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Low-Resolution Imagery: From Acquisition to Analytics

We often share information about our medium-resolution imagery data sources and processing chain, as it provides key insights for those who need analytic data to understand field variability and make decisions. Both medium- and high-resolution vegetation index (VI) maps offer a snapshot of the crop’s variability and status on that day. And while today, medium resolution satellite constellations offer more and more snapshots, the true, high-quality monitoring can only be done using daily measurements at low-resolution.

Imagine you’re running a marathon and take your pulse at the half-way point. While this data point is valuable, it only provides one piece of information. Unless it is extreme, it would be hard to gather any insights from the data unless you were able to compare it to a reliable source of frequent, high- quality measurements. As such, many runners chose to use wearable technology to allow a regular measurement of their pulse – this is monitoring.

Monitoring Crops

We often share information about our medium-resolution imagery data sources and processing chain, as it provides key insights for those who need analytic data to understand field variability and make decisions. Both medium- and high-resolution vegetation index (VI) maps offer a snapshot of the crop’s variability and status on that day. And while today, medium resolution satellite constellations offer more and more snapshots, the true, high-quality monitoring can only be done using daily measurements at low- resolution.

Combining data types (low and medium/high) is the ideal approach in farm management to empower decisions based on daily, objective, crop condition information.

Few low-resolution satellites provide reliable and consistent daily imagery flow for agriculture globally. As discussed in Understanding and Evaluating Satellite Remote Sensing Technology for Agriculture, effective monitoring requires daily revisit capability, systematic acquisitions, and the ability to download and process data from the ground segment.

At Geosys, we use the MODIS Constellation for low-resolution imagery (LRI) data. The MODIS constellation is comprised of two satellites, Terra (launched in 1999) and Aqua (launched in 2002). Together, they capture the entire Earth every 1-2 days in 36 spectral bands.

The spatial resolution varies by band: two bands at 250 m; five bands at 500 m; and 29 bands at 1 km. This data is available for any field globally, for more than 10 years.

Our primary use for MODIS data is in the development of daily vegetation index (VI) time series at 250 m resolution. We aggregate these measurements at different spatial scales, from field level to agricultural regions.

The Terra satellite captures data of any given point around 10:30 am and the Aqua satellite at 1:30 pm, so we generally have two sets of data each day. Based on the angle of view and cloud coverage, we pick the best measurement for each pixel – or area of interest (AOI) – after downloading the full tiles.

 
 
 

 

MODIS images have a ground sampling distance (GSD) of 250 m. Therefore, each pixel represents an area of 250 m x 250 m, or 6.25 hectares.

The spatial resolution of MODIS at NADIR (point right below the satellite) equals 250 m,

but off-NADIR it can be more than 500 m within the 250 square meter pixel.

Based on daily Modis low-resolution imagery, our algorithm detects field changes, anomalies, weaknesses and alerts our Customers immediately.

When monitoring crops at field-level, both in- season and historical low-resolution satellite analytics are great tools for assessing the current situation. Accessing 10 years of data enables comparisons to historical statistics or known situations from previous years.

Providing current analytics using only scientific-grade data

Our platforms provide daily analytical data from our LRI processing chain and each day the processing chain adds more than 3 billion new measurements. Because we understand that timing is everything, Geosys guarantees delivery of new measurements within 72 hours after acquisition – and our performance is improving every year.

After acquisition by Geosys, data goes through several steps of pre-processing in order to select the most relevant data based on:

  • Selection of clear pixels
  • Correction of satellite viewing angles to normalize the reflectance
  • Selection of the best pixels between the Terra and Aqua acquisitions each day
  • Verification of the data and removal of cloud residues

All the steps of the process are important as they enable data comparison, change detection and machine learning. This process enables analysis of fine variations in crops health measurement to allow for more accurate assessments of the crop conditions.

In this example, the Croptical® platform was able to see the affects and management of frosted crops.

Farm Location: Howlong, Victoria in Australia

Frost event: 29th of August

Management of Frost: Cut Canola for Silage and cereal crops for hay. Left sections of the field not as affected by frost to harvest for grain.

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Advancing today, preparing for tomorrow

Ultimately, LRI processing gives our customers access to key insights to monitor their fields. More than just maps, we offer a daily flow of measurements and information – giving you just a glimpse of the benefits our customers can expect in the future by leveraging the EarthDaily constellation. This new constellation will allow Geosys to provide daily monitoring at the sub-field level within hours of the satellite acquisition.

 

Learn more about our technology and our ready to use API and Analytics, or contact us for more information. Follow Geosys on LinkedIn or Twitter for even more insights.

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