Satellite imagery for precision agriculture: Satamap

Satamap is a web based satellite imagery service for precision agriculture. It’s available at This is a project I am part of so the following is not an independent review, just a quickly written explanation of this innovative app. I understand my audience is fairly schooled in most things precision agriculture so I’ll skip the marketing talk and get straight to the point.

Today we are launching Satamap. This is a brand new service making up to date satellite imagery available to everyone. Our focus is on agriculture, therefore all imagery is paired with a vegetation index called Satamap Vegetation Index (SVI). It is similar to NDVI but we believe it is better at showing variability in high biomass crops and less impacted by soil colour. The colour ramp we use to represent the SVI values, while in your face at first, is designed to show biomass variability in all crops, at all stages of crop growth at all times of year. The colours remain consistent year round so that, for example blue represents the same as blue and red, red no matter which location or time of year. This is important because the Satamap slider allows any two image dates to be laid over the top of the other and the ability to slide between the two for a direct comparison. The same can be done with the standard colour imagery as well.

Satamap screenshot
Satamap screenshot

This service does not require drawing in of paddock boundaries or limit you to a small area of interest. Subscriptions are based on a 3 million plus hectare tile. It takes 5 minutes to subscribe and you have access to the whole area and an archive back to winter 2013. Imagery is captured at a 16 day interval. Cloud can get in the way at times which can be frustrating but we are working on increasing our imagery availability to reduce cloud impacts. The colour imagery has a resolution of 15 m and the SVI is 30 m. We cover all major cropping regions of Australia.

Satamap works best in an iPad or similar tablet device, but functions equally as well on a desktop computer. Other standard features in Satamap include custom markers, area measurement tools, imagery export and GPS location on the map. All these features themselves could warrant an article, but best to just watch the video to see some of them in action.

Satellite imagery has been available to agriculture and related industries for decades and those that have invested the time and money will attest to the value and significance in this technology but admit that all too much the time and money is often the biggest hindrance. We are aiming to solve these problems with Satamap and bring out the potential of satellite imagery for agriculture. Agronomists, grain traders, farmers, suppliers and more can all benefit from rapid, cost effective access to up to date satellite imagery.

We are in constant development. We are working on offering higher resolution imagery, ground truthing data points, exporting with post-processing and more. Currently only available in Australia, very soon we will be opening up to other parts of the world. Thanks for checking in.

Please check it out at

Processing harvest yield data to grid / raster

The process to convert point data to raster (or grid) for can differ dramatically depending on who you ask and the purpose of the conversion. Generally data is processed for two main functions. The first being ‘stacking’ or ‘layering’ to later develop prescription maps or other further processing. The second is improved visual representation of the data. My process aims to satisfy both with minimal processing time. My method does contain some compromises; since I use a coarse resolution it is not as pleasing on the eye as some other methods. In addition, my data smoothing technique is quite broad which means you will loose some detail in the grid.

At this point in time I follow these steps:

  1. Clean up point data either manually manually or with a filter to remove any obvious errors such as where header turns in paddock. EDIT: I am currently trying Yield Editor for this step.
  2. Produce a grid over the top of the point data. Any cells that share a points average the point values.
  3. Gaps in the grid are filled in.
  4. Gaussian Filter is used to ‘smooth’ the data.

In QGIS this is the process:

Note: You will need to have SEXTANTE and additional toolboxes setup to follow these steps. Instructions are available here.

  1. Load paddock boundary (shape file polygon)
  2. Load yield data points (You will need these in shape file format – use export function in SMS or FOViewer if your yield monitor does not produce ESRI shape file)
  3. SEXTANTE > SAGA Toolbox > Shapes – Points > Points Filter
    1. Radius: 100
    2. Minimum Number of Points: 25
    3. Maximum Number of Points: 200
    4. Quadrants: No
    5. Filter Criterion: Remove Below Percentile
    6. Percentile: 15-20
  4. SEXTANTE > SAGA Toolbox > Shapes – Points > Points Filter (use output from step 3)
    1. Radius: 100
    2. Minimum Number of Points: 25
    3. Maximum Number of Points: 200
    4. Quadrants: No
    5. Filter Criterion: Remove Above Percentile
    6. Percentile: 90
  5. SEXTANTE > SAGA Toolbox > Grid – Gridding > Shapes to Grid
    1. Preferred Target Grid Type: Floating Point
    2. Cell Size: 15
    3. Method for Multiple Values: Mean
  6. SEXTANTE > SAGA Toolbox > Grid – Tools > Close Gaps
  7. SEXTANTE > SAGA Toolbox > Shapes – Grid > Clip Grid with Polygon
  8. SEXTANTE > SAGA Toolbox > Gaussian Filter
    1. Standard Deviation: 3
    2. Search Mode: Circle
    3. Search Radius: 50

Make sure you do a visual comparison with original point data to ensure the final grid is a good representation of the original data.

Visually check to see that processed grid file is a fair representation of the original point data.
Visually check to see that processed grid file is a fair representation of the original point data. Note I was lazy in this photo and used un-filtered yield data.

To generate useful color maps to represent the grid data try One day I will explain this process, but it is not too hard to try it yourself.

Now when we get clever we can use the SEXTANTE Modeler to automate this process. More on this later! –Edit: Here is the modeler info.