Modern farming practices incorporate the identification, analysis, and management of spatial and temporal variability within fields for optimum profitability, sustainability, and protection of the environment. Hyperspectral instruments can substantially support precision farming, providing agricultural information more accurately and in more detail than existing operational multispectral sensors. 

 

 

 

The following major scientific and application tasks have been identified in agriculture:

• Improvement and development of accurate, robust and reliable crop parameter retrieval methodologies using HSI data (methodologies based on optical CR models for the retrieval of crop type, LAI, APAR, chlorophyll content, PRI, plant water content, canopy geometrical structure)

• Improvement and adaptation of crop production models to allow the assimilation of canopy parameters retrieved from HIS

• Assimilation of hyperspectral remote sensing information into agro-meteorological models to determine the cause of crop condition variability

• Improved discrimination of crop stress caused by nitrogen deficiency, crop disease, insect infestation, water stress, and chlorosis

• Development of operational methodologies for yield estimation and yield forecasting based on EnMAP and ancillary data.