Remote sensing and satellite imagery data is being used around the globe by the agricultural industry to:
Make decisions, Understand changes, and Estimate future conditions.
One can use these spatial data to forecast: changes to conditions under different management strategies | climate scenarios | market pressures.
This knowledge of can be combined with satellite data to tell a complete history of the farm and give confidence to investors in the capacity of the operation.
Keep track of: conditions, monitor growth, weather & carbon with newfound ease and surprising accuracy
Satellite imagery is changing the way business is done in the agricultural sector. Managers, researchers, and investors now all have access to data that can improve decision making and push the industry to a place of better soil health, more efficient production, and happier farmers.
NVDI Images give a measure of vegetation type, amount, and condition on land surfaces.
NDVI values can be averaged over time to establish “normal” growing conditions in a region for a given time of year. Further analysis can then characterize the health of vegetation in that place relative to the norm.
When analysed through time, NDVI can reveal where vegetation is thriving and where it is under stress, as well as changes in vegetation due to human activities such as deforestation, natural disturbances such as wild fires, or changes in plants’ phenological stage.
The Normalized Difference Moisture Index (NDMI) detects moisture levels in vegetation using a combination of near-infrared (NIR) and short-wave infrared (SWIR) spectral bands.
It is a reliable indicator of water stress in crops.
Severe drought conditions not only stress the crops but can destroy the entire yield.
NDMI can detect water stress at an early stage, before the problem has gone out of hand.
Further, using NDMI to monitor irrigation especially in areas where crops require more water than nature can supply, helps to significantly improve crop growth.
All of this makes NDMI an excellent farm tool.
Productivity zones maps provide information to know the variability within a given field. Based on this knowledge, field visits can be carried out, making decisions regarding field management, management zones, selecting areas for better suited crops, etc.
Productivity zones maps are used to compare variability within fields.
Using productivity zones to define sampling points makes possible to review on site the different zones.
It would either be soil chemical or soil physical variations
This can be a starting point to delineating management zones, define inputs and recommendations, and selecting the type and quantity of inputs according to the management zones.
By helping you to understand variability, Productivity maps can support different tasks:
Remote sensing and satellite imagery data is being used around the globe by the agricultural industry to:
One can use these spatial data to forecast: changes to conditions under different management strategies | climate scenarios | market pressures.
This knowledge of can be combined with satellite data to tell a complete history of the farm and give confidence to investors in the capacity of the operation.
Keep track of: conditions, monitor growth, weather & carbon with newfound ease and surprising accuracy
Satellite imagery is changing the way business is done in the agricultural sector. Managers, researchers, and investors now all have access to data that can improve decision making and push the industry to a place of better soil health, more efficient production, and happier farmers.
NDVI values can be averaged over time to establish “normal” growing conditions in a region for a given time of year. Further analysis can then characterize the health of vegetation in that place relative to the norm.
When analysed through time, NDVI can reveal where vegetation is thriving and where it is under stress, as well as changes in vegetation due to human activities such as deforestation, natural disturbances such as wild fires, or changes in plants’ phenological stage.
Productivity zones maps provide information to know the variability within a given field. Based on this knowledge, field visits can be carried out, making decisions regarding field management, management zones, selecting areas for better suited crops, etc.
Productivity zones maps are used to compare variability within fields.
Using productivity zones to define sampling points makes possible to review on site the different zones.
It would either be soil chemical or soil physical variations
This can be a starting point to delineating management zones, define inputs and recommendations, and selecting the type and quantity of inputs according to the management zones.
By helping you to understand variability, Productivity maps can support different tasks: