Earth Observation (EO) satellites have been essential in tracking various global challenges, including climate change, plastic waste, and the effects of disasters. More recently, their role in agriculture has become increasingly important, helping farmers manage yields, plan irrigation effectively, and swiftly detect threats such as droughts and pest infestations. Let’s delve into the ways EO satellites and spatial analysis support agricultural practices.
How Satellites Help Farmers with Their Work
Satellite monitoring goes beyond just capturing optical images of crops. Remote sensing is a key tool in precision farming, using different sensors to gather data across various parts of the electromagnetic spectrum. These parts, most of which we as people cannot see, give important information about plant health.
A common tool used in crop monitoring is the Normalized Difference Vegetation Index (NDVI). Created in the 1980s, NDVI depicts how plants reflect visible and near-infrared light to evaluate their health. Healthier plants have higher NDVI values, while lower values indicate stress or illness. This information is vital for making fertility maps and helping farmers manage their croplands better. Beyond vegetation, NDVI also helps observe water bodies. NDVI is useful for spotting problems like diseases and lack of water that might not be visible in standard optical imagery.
Studying satellite data provides insights into factors affecting crop production. The effectiveness of satellite monitoring in agriculture relies on comprehensive geospatial data analysis, which is increasingly improved by AI advancements. One company that leverages AI to transform these insights into practical solutions for enhancing farming methods is EOS Data Analytics.
EOS Data Analytics
EOS Data Analytics (EOSDA), founded by Dr. Max Polyakov, stands at the forefront of AI-based satellite monitoring and analytics. The company collaborates with global partners to deliver cutting-edge Earth observation solutions, enabling more intelligent decisions in agriculture and forestry.
By merging satellite imagery with AI, EOSDA provides insights into crop and forest health, fostering sustainable practices. The company aims to leverage satellite technology for swift, accurate, and data-driven decision-making.
EOSDA's flagship product, Crop Monitoring, integrates essential crop data into a single online platform, allowing farmers to oversee fields remotely, identify issues early, and enhance yields while minimizing expenses and environmental impact.
Case Studies
The following case studies explain how satellite-powered technology benefits various businesses worldwide.
Satellite Data Analytics In Agricultural Insurance
Agricultural insurance is particularly vulnerable to climate-induced risks, which frequently result in significant financial losses for both farmers and insurers. To mitigate these risks and guarantee financial stability, reinsurance companies like Der Neue Horizont Re, S.A. (DNHR) in Mexico are turning to satellite-based analytics. DNHR, a reinsurer specializing in agricultural risks, leverages EOSDA Crop Monitoring to validate insurance claims more efficiently and accurately.
Traditionally, DNHR faced challenges in assessing harvest damage caused by weather extremes, such as hail, frost, and drought, due to the resources and time required for manual field inspections. However, by adopting remote sensing technologies, DNHR can now study plant health and environmental conditions remotely. The EOSDA Crop Monitoring platform provides real-time information on vegetation health, soil conditions, and weather patterns, enabling DNHR to quickly assess the extent of damage and determine the necessity of on-site inspections.
The platform's scouting feature allows DNHR to direct field scouts to specific locations, cutting time and costs associated with traditional methods. Analyzing historical data improves understanding of crop performance and forecasts potential losses, speeding up claims validation and enhancing risk assessment accuracy. This data-driven approach leads to better resource allocation and informed decision-making, enabling DNHR to offer superior agricultural insurance services to clients.
Yield Estimation
Cotton farming in Texas faces increasing challenges due to global warming, which affects yields and disrupts planting schedules. To address these challenges, EOS Data Analytics developed an AI-powered yield prediction model tailored specifically for cotton. The project focused on five major cotton-producing counties in Texas, where the team gathered info on crop yield statistics, weather conditions, and crop calendars from 2020 to 2022.
Using the Random Forest Regression model, EOSDA integrated these diverse datasets to predict cotton yields with approximately 80% accuracy. This model was chosen for its efficiency in handling limited information and its ability to generate reliable predictions by analyzing weather patterns, soil conditions, and other environmental factors. The final yield predictions were based on the number of acres and the yield per acre for each field, with results validated through cross-validation methods.
The project demonstrated the model’s effectiveness in forecasting cotton yields, offering significant potential for scalability and further refinement. By automating the remote sensing data collection and analysis process, EOSDA's solution saves time and resources, providing farmers and agribusinesses with a powerful tool to optimize crop management and improve financial planning. This proof of concept not only highlights the potential for accurate yield prediction in cotton farming but also lays the groundwork for future advancements in agricultural forecasting.
Calculating Organic Carbon Content In Soils
In March 2024, the EOSDA team presented their research on soil organic carbon (SOC) content in Ukrainian soils at the ESA Symposium in Italy. The study, led by Lidiia Lelechenko and Dr. Vasyl Cherlinka, focused on assessing SOC levels across Ukrainian farmlands from 2015 to 2020 and predicting future trends using the modified RothC model.
Given the critical role of soil carbon sequestration in combating climate change, the team aimed to provide a comprehensive analysis of SOC dynamics across Ukraine’s vast agricultural landscape. However, the project faced significant challenges due to the scarcity and inaccessibility of high-quality soil information. The researchers relied on a combination of available datasets, including information from the National Agriculture Land Degradation Neutrality Monitoring platform, which provided the most promising source of information.
Using the Random Forest algorithm, the team created spatial models to map SOC levels, focusing on the 0–30 cm plow layer. These models were used to generate SOC maps for each year in the study period, tracking changes over time and assessing sequestration potential. Despite data limitations, the project successfully produced detailed SOC maps, highlighting significant potential for future carbon forecasting.
This research not only provides valuable insights into soil health in Ukraine but also sets the stage for further advancements in SOC modeling, particularly as more high-quality information becomes available. The findings are crucial for informing sustainable land management practices and enhancing Ukraine's agricultural resilience in the face of climatic changes.
Precision agriculture is ushering in an era of smarter, more efficient farming, where increased yields and reduced waste are becoming the norm. Just as the plow and harvester revolutionized farming, satellite technology is now indispensable. EOSDA’s remote sensing services are key to this transformation, helping farmers optimize operations and achieve productive, sustainable agriculture globally.