Soil Health Track

High-resolution soil property maps reveal organic carbon, moisture, and nutrient levels across fields, enabling data-driven regenerative management and long-term resilience.

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How does it work?

Conventional soil tests are labor-intensive and infrequent, offering limited spatial and temporal resolution. AI-driven geospatial analytics enable continuous, high-resolution monitoring of soil health indicators, supporting adaptive regenerative farming decisions.

High-Resolution Mapping: Generate detailed soil property maps at field scale using satellite imagery and in-field sensors. This spatial granularity uncovers patterns of organic carbon and moisture variability for targeted interventions.
Organic Carbon Trends: Track changes in soil organic carbon over time to assess the impact of cover cropping and amendments. Consistent monitoring supports verification of regenerative practices and carbon sequestration goals.
Moisture Variability: Monitor soil moisture dynamics across different zones to optimize irrigation and water conservation. Real-time insights help prevent both drought stress and waterlogging.
Nutrient Optimization: Map nutrient levels such as nitrogen and phosphorus to guide precise fertilizer applications. This reduces input costs and mitigates environmental runoff.
Practice Evaluation: Evaluate the effectiveness of regenerative practices—like reduced tillage or crop rotations—by comparing soil health metrics before and after implementation. Data-driven feedback helps refine management strategies.
Seamless Integration: Integrate soil health data with farm management systems and GIS platforms for cohesive decision support. Automated analytics streamline reporting and compliance with sustainability standards.

Frequently asked questions

Have a different question and can’t find the answer you’re looking for? Reach out to our support team by sending us an email and we’ll get back to you as soon as we can.

How does AI-based soil health monitoring compare to traditional soil testing?: Traditional soil testing relies on periodic manual samples and lab analysis, resulting in low temporal and spatial resolution. AI-driven mapping uses satellite and in-field sensors to deliver continuous, high-resolution insights across entire fields.
How does this approach differ from crop health remote sensing?: Crop remote sensing focuses on canopy reflectance and plant status, while soil health monitoring targets subsurface properties like organic carbon and moisture. This geospatial analytics use-case integrates soil metrics to guide long-term regenerative practices beyond immediate crop performance.
Why is soil health monitoring popular in regenerative agriculture?: Regenerative farmers prioritize building soil organic matter and ecosystem resilience, which requires timely feedback on soil conditions. Continuous monitoring supports adaptive management of cover crops, tillage, and amendments to achieve soil health goals.
What are the limitations of AI-driven soil monitoring?: Accuracy depends on the quality and resolution of input data sources such as satellite imagery and ground sensors. Extremely heterogeneous fields or dense canopy cover can reduce the reliability of remote estimates, requiring supplementary in-situ validation.
Can I use this platform alongside manual soil probes?: Yes, manual sampling data can be integrated to calibrate and validate model predictions, improving overall accuracy. This hybrid approach leverages both in-field measurements and geospatial analytics to refine soil health assessments.
What alternative methods exist for monitoring soil carbon?: Alternative methods include laboratory analysis of soil core samples and spectroscopic sensors mounted on field equipment. While these offer precise point measurements, they lack the scalability and frequency provided by AI-based geospatial analytics.

Geospatial AI Platform

Intelligence

AI & foundation models

Deep-learning and foundation models turn raw imagery into ready-to-use insights, so you ship answers instead of training pipelines.

Experience

Conversational workflow

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Data

Any sensor, any format

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