricultural crops. The aim of this study was to identify the area under dominant crops such as in Shushtar Province using Landsat 8 satellite images during the growing season during 2019. With Maximum Probability technique and Support Vector Machine in the first approach and using NDVI index in the second approach, crops in different growing seasons and according to their calendar, a cropping pattern map was drawn. In order to evaluate the accuracy of the results, the generated maps with reference data were examined. Agricultural Jihad statistics of Khuzestan were also used. The results showed that Kappa coefficient and overall accuracy were calculated as 90% and 80% in the Maximum Probability technique, 92% and 90% in the Support Vector Machine and 95% and 93% in the NDVI, respectively. Based on the results, the cultivation area of wheat, barley, rice, and corn, in the Maximum Probability technique, in comparison with the statistics of Agricultural Jihad, had an error of 12.6, 16.4, 8.7 and 6.6%, respectively and in the Support Vector Machine had an error of 10.1, 8.3, 5.1 and 7.2%, respectively. However, using the NDVI index as the best approach for estimating the cultivation area in this region, in comparison with the statistics of Agricultural Jihad, has an error of 2.4, 1.5, 4.3 and 4.6%, respectively, which indicates the high capability of vegetation indices to estimate the Cultivation Area, According to their phenological stage.

This research was carried out to study the grain yield, yield components and NDVI (normalized difference vegetation index) responses of four wheat cultivars to growth retardant cycocel under terminal drought stress conditions. The experiment was laid out as a split split plot with three replicates at the experimental farm of the School of Agriculture, Shiraz University, Shiraz, Iran in 2016-2017 and 2017-2018 growing seasons. The treatments consisted of two levels of water stress (full irrigation and irrigation cut off at the beginning of flowering), exogenous cycocel in two levels (4 g ha-1 and 0) and four wheat cultivars (Sirvan, Baharan, Varedati and Ofogh). Water stress reduced the measured traits including, grain number per ear (19%), 1000-grain weight (14%), biological yield (22%), grain yield (29%), and NDVI (17%). Exogenously-applied cycocel compensated for some of such reductions including, number of spikes per square meter (18%), grain number per ear (29%), 1000-grain weight (6%), biological yield (11%), grain yield (17%), and NDVI (5%). Ofogh and Sirvan cultivars showed a better response for most of the measured traits to water stress conditions when cyclocel was used. Regarding the ameliorating role of cycocel in reducing negative impacts of terminal water stress, application of this growth regulator could be recommended for similar conditions.

In order to improve food security as one of the most crucial needs of the society, prediction and estimation of wheat yield should be considered by decision makers in the country. On the other hand, agricultural production is always at risk of climate and international markets changes, however, this risk is never completely eliminated, but we can estimate the yield before the harvest season to minimize them. Nowadays, one of the methods of yield estimating is using satellite- imagery. Remote sensing data allow estimating crop yield based on vegetation indices. The present study aimed to find a fast way with acceptable accuracy for predicting wheat yield in the field in Shahrekord County, Chaharmahal and Bakhtiari province, Iran by using Landsat 8 data.

In order to fulfill the goal three sets of Landsat 8 imagery data dated on June 4th, 20th and July 6th 2016 were downloaded from http://earthexplorer.usgs.gov. The acquisition dates were corresponded with milk, dough and ripening stages of wheat growth cycle. Concurrently three wheat cultivated farms were selected in the Shahrekord County, based upon surface area, homogeneity in wheat cultivated farms and satisfaction of landlords with sampling. Coordinates of samples were recorded using GPS device (Garmin etrex). In the whole, the yield of 60 plots (0.25 m2) from selected fields were used for yield prediction. The density of plants per plots was considered and the yield in each plot was estimated. Nine introduced vegetation indices by literature were considered and the correlation coefficients between indices' valued and estimated yields were calculated. The models were evaluated using the coefficient of determination (R2) and standard error of estimation (SEE), reduced variance (RV) and mean estimation error (MEE). Image processing and statistical analysis were carried out using ILWIS 3.3 and Sigma Plot 10.0 software, respectively.

The results showed that among the imagery data, the highest correlation coefficients existed between wheat yield and vegetation indices developed by images dated on 20th June 2016 corresponded to wheat dough stage. The coefficients of determinations (R2) of models with vegetation indices NDVI, NRVI, OSAVI, RVI, SAVI, RDVI, DVI, EVI and GNDVI were 0.86, 0.86, 0.86, 0.86, 0.86, 0.83, 0.81, 0.80, 0.78, respectively. The results indicated that the most appropriate models were Polynomial and quadratic. The results also showed the potential of satellite images for yield prediction at pre-harvest stage with an accuracy above 80 percents.

According to this study appropriate utilizing of satellite images and field observation at dough stage aided yield estimation of wheat in semi-arid regions. The most appropriate indices for yield estimation were NDVI, NRVI and OSAVI. Among the statistical models, polynomial and quadratic models with the highest coefficients of determination were introduced as the best models. Based upon the results Landsat 8 data and field samples could be used for wheat yield forecasting which help decision makers managing the market for this strategic crop. The results should be tested in other similar climates.

Remote sensing technology is widely used in environmental and agricultural research, and nowadays it has become a valuable and reliable source of information for the management of crop production and assessment of global supply and demands for these products. The objective of this study was to investigate the feasibility of using MODIS data and vegetation indices based on sugar beet growth monitoring. For this purpose, MODIS 8-day red and near infrared surface reflectance data at 250 m spatial resolution (MOD09Q1 collection 5) was used. Imagery data was acquired from mid-May to the end of December 2011 from the sugar beet fields located in Eqlid, Fars province, Iran. Results showed that although red and near infrared reflectance differences were relatively small at the beginning of sugar beet growing season, these differences were enhanced with canopy development and ground cover. The most red absorption and near infrared reflectance were in the maximum ground cover, and the most dramatic declines in the near infrared reflectance and relative increase in red reflectance were observed at harvest time. It was found that the RVI is able to show the status of red absorption and reflection during the growth of sugar beet canopy. The SR and NDVI change patterns demonstrated how to grow sugar beet over the growing season. Thus in the existing climate and crop management conditions in Fars province and Iran, MODIS data and vegetation indices can be used to monitor changes in canopy growth of such crops as sugar beet.

Remote sensing is one of the techniques recently used for the purpose of monitoring and predicting crop yield. The objective of this study was predicting wheat yield in Mashhad during 2009 and 2010 by Landsat (TM) and (ETM+) images with ground based data. For this purpose, three images dated 6, 14 and 22 Jun 2009 and 8 and 24 May and 9 Jun 2010 were analysed and vegetation indices were computed. Field data included wheat growth stage, yield and geographical position. Results showed that the best relation between yield and indices were obtained in 14 Jun 2009 and 14 May 2010. These dates coincided with milky and maturity stages of wheat. Cumulative indices estimated yield prediction more precisely than others indices. Red band had the highest correlation (r2=0.76) with yield among the other band. Through the other indices, PD311 and PD312 had the best correlation with the yield. Correlation between NDVI and yield was not significant. The best correlation with the yield was obtained by applying the multi linear regression method (r2=0.83).