The aim of our study was to verify the impact that pansharpening (PS) methods produce on vegetation indices. We used images with both moderate (Landsat 7, Landsat 8) and high (World View2, Ikonos) spatial resolution on which we performed three methods of PS (Brovey transform, Gram-Schmidt and Principal component). The study is based on the differences of vegetation indices (VI) values before and after the pansharpening method is applied. The difference is quantified as an root mean square error. Vegetation indices used in this study were: NDVI, MSAVI2, EVI2, GNDVI, OSAVI and SAVI. Statistical analysis is carried out by calculating coefficients of correlation, root mean square errors and bias calculations for every vegetation index before and after pansharpening procedure is done. The results imply that the BT gave the most diverse results between original VI values and the PS VI values, while the GS and PC methods preserved the values of pixel bands, and that the effect of any PS method is most evident when using Ikonos bands.
The aim of our study was to verify the accuracy and the usability of Moderate resolution imaging spectroradiometer
(MODIS) 13Q1 product for corn yield estimation on a local level for 2014 year. Product 13Q1 consists of Normalized
Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) 16-day composites with 250 m spatial
resolution. The estimation is based on ground truth data (sowing structures for 8 years) which was provided by local
agricultural organization in Vojvodina, Serbia. The indices were used in linear regression, where the average yield for
corn was the dependent variable, NDVI and EVI were independent variables. Average corn yield was estimated
approximately 15 days before the beginning of the harvest and compared with official results. Depending on the used
linear method, relative errors ranged from 0.6 % to 7.4 %. Overall, coefficients of determination (R<sup>2</sup>) ranged from 0.66
to 0.75 and were significant at 0.05. The smallest difference between official results for corn yield and our estimate when
using NDVI was 0.59 t/ha, when using EVI the smallest difference was 0.07 t/ha. Paper showed that NDVI and EVI
from MODIS follow linear relationship with average corn yield and can be used in estimation of crop yields in Serbia
and also that EVI produces better prediction results than NDVI. The crop yield estimation can be used for similar
cultivated plants in Serbia and for longer period dataset.