Process Flow

Accurately mapping cropland and estimating N₂O emission-reduction potential is crucial for understanding the role of agriculture in mitigating climate change. Carbontribe leverages computer vision models and remote sensing technologies to classify cropland and non-cropland areas, monitor critical agricultural parameters, and estimate annual emission reductions. This methodology integrates high-resolution satellite imagery, machine learning algorithms, and ecological equations to provide a comprehensive, scalable solution for cropland monitoring and emissions accounting.

Step 1: Data Acquisition and Preprocessing

• Satellite imagery is collected from various sources such as remote sensing platforms.
• The collected data undergo preprocessing, including noise reduction, radiometric correction, cloud detection and removal, and geographic alignment, to ensure compatibility with the computer vision models.

Step 2: Cropland Classification Using Computer Vision

• A computer vision model is trained to classify regions as cropland or non-cropland. Training data includes labeled examples of both categories.
• The model processes high-resolution images to identify cropland areas based on spectral, textural, and structural features.
• The output is a classified map highlighting cropland and non-cropland regions with pixel-level accuracy.

Step 3: Monitoring Key Cropland Parameters

• For areas classified as cropland, the monitoring process aligns with IPCC guidelines to estimate nitrogen flow and N₂O accurately. More detailed information on what parameters and how exactly we conduct the monitoring can be found in the following chapters.
• Wherever possible, further literature reviews will be conducted to obtain detailed information such as country-specific factors, fertiliser type-specific emission equations, and regional emission factors. This ensures that monitoring results are both scientifically robust and tailored to local ecological contexts.
• These parameters are monitored over time at the specified frequency to track changes and trends in agricultural activity.

Step 4: N₂O Reduction Estimation

• Using the monitored parameters, relevant equations or estimation models are applied to infer N₂O reduction.

Step 5: Blockchain Storage

• All relevant data, including cropland classification outputs, monitored parameters, and N₂O reduction estimates, are securely stored on a decentralized blockchain platform.
• Data links, metadata, and timestamps are recorded to ensure traceability and tamper-proof documentation.

Step 6: Digital Asset Creation

• A digital asset in the form of a Non-Fungible Token (NFT) is created to represent the emission-reduction results for a specific geographic area and time period.
• The NFT includes embedded data or links to external datasets, including: detected cropland areas; parameter estimates and methods; scientific equations and values used in the calculations; and documentation on compliance with IPCC methodologies and any country-specific factors.
• The blockchain-stored data and NFT serve as a transparent and auditable foundation for issuing carbon credits.
• This process ensures that credits are backed by scientifically verified data, reducing the risk of fraudulent claims.