شاخص ndvi

Up-to-date information on growing area is necessary for cropping pattern, and remote sensing is a useful tool in achieve this goal (Hunt et al., 2019). Among various image classification algorithms and vegetation indices for crop type identification, machine learning methods and normalized difference vegetation index (NDVI) have had better results (Mousavi et al., 2020). In addition, attention to plant phenological stages and using multi-temporal images have led to more accurate crop distinction. The aim of this study was estimation of wheat growing areas in Shushtar county in Khuzestan province in Iran.

Time series of NDVI index was obtained from eight Sentinel-2 satellite images from November to June, which correspond to the wheat growth stages. Then, maximum likelihood and support vector machine algorithms were used to identify wheat fields. To implement the classification methods, training and test samples were needed, which were obtained by matching the NDVI diagram of the wheat crop growth stages and field surveys.

Error matrix was used to evaluate the classification results. Based on this, the support vector machine with overall accuracy and Kappa coefficient of 98.5 and 96.5 percent, respectively, had higher accuracy than the maximum likelihood with overall accuracy and Kappa coefficient of 97.8 and 95 percent, respectively. In addition, considering the wheat crop phenological stages using the time series of NDVI in training samples, selection increased the classifications accuracy. Based on the support vector machine results, the total wheat growing areas was estimated to be 48233 hectares, which showed a small deviation from the available surveys.

Using the pattern of NDVI time series to obtain training samples showed the efficiency of plant indices in estimation of wheat growing areas according to the crop phenological stages. In addition, the results revealed that the support vector machine classification was more accurate than the maximum likelihood in the study areas, and it was considered as a base method. One reason for the appropriate performance of the support vector machine was appropriate distribution and adeqaute number of training samples based on wheat phenological stages. It can be concluded that the time series of the normalized difference vegetation index in combination with the support vector machine classification provided the possibility of quick and accurate estimation of the wheat growing areas in Shushtar county in Khuzestan in Iran.

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.