Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation

Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation This study investigated the relationships between sunflower yield and crop multi-temporal spectral data obtained from aerial photographs, land elevation and the presence of Ridolfia segetum weed. Conventional-color and color-infrared airborne photographs were taken at three dates corresponding to the vegetative, flowering and senescent crop stages. Descriptive and statistical methods were applied to every spatial variable to extract the influence of each component on the sunflower yield variability. Principal components and regression models were used to explore the potential of the multi-spectral variables from the airborne photographs to predict the sunflower yield map at every studied date. Higher sunflower yield was found in areas with lower elevation. These areas were also predominantly free of weed infestation. The Normalized Difference Vegetation Index derived from the image taken at crop vegetative stage was strongly correlated to crop yield. A very poor correlation was detected between the sunflower yield and all the multi-spectral variables studied in the flowering and the senescence crop stages. A map with three zones of yield was predicted with 67.81% of overall accuracy using the stepwise-model equation formed by the green and red bands and the two vegetation indices obtained at vegetative crop stage. The selected multi-spectral data taken in early season (mid-May), plus the additional knowledge of weed presence and field elevation, could provide valuable spatial information to estimate the yield crop variability in the studied fields. This estimation might aid in the development of adequate spatially variable management strategies in the months prior to the sunflower harvest. Precision Agriculture Springer Journals

Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation

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Springer US
Copyright © 2009 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
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