After adding your field, you can see a satellite image of it. You can switch the view by clicking on the tile in the lower right corner. You can choose between different satellite maps and select different biomass maps to display the in-field variability.
Which satellite map should I use?
You can choose between two types of satellite maps:
This high resolution but outdated earth map is a useful option to identify field boundaries. The base map offers the possibility to navigate on fields and get high resolution on field boundaries specially to detect creeks and roads, etc.
These low resolution but up to date satellite images are a useful to check the field condition. The satellite images are taken every 3 - 5 days by our satellites. By checking various timelines, you can observe crop development of a field. Clouds can be detected easily through field view images due to its cloud detection algorithm. As clouds can interfere with creating variable rate application maps, this feature is giving users an option to check unclouded images.
To show cloudy satellite images, click 'Show cloudy days' in the lower right corner.
You can now select a satellite image with clouds by clicking on the satellite image displayed in the timeline. Satellite images containing clouds, are marked with a cloud icon.
A satellite image with clouds will look like this:
Which biomass map should I use?
You can choose between three types of maps to view your in-field variability:
Normalized Difference Vegetation Index (NDVI) map
The NDVI map uses the NDVI index to display nitrogen deficiency on targeted vegetation. The NDVI index is shown on a fixed green colour scale from very low biomass to maximum biomass. Dark green areas are representing higher nitrogen availability while light green areas are low in nitrogen. With the help of NDVI maps, a difference between healthy and stressed vegetation can be quantified. However, this feature has a limitation. Especially when crops are growing bigger and the canopy is saturated (highest biomass), the NDVI index saturates, and you can no longer recognise differences between highest and lowest growth within a field. For this reason, it is recommended to use the optimized map to estimate the nitrogen uptake at later growth stages.
The basis for the optimized map is the data obtained from several years of use of the Yara N-Sensor in the field. The optimized map is not sensitive to various growth stages or canopy saturation. The optimised index is not prone to saturate as fast as the NDVI and has a better correlation to nitrogen uptake. The nitrogen uptake can be estimated for a better vegetation monitoring. The optimized map allows you not only to analyse crops through the whole growth cycle but also across seasons. The colour scales are adjusted on lowest and highest index values in a satellite image. Low biomass is represented by brown tones. A variety of green shades is representing the canopy evolvement. As the canopy evolves, the colour changes to blue and purple shades. This map is useful to identify differences in vegetation growth on fields.
The N-uptake map indicates the plants' absolute nitrogen uptake on a specific day. The absolute in-field nitrogen uptake (in kg N/ha) is calculated from cloud free satellite images.
The map shows day specific absolute N-uptake, where each pixel is associated with an N-uptake value. This makes the N-uptake map ideal for identifying nitrogen uptake variation within a field.
The N-uptake map is currently available for various crops. Namely, for cotton, durum wheat, maize, oats, oil seed rape, potatoes, spring barley, spring wheat, sugarcane, triticale, winter barley, winter rye, winter wheat, and grassland. More crops will be added in the upcoming months.
The legend makes use of a colour scale from yellow to dark green and visualises a dynamic scale. Yellow represents the lowest kg N/ha, which is -30 of the daily average, while dark green represents the highest amount of kg N/ha, which is +30 of the daily average. The map therefore shows a total variance of 60 kg/ha within a field.