基于遥感数据的土壤有机碳智能推演平台，助力精准农业与碳中和。
A Collaborative Platform for Soil Organic Carbon Inference Based on Spatiotemporal Remote Sensing Data
- 融合机器学习与多源遥感数据，构建土壤有机碳预测模型。
- 支持历史公私数据融合分析，实现大范围动态监测。
- 为科研人员和政策制定者提供可视化决策工具。
土壤有机碳（SOC）是衡量土壤健康、肥力及固碳能力的关键指标，对可持续土地管理和应对气候变化至关重要。然而，由于空间异质性、时间动态性及多重影响因素，大规模SOC监测仍面临挑战。本文提出WALGREEN平台，通过机器学习与多样化的土壤样本数据，利用历史公开与私有数据构建预测模型，克服现有应用局限。该平台基于云技术开发，采用Python、Java与JavaScript实现，集成Google Earth Engine与Sentinel Copernicus数据，通过脚本、OpenLayers与Thymeleaf在模型-视图-控制器框架下运行。用户可通过友好界面获取碳数据、分析趋势，支持科学决策。平台旨在推动土壤科学发展，促进可持续农业，增强生态系统对气候变化的响应能力。
原文摘要 · Abstract (English)
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.
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