Volume 12, Issue 45 (fall 2008)
jwss 2008, 12(45): 11-19 |
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Sanaienejad S H, Shah Tahmasbi A R, Sadr Abadi Haghighi R, Kelarestani K. A Study of Spectral Reflection on Wheat Fields in Mashhad Using MODIS Data. jwss 2008; 12 (45) :11-19
URL: http://jstnar.iut.ac.ir/article-1-896-en.html
URL: http://jstnar.iut.ac.ir/article-1-896-en.html
Abstract: (33872 Views)
Remote sensing science and satellite data are widely used by researchers for agricultural studies. Vegetation spectral reflections recorded by satellite sensors have been used extensively for identifying plant types, plant cover, health community of plants and predicting yield. The TERRA satellite, with 5 sensors, provides an opportunity to observe land, atmosphere and ocean characteristics. The Moderate Resolution Imaging Spectroradiometer (MODIS) is
on–board TERRA satellite. This sensor with 36 bands by 250m, 500m, and 1000m spatial resolution help us to study our environment. The MODIS vegetation indices are used to monitor photosynthetic activity radiation, change detection in plant communities, planted area estimation and plant health. A statistical analysis was done to analyze Near Infra Red (NIR) (841-876 nm) and Red (R) (620-670 nm) bands of MODIS images for a 16 day period. The images have been used for winter wheat in Mashhad (North East of IRAN) during agricultural season of 2004-05.Some image processing techniques were used to extract the related digital numbers (DN), showing the electromagnetic spectrum reflection for all of the pixels. The analysis shows a positive correlation between R and NIR spectrum (0.70 and 0.69) and decrease in NDVI (0.18 and 0.24) in the first and late wheat growth season. However, there is not such a good correlation in the middle of the season and NDVI increased very much. In spite of having wheat cover in the field, NIR reflection decreased very much in the late wheat growth season (0.5). Therefore the correlation relation between R and NIR band along with NDVI could be used effectively in precision agriculture management such as predicting of phonological stage, wheat yield estimation and wheat health condition.
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