جستجوی مقالات مرتبط با کلیدواژه « satellite images » در نشریات گروه « زراعت »

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.

Traditional methods of biophysical crop parameters (including biomass) estimation in the form of finite sampling or final weighing of the harvested products, is time consuming, costly and difficult. In recent years, the use of satellite imagery and remote sensing technology has been considered to estimate these parameters. So far, several vegetation indices have been developed and used to evaluate and estimate the bio-physiological and biochemical parameters of the crops. Because of the ease of using these indicators, this method is one of the most commonly used remote sensing techniques to estimate such parameters. Considering that such studies have not been carried out so far in Kermanshah province, the current study was carried out to estimate the corn biomass in a fertile plain of Kermanshah province (Mahidasht) using Landsat 8 satellite imagery. The dry weight of the crop biomass was measured at the time of the satellite passing from 15 farms at the study area. During the corn growth period, there were 8 satellite images which downloaded from the American Geological Survey web site. In this study, 17 vegetation indices (NDVI- TNDVI- MNDVI- SAVI- OSAVI- VI1-VI2-VI3-PD311-PD312-PD321-RVI-NRVI- MIRV1-NIR*-DVI-IPVI) which in previous studies showed acceptable correlation with crop biomass were used. The correlation coefficient between the measured biomass and the corresponding values of the vegetation indices were used to evaluate the accuracy of the algorithms. For each fieldwork, the index with higher correlation coefficient was determined as the appropriate index for that stage of crop growth, and a regression relation was presented between the amount of corn biomass and the desired index. Finally, estimated values of the biomass based on the regression equations were compared with measured biomass using normalized mean square error (NRMSE). The measured values of the biomass were low at the beginning of the growth period and gradually increased until the seventh visit (August 26) and then decreased in the last visit (September 11). The average of biomass in 15 farms was measured as 40195 and 36741 kg / ha respectively in seventh and eighth fieldworks. Results of the study showed that the indices of PD311 for the first visit, PD321 for the second visit and the initial stages of growth, NIR* for the third, sixth, seventh and eighths, VI3 for the fourth visit, and the NRVI for the fifth visit, had the highest correlation coefficient with the measured values of biomass. The correlation coefficient of the appropriate index in the 8 fieldworks was 0.42, 0.5, 0.58, 0.71, 0.73, 0.66, 0.57 and 0.47, respectively. In overall, NIR * with the mean correlation coefficient of 0.52 was the most favorable index for the entire growth period. Based on values of NRMSE, it can be concluded that fitted relationships were able to estimate the amount of corn biomass except in the first stage of growth with a moderate to good accuracy. The amount of NRMSE in the last fieldwork, which is related to the final biomass yield, was 11.7%, indicating a good match between observed and predicted data. The results of this study indicate that corn biomass can be estimated using vegetation indices with acceptable accuracy. The precision of this method was better for intermediate periods of crop growth than the early stages. It is better to use an appropriate vegetation index for each stage of crop growth instead of using an index for the entire crop growth period.

One of the major branches of Precision Agriculture (P.A) is Remote Sensing (R.S). This study was in Eghlid Township to investigate the plant population of sugar beet and through this research the capability of LISS-III sensor images taken from satellite IRS-1D, P6 was investigated. 1D image was taken in 86/5/17 and simultaneously 51 data from 4 farms were taken systematically random for regression relationship, and investigation of models accuracy.. The farm “A” had more coverage than other farms for being early planted, therefore for first stage investigation was performed without the “A” farm and the next stage all farms were included. The second image was taken from satellite P6 which was close to sugar been harvesting time 86/7/13 and simultaneously 46 data from 2 farms were taken systematically random for regression relationship, and investigation of models accuracy. Images with less than one pixel accuracy were geo-referenced. For investigation of the relationship between measured plant population and satellite images, liner regression was applied. 1D image in the first stage (B, C, D) had the highest R square (R2=0.74) related to IPVI, NDVI indexes and in the second stage (all the farms), IPVI, NDVI, NRR had the highest R square (R2=0.43). For P6 image the highest R square was 0.27 related to NDVI index. For investigation of models accuracy we used the RMS method. It is concluded that when all the farms are not covered, the images of LISS-III satellite enable us to estimate the sugar beet population. But this needs highly correct image taking period. If the period exceeds 10 days form correct time, it will cause the plant population by satellite images be underestimated.