Satellite remote sensing of vegetation cover and nitrogen status in almond
Demand for nitrogen in almond is directly dependent on tree growth and yield. Knowledge of nitrogen status is important in optimising nutrient management, minimising input costs and avoiding environmental pollution. Optimal nitrogen management requires knowledge of vegetation cover, tree nitrogen status and yield. Fertiliser recommendations for mature almond crops are ~300 kg N ha-1 year-1. Immature crops generally receive half the nutritional inputs of mature trees. Currently, the best practice method of monitoring nitrogen status involves leaf sampling, laboratory analysis and agronomic interpretation. Satellite-based estimates of nitrogen status for potential application to orchard fertiliser programs was sought to overcome existing limitations of cost and sample size, and to account for effects of vegetation cover. Satellite-based estimates of vegetation cover (Normalised Difference Vegetation Index, NDVI) and nitrogen status (Canopy Chlorophyll Content Index, CCCI) in almond crops grown in the Sunraysia Irrigation Region of SE Australia were determined during the 2010/2011 and 2011/2012 growing seasons. RapidEye imagery proved useful in deriving multi-temporal and multi-locational crop data. High temporal- and spatial-variability in NDVI was observed in mature crops. Large variability in vegetation cover between fields has implications for orchard management including potential yield and nutrient requirement. A 100-fold range in CCCI between fields suggests large diversity in nitrogen requirements. The above findings will contribute to regional biophysical benchmarking of nitrogen status in tree crops using remote sensing approaches. Further work is required to develop relationships between CCCI and measures of leaf nitrogen to guide fertiliser management in almond.
O¿Connell, M., Whitfield, D. and Abuzar, M. (2016). Satellite remote sensing of vegetation cover and nitrogen status in almond. Acta Hortic. 1130, 559-566
NDVI, CCCI, RapidEye, near-infrared imagery, red-edge imagery, spatial variability, temporal variability