Abstract
Abstract. Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.
Highlights
Agriculture is an important sector in the global economy and is a crucial component for fighting hunger and food insecurity
New satellite systems have emerged in the past five years that have high spatial, temporal (PlanetScope), and spectral resolution (Sentinel-2) that have the potential to revolutionize the field of precision agriculture by aiding decision making
While previous studies on modelling yield prediction used Landsat (Song et al 2016) and Sentinel-2 (Al-Gaadi et al 2016), this paper offers a new approach utilizing high spatial and temporal resolution PlanetScope (3 m, daily) analytical scenes in comparison with Sentinel-2A (20 m, 10 days) to monitor potato crop health over the 2017 growing season and predict crop yield
Summary
Agriculture is an important sector in the global economy and is a crucial component for fighting hunger and food insecurity. New satellite systems have emerged in the past five years that have high spatial, temporal (PlanetScope), and spectral resolution (Sentinel-2) that have the potential to revolutionize the field of precision agriculture by aiding decision making. These advances in satellite imagery provide a valuable resource for crop monitoring and yield modeling and prediction. The most common vegetation indices used for forecasting crop yield status are NDVI, Green Normalized Difference Vegetation Index (GNDVI), SAVI and Modified Soil Adjusted Vegetation Index 2 (MSAVI2) These indices were all tested and compared to potato yield data from ten fields (402.98 ha) in an ordinary. Upon considering the offset in the growing seasons between Idaho and Lebanon as well as the potato varieties, the adjusted Idaho model was applied to forecast potato yield in Lebanon
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