Soil Carbon Index

High-resolution soil carbon maps track sequestration rates across regenerative agriculture fields, enabling market verification and optimized carbon credit management.

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How does it work?

Lack of accurate, field-scale soil carbon estimates hinders market entry for regenerative farmers and undermines trust in carbon credits. Our AI-driven geospatial analytics integrate multispectral and soil property data to deliver precise, verifiable soil carbon assessments, streamlining certification and market participation.

High-Resolution Mapping: AI fuses satellite imagery with in-field soil measurements to create accurate, field-scale carbon maps. These maps highlight spatial variability and support targeted regenerative practices.
Automated Assessments: Our algorithms process multispectral and sensor data with minimal manual intervention, delivering precise carbon estimates. This automation reduces labor costs and accelerates certification timelines.
Market-Ready Reporting: Standardized reports comply with major carbon registries and certification bodies. Farmers can directly submit these documents to streamline credit issuance and trading.
Practice Optimization: Correlation analysis links carbon changes to specific regenerative techniques used on each field. This insight guides data-driven decisions and maximizes sequestration returns.
Scalable Integration: The platform handles diverse data sources—satellite, drone, LiDAR, and soil probes—across thousands of hectares. This scalability supports small farms and large enterprise programs alike.
Continuous Monitoring: Regular updates detect seasonal carbon fluxes and long-term trends over multiple years. This continuous tracking ensures audit readiness and adaptive management.

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 carbon estimation compare to laboratory soil sampling?: AI-driven methods offer broader spatial coverage and faster turnaround than point-based lab sampling. While lab analysis remains the gold standard for certain metrics, remote sensing provides cost-effective, high-frequency monitoring across entire fields.
Why is this approach popular among regenerative agriculture stakeholders?: Stakeholders value its ability to deliver transparent, verifiable carbon data that meets market and regulatory requirements. This popularity stems from reduced reporting overhead and enhanced trust in carbon credits.
What alternatives exist for assessing soil carbon in regenerative systems?: Alternatives include manual soil coring with laboratory analysis and handheld spectroscopy devices. These methods can be more labor-intensive or limited in spatial coverage compared to geospatial analytics.
Are there limitations in data accuracy for varied soil types or land cover?: Soil heterogeneity and dense canopy cover can introduce uncertainty in remote estimates, requiring calibration with field samples. Our platform mitigates these issues through machine learning corrections using local ground-truth data.
Can smallholder farms leverage this soil carbon platform effectively?: The platform supports flexible data input scales, from single fields to large estates, making it accessible for smallholder operations. Tiered pricing and streamlined setup minimize technical barriers for smaller users.
How frequently can soil carbon maps be updated, and how does this compare to traditional methods?: Maps can be refreshed as often as new imagery is available, typically monthly or after major field events. Traditional lab-based approaches often require seasonal sampling cycles, making them slower and less responsive to management changes.

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

Ask questions in plain language and the platform responds with charts, visualizations, and next step suggestions.

Speed

GPU-accelerated cloud

Cloud-native architecture spins up on-demand GPU clusters that scale from a single scene to global archives—no manual ops, no bottlenecks.

Data

Any sensor, any format

Optical, SAR, drone, IoT, vector or raster—ingest, fuse, and analyze without conversion headaches.

Visualization

Insight you can see

Real-time 2D / 3D maps and export-ready plots make results clear for engineers, execs, and clients alike.

Boost your productivity. Experience Geospatial AI.

Turn satellite, drone, and sensor data into clear, real-time insights using powerful AI – no complex setup, just answers you can see and act on.

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