Crop Forecasting Software
Remote Sensing & Crop Simulation ModelSatFarming is a decision support system linking remote sensing and crop modeling. It evaluates key data in real time to provide an accurate forecast of crop growth and development. Using satellite imagery (Sentinel – ESA), and remote sensing components as important as weather, soil, field data and more, our algorithms calculate and simulate plant growth. Then our crop simulation model presents tangible information the users can easily interpret to optimize farming management and make business decisions.
Satfarming is the first system using both technologies together.
SatFarming Processing
How It Works
Our crop simulation model can be used by farmers or companies for statistical use of the results. It represents a valuable tool to exchange with agricultural technicians to identify problems in the field and to make more accurate business decisions to increase profit. Companies and larger organizations can benefit from the system using crop identification process to forecast counties’ productions.
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Get the graphic and follow the explanations in the video below:
The decision support system is build on two compartments:
- SATELLITE: Watch the crop and evaluate crop biophysical parameters.
- CROP MODEL: Calculate interactions between plant, climate and soil.
Three satellites can be used to scan the field crops: Modis (250m) Landsat (30m) and Sentinel (10m), satellite images are processed through mathematical and physical algorithms to produce:
Leaf Area Index, Leaf Chlorophyll content and Leaf Water Content which are essentials to crop health status estimation.
The crop model is a set of agro-physiological components to calculate phenology (growth stages along the season) and physiology (biomass, leaf area index…).
Remote sensing information is used to update the model results by tuning some parameters (soil, management or model) to match the observed and calculated biophysical parameters.
Once updated, the model is used to forecast agronomic results such as soil water content to manage irrigation schedule, or crop nitrogen nutrition index to evaluate plant’s nutrient needs and make yield predictions.
Weather, Field and Farming Data
What Is Needed to Run the System?
Weather Data
Weather data for the field location at a daily intervals.
Field Soil Information
Soil depth, texture, stone, and organic matter content.
Farming Management Data
Crop variety, sowing density and date, nitrogen inputs and irrigation amounts.
What is calculated?
• GROWTH STAGES: Growth stages are mainly driven by temperature and day length, but some stresses due to frost or drought can generate bias from logical crop calendars. Our calculations are useful to start field interventions which require specific growth stages achieved to be efficient as growth regulator at Zadoks 30 for winter wheat. Also, some fungicides need a specific growth stage to be achieved before being applied.
• BIOMASS: Driven by solar radiation, the photosynthesis process is subject to some brakes due to water and nitrogen stresses. Leaf area index, chlorophyll content and water content are limiting factors in the photosynthesis. Biomass is a good indicator of crop health status if it is too low the yield will be affected and if it is too high then, lodging may occur.
• NITROGEN: Crop nitrogen content information can be derived from biomass and leaf chlorophyll content calculations, which is important data to estimate crop nitrogen needs (Wheat for the second and third input)
• WATER BALANCE: Water content is calculated at daily intervals, knowing the irrigation water return in days, it is possible to anticipate the crop drought occurrence and start irrigation before any yield damage.
• YIELD: It can be affected till the growing season ends, and can be visible sometimes as low biomass but may also be invisible, as kernel weight damages dues to high temperature or drought during grain filling. Along the season, the user can check about yield forecast and adapt its fields inputs and investment strategy to optimize gross margin.