We’ve often shared 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.
Medium or high-resolution vegetation index (VI) maps offer a snapshot of the crop’s variability and status on one day, just like taking your pulse once half-way through a marathon. It gives you a piece of information, and unless it is extreme, you cannot be sure whether you should be concerned – is it good or not: you need to be able to compare it with frequent reliable high-quality measurements. This is what runners do with wearable technology: measuring their pulse every minute or so. This is monitoring.
Today, medium resolution satellite constellations offer more and more snapshots, but the only true high-quality monitoring you can do uses daily measurements at low resolution.
Combining data types (low and medium/high) is the ideal approach in Agriculture and Risk Management, where decisions are based on daily, objective, crop condition information.
There are less than a handful low–resolution satellites providing reliable and consistent daily imagery flow for agriculture globally. As previously stated in our white paper about Satellite Remote Sensing Technology; effective monitoring requires daily revisit capability from the satellite, systematic acquisition over agricultural areas and the ability to download and process all the data from the ground segment.
At Geosys, we use the MODIS Constellation for LRI data.
The constellation is comprised of two satellites – Terra which was launched in 1999, and Aqua which was launched in 2002 – together, they capture the entire Earth every 1 to 2 days in 36 spectral bands.
The spatial resolution varies by band with two bands at 250 m, five bands at 500m and 29 bands at 1km. 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 250m resolution. We aggregate these measurements at different spatial scales, from field to agricultural regions.
Since the Terra satellite captures data of any given point around 10:30 am and the Aqua satellite at 1:30 pm, 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 meters.
Therefore, each pixel represents an area of 250 meters x 250 meters, or 6.25 hectares.
The spatial resolution of MODIS at NADIR (point right below the satellite) equals 250 meters,
but off-NADIR it can be more than 500 meters within the 250 meters x 250 meters pixel.
In this illustration, the ground sampling distance is 250 meters and,
as such, the spatial resolution at NADIR is also 250 meters.
However, looking at the pixels captured away from NADIR,
the spatial resolution of the datacaptured decreases.
Our Customers need more than pixel level measurement, they need answers.
For our Customers who need to monitor large areas, such as states, regions or entire countries for large scale crop condition analysis, in-season and archive low resolution satellite analytics are a great tool to assess the current situation as compared to the historical statistics or past known situations.
The same is true at field level, where you can compare how the crop is growing now in comparison to the last 10 years.
Based on daily Modis low resolution imagery, our algorithm detects field changes, anomalies, weaknesses and alerts our Customers immediately.
Here are 2 examples of how our Customers take advantage of LRI data to improve/support their business. We will provide more examples in future articles.
1. AG-INSURANCE and Sowing Date Estimate
Geosys’ time series analysis enables us to detect the sowing date of crops.
Unlike other companies in the market place that use 8- or even 16-day composite datasets, Geosys’ dataset is built from daily images, integrated every day. This gives our customers finer detail and more accurate data.
Sowing date estimates help our customers in Brazil manage their risk. The Ministry of Agriculture, in Brazil, publishes agronomic recommendations including sowing periods per region and crop type.
These sowing windows are considered as optimal periods with a low risk level for the operation and serve as a contractual requirement for insurers to accept risk when farmers buy crop insurance.
To make it easier for our customers to verify the compliance of sowing dates with the official sowing window, we deliver comprehensive reports with supporting evidence: VI time series that highlight the emergence period of the crop (see Graph below).
Our service alerts insurance companies if fields in their portfolio were planted outside of the window recommended by the Ministry of Agriculture.
This allows the insurance company to take action and adapt crop insurance policy accordingly.
2. BANKS and Monitoring of plant senescence
Geosys analytics are used to detect the moment when crops initiate the senescence stage, just before harvest.
In South America, farmers use their crops as collateral to secure operating loans for their farms.
Lenders closely monitor these farms during the season, particularly during the days prior to harvest to ensure that the debt will be paid.
We deliver a dynamic dashboard to our customers displaying the crop development status for each field registered as collateral. Our algorithm reads the data associated with each field and tags the field with a specific color according to VI values. Fields in red color are entering the senescence phase (see below LRI Map included in the dashboard).
In nuances of red, crops in progressive senescence from early, to late. In nuances of green, crops in vegetative development phase, still not in senescence.
With this level of information in hands, the credit collection team can follow-up with its clients and optimize the logistics to visit them as necessary.
Risk managers can call farmers for more details on their harvest plans to adapt the credit collection strategy.
Our customers expect analytics globally from current, scientific grade data.
Our platform provides analytical data daily from our LRI processing chain. Each day the processing chain adds over 3 billion new measurements into our platform.
Our service level guarantees that we deliver new measurements within 72 hours after acquisition – and our performance is improving every year.
From acquisition to delivery, analytics are available within 72H.
After delivery to Geosys, data goes through several steps of pre-processing in order to select the most relevant data:
- Selection of clear pixels
- Correction of satellite viewing angles to normalize the reflectance
- Daily composition (= selection of the best pixels reading every day)
- Verification of the data and removal of cloud residues
These steps allow for analyzing fine variations in crops health measurement, and making the right assessment of the crop conditions, like in the example below where August night time temperature in 2015 affected crop health during grain filling stage and had a significant impact on the final yield.
2017 started the same as 2015, but cooler August ensured prolonged grain filling time resulting in higher yield.
Our advanced processing system allows us to provide our customers with scientific grade data.
All the steps of the process are important as they enable data comparison, change detection and machine learning.
We’ll be sharing more details very soon about our LRI processing chain.
Advancing Today, Preparing for Tomorrow
Ultimately, LRI processing gives our customers access to key insights to monitor their fields and regions. More than just maps, it offers a daily flow of measurements and information – just a glimpse of the benefits that our customers can expect in the future leveraging EarthDaily.
The EarthDaily constellation will allow us to provide daily monitoring at the sub-field level within hours of the satellite acquisition.
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