A Collaborative Platform for Soil Organic Carbon Inference Based on Spatiotemporal Remote Sensing Data
This work addresses the problem of monitoring soil health and carbon sequestration for researchers, policymakers, and land managers, but it appears incremental as it builds on existing technologies and methods.
The paper tackles the challenge of large-scale soil organic carbon monitoring by presenting WALGREEN, a collaborative platform that enhances SOC inference using machine learning and diverse soil samples, resulting in a user-friendly tool for accessing carbon data and analyzing trends.
Soil organic carbon (SOC) is a key indicator of soil health, fertility, and carbon sequestration, making it essential for sustainable land management and climate change mitigation. However, large-scale SOC monitoring remains challenging due to spatial variability, temporal dynamics, and multiple influencing factors. We present WALGREEN, a platform that enhances SOC inference by overcoming limitations of current applications. Leveraging machine learning and diverse soil samples, WALGREEN generates predictive models using historical public and private data. Built on cloud-based technologies, it offers a user-friendly interface for researchers, policymakers, and land managers to access carbon data, analyze trends, and support evidence-based decision-making. Implemented in Python, Java, and JavaScript, WALGREEN integrates Google Earth Engine and Sentinel Copernicus via scripting, OpenLayers, and Thymeleaf in a Model-View-Controller framework. This paper aims to advance soil science, promote sustainable agriculture, and drive critical ecosystem responses to climate change.