بررسي و برآورد نقش عوامل اقليمي بر NDVI گندم با استفاده از تصاوير سنجنده MODIS در منطقه عمومي شهرستان مشهد

An investigation into the relationship between NDVI and climatological factors in wheat planted farms in Mashhad area using MODIS images

Vegetative vigor or “greenness" for wheat could be considered as an appropriate index to measure plant health, water deficiency stress and also plant density and quality, which can be determined by Normalized Difference Vegetation Index (NDVI). In this study MODIS images were used to calculate NDVI. The index values were compared with climatological factors to assess the relations between vegetative vigor and climatological factors. The consequent results can be used in crop modeling equations. The NDVI values for three selected wheat farms in Mashhad area were calculated using MODIS images for 2003-2004 growing season. The data of four clamatorial factors including air temperature, precipitation, relative humidity and sunshine hours were also collected from the nearest weather station to the farms. Then a multi-regression statistical analysis was performed to find the relations between wheat’s NDVI and climatological factors in the study area. Pertaining statistical methods including Mixed, and Stepwise (Forward and Backward) were used in this analysis. Scattering matrix was used to determine the data scattering of the models and NDVI values for the sake of comparison. The results showed that Backward method was more appropriate than the other two methods for predicting NDVI values of the study area. After finalizing this model the results were statistically tested using 20% of the samples for the test purpose and the remaining 80% for running the model. The results showed that there was no significant difference between Backward, Testing Backward and Training Backward models. The results from the latter method showed that the NDVI of the pixels could be estimated for 79% of the cases. It can be stated that the rest of NDVI values could be affected by other environmental factors such as soil type and conditions, topographical characteristics, agronomical practices, plant diseases and other unknown factors. Finally, maps showing the potential wheat farming in the area according to the model results were developed.