A Collaborative Platform for Soil Organic Carbon Inference Based on Spatiotemporal Remote Sensing Data
Jose Manuel Aroca-Fernandez, Jose Francisco Diez-Pastor, Pedro Latorre-Carmona, Victor Elvira, Gustau Camps-Valls, Rodrigo Pascual, Cesar Garcia-Osorio
TL;DR
WALGREEN presents a cloud-based platform to infer and map soil organic carbon (SOC) by integrating spatiotemporal remote sensing data (Landsat, Sentinel) with in situ measurements via Google Earth Engine and Sentinel Hub. Built on an MVC architecture with Java/Spring Boot and a Python Flask API, it provides end-to-end SOC inference through CSV data handling, map visualization, and ML predictors, including Kalman/EM-based temporal data filling. The approach leverages public datasets (e.g., LUCAS) to train predictors and supports SaaS deployment for researchers, policymakers, and land managers, aiming to democratize access to SOC information and support sustainable land management. Future work includes adding more advanced regression and data assimilation methods to enhance predictive accuracy and temporal robustness, with practical impact on climate-smart agriculture and ecosystem resilience.
Abstract
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.