Access to Planet imagery became simpler, faster, and more affordable with GeoPard Agriculture. Since August 2022 GeoPard has released the capabilities to search and analyze only requested Planet images from the user’s preferred date range.
So a GeoPard user requests only preferred Planet images and can use them in GeoPard analytical toolbox.
Planet images extend Sentinel and Landsat coverages (provided by default) and can be mixed with other data layers (harvesting/spraying/seeding machinery datasets, topography profile) via existing Multi-Layer, Multi-Year, and Equation tools.
Planet is the largest earth observation satellite network delivering a near-daily global dataset and enables its high-resolution and high-frequency satellite imagery data.
With the release of the Equation-based analytics module, the GeoPard team has taken a big step forward in empowering farmers, agronomists, and spatial data analysts with actionable insights for each square meter. The module includes a catalog of over 50 predefined GeoPard formulas that cover a wide range of agriculture-related analytics.
The formulas have been developed based on multi-year independent agronomic university and industry research and have been rigorously tested to ensure their accuracy and usefulness. They can be easily configured to be executed automatically for any field, providing users with powerful and reliable insights that can help them to optimize their crop yields and reduce input costs.
The Equation-based analytics module is a core feature of the GeoPard platform, providing users with a powerful tool to gain a deeper understanding of their operations and make data-driven decisions about their farming practices. With the ever-growing catalog of formulas and the ability to customize formulas for different field scenarios. The GeoPard can meet the specific needs of any farming operation.
Potassium Removal based on Yield data
Use Cases (see examples below):
Nitrogen Uptake in absolute numbers using Yield and Protein data
Change detection and alerting (calculate difference between Sentinel-2, Landsat8-9 or Planet imagery)
Soil and grain moisture modeling
Calculation of dry yield out of wet yield datasets
Target Rx vs As-applied maps difference calculation
Potassium Recommendations based on Two Yield Targets (Productivity Zones)
Fertilizer: Recommendations Guide (South Dakota State University): Potassium / Corn. Review and Revision: Jason Clark | Assistant Professor & SDSU Extension Soil Fertility Specialist
Potassium Use Efficiency in Kg/Ha
Nitrogen Use Efficiency in percentage. Calculation is based on Yield, Protein and Grain Moisture data layers
Nitrogen: Target Rx vs As-Applied
Chlorophyll difference between two satellite images
A user of GeoPard can adjust existing and create their private formulas based on Imagery, Soil, Yield, Topography, or any other data layers GeoPard supports.
Examples of the template GeoPard Equations
Formula-based analytics helps farmers, agronomists, and data scientists to automate their workflows and make decisions based on multiple data and scientific research to enable easier implementation of sustainable and precision agriculture.
GeoPard extends the family of supported chlorophyll-linked vegetation indices with – Canopy Chlorophyll Content Index (CCCI) – Modified Chlorophyll Absorption Ratio Index (MCARI) – Transformed Chlorophyll Absorption in Reflectance Index (TCARI) – ratio MCARI/OSAVI – ratio TCARI/OSAVI
They help to understand the current crop development stage including – identification of the areas with nutrient demand, – estimation of the nitrogen removal, – potential yield evaluation,
And the insights are used for precise Nitrogen Variable Rate Application maps creation.
Canopy Chlorophyll Content Index (CCCI) vs Modified Chlorophyll Absorption Ratio Index (MCARI) vs Transformed Chlorophyll Absorption in Reflectance Index (TCARI) vs Ratio MCARI/OSAVI
GeoPard team introduces the Normalized Difference Moisture Index (NDMI). The index determines vegetation water content. It is useful for finding the spots with existing water stress in plants.
The difference of the vegetation relative water content between two satellite images (Sentinel-2 constellation in this case)
Lower NDMI values mark the spots where the plants are under stress from insufficient moisture. On the other side, lower NDMI values following the vegetation peak highlight the spots that are becoming ready for harvesting first.
In the following screenshots, you can find the NDMI zones generated based on June 19 (vegetation peak) and July 6 satellite images and the equation map representing the NDMI difference.
Normalized Difference Moisture Index calculated on top of Planet / Sentinel-2 / Landsat image
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