The Role of Earth Observation in Supporting Regenerative Agriculture Adaptation

With global population growth on the horizon, increasing food production in a sustainable way is becoming a pressing challenge. In the recent past, regenerative agriculture has emerged as a beacon of hope for sustainable food systems. This farming approach prioritises soil health, biodiversity, and carbon sequestration, among other benefits. However, measuring its impact and ensuring widespread adoption requires the interplay of various technologies. This is where Earth Observation (EO) and Machine Learning (ML) play a crucial role.

As part of the Agriculture 4.0 movement, an evolution from traditional precision farming to data-driven, connected systems, EO and ML enable smarter, more efficient farm monitoring. EO platforms such as satellites and drones enable near real-time monitoring of large extents of farmland, while ML models are used to process large amounts of data and capture details that may not be obvious to the human eye. Together, these tools provide useful intelligence for farmers, policymakers, and researchers to help optimise regenerative strategies across different ecosystems.

Components of regenerative agriculture  (Credit: Horus Impact)

Healthy soils rich in organic matter are capable of sequestering carbon and are the foundation of regenerative agriculture. EO technologies, such as satellite-based radar and spectral imaging, help assess soil-related factors such as composition, moisture levels, and organic carbon content that facilitate the monitoring and enhancement of soil health. ML models can then be used to analyse this data, track changes over time, and assess the impact of farm management practices such as cover cropping, crop rotation, and reduced tillage.

Satellite and ML-based crop type detection service (Credit: KappaZeta )

Beyond soil health, regenerative agriculture enhances biodiversity by incorporating a range of crops, trees, livestock, pollinators, and soil. With satellite imagery and ML-driven classification techniques, researchers can track land cover changes, measure biodiversity, and detect shifts in vegetation patterns. These initiatives can help determine whether farming systems are moving toward more resilient, ecologically rich landscapes.

Water efficiency is another key element of regenerative farming, especially in the face of climate uncertainty. Radar-based EO can detect soil moisture levels, track irrigation patterns, and predict drought conditions. ML-powered analysis turns this data into smart water management strategies, helping farmers make better use of limited resources.

In addition to soil health, biodiversity, and water, regenerative practices promote healthier plants by improving soil biology and reducing reliance on chemical inputs. AI-based plant stress detection models can be used in analysing satellite imagery to detect early signs of nutrient deficiencies, pest outbreaks, and disease risks, allowing farmers to take timely corrective action and reduce reliance on synthetic treatments.

3-D SAR mission concept (Credit: KappaZeta)

One of the greatest challenges in regenerative agriculture is demonstrating its climate benefits in measurable terms. ML and EO together can be used to estimate carbon sequestration and reductions in greenhouse gas emissions. These metrics not only validate the environmental impact of regenerative farming but also help farmers access carbon credit markets and financial incentives, making sustainability both a viable and rewarding quest.

Copernicus expansion missions (Credit: ESA)

Policy frameworks such as the Farm to Fork Strategy and the European Green Deal support the implementation of regenerative agricultural practices. Additionally, upcoming satellite missions will further enhance the monitoring of these practices. One such initiative is led by KappaZeta, a company dedicated to launching the 3D-SAR, a mission designed to transform Sentinel-1 satellite data into three-dimensional data to aid in measuring above-ground biomass.

In conclusion, the combination of EO and ML has made regenerative agriculture more trackable, scalable, and financially attractive. However, to accelerate its adoption, we need greater investment in open-access satellite and field-level validation data, digital advisory tools, and policy frameworks that reward sustainable land management. By embracing technology, we can restore ecosystems, secure our food supply, and fight climate-related challenges. The future of farming is regenerative, and with ML and EO, we can track it every step of the way.

About the Author

Catherine Akinyi Odera is an Earth Observation Project Manager at KappaZeta, where she oversees projects focusing on the use of Earth Observation (EO) and Machine Learning (ML) to address agricultural and climate-related challenges.

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