Over time, agronomists can become adept at examining the most recent satellite images of a paddock with a familiar spectral index and color scale applied, extracting a large amount of information very quickly. This tool enhances human productivity and helps catch issues that might otherwise go unnoticed. From a typical ground-level inspection, an agronomist may only see 20% of a field, making it difficult to detect variability in crop health. Imagery provides a bird’s eye view and allows for targeted in-field inspections. For effective crop monitoring and scouting, consider the following process:
1. Understand the Paddock History
Many paddocks farmed today were once several smaller paddocks. Old boundaries can sometimes be identified even 10+ years after they were combined. Similarly, if a paddock was split in half for a single season, this division can often be seen in subsequent crops. Knowing the paddock's history is crucial as it affects overall variability, which will become evident in the next step.
2. Investigate for ‘Normal’ Variability
It's essential to understand the ‘normal’ variability in a paddock. By examining several years of satellite imagery, one can identify common trends that persist season to season. With access to over 5 years of Sentinel 2 data and almost 10 years of Landsat 8 data, it is possible to manually analyze this variability, despite the existence of automated services. This manual process is particularly useful for fields previously not studied in-depth through imagery.
The variability can differ based on the season type, crop type, and management practices. For example, low-lying areas prone to waterlogging during wet seasons may produce low yields but could be high-yielding in dry years. There is often consistency in how crop growth variability manifests across different seasons.
- Beyond Peak Biomass: Don’t only rely on images at peak biomass, as uniform appearance may mask underlying variability. Earlier imagery may reveal more variation.
- Color Scale Understanding: Be aware of how software visualizes imagery—whether using a static or dynamic color scale. Consistency in comparing data is key.
- Causes of Variability: Investigate the reasons for variability, using additional imagery sources like Google Earth for bare earth images, to see if patterns align with soil color trends.
- Normal vs. Acceptable: Some 'normal' variability may be undesirable, such as consistent weed infestations.
- Fence Line Analysis: Examine if variability patterns continue across fences, indicating external factors.
3. Inspect the Latest Image
With an understanding of normal variability, focus on spotting anomalies—areas deviating from the norm. These anomalies, whether higher or lower in values, need to be explained using field knowledge and satellite data, then verified on the ground.
Common anomalies detected with Sentinel 2 or Landsat 8 using NDVI or similar indices include:
- Weed infestations (higher NDVI values)
- Spray drift
- Waterlogging
- Hail damage
- Large areas of disease (e.g., ascochyta blight)
- Feral pig damage
- Nutrition deficiencies
Target specific areas for inspection, such as low, mid, and high plant growth areas, to make informed decisions on desiccation, insect pressure, and harvest timing. Pay attention to known trial or accidental trial areas for unexpected results.
4. Consider Timed Difference / Change Maps
Change maps, which show the difference between images from different dates, provide valuable insights into crop development. For example, a 10-day difference map can highlight changes in crop health or biomass, helping to understand crop growth patterns over time.
5. Plan Your Next Trip to the Paddock
Armed with satellite imagery insights, visit the paddock to verify anomalies and investigate areas of interest. While imagery doesn’t capture every issue, it significantly enhances the scouting process.
Other Considerations and Tips:
- Imagery may occasionally show artifacts or inaccuracies, such as cloud shadows affecting NDVI values.
- Some in-crop sprays can affect leaf angle, altering spectral index readings (e.g., Tordon 242 in wheat and barley).
- Explore other spectral indices beyond NDVI, such as MCARI2, which may provide better differentiation in high biomass crops.