The main hurdle in instrumentalizing agricultural soils to sequester atmospheric carbon is the development of methods to measure soil carbon stocks which are robust, scalable, and widely applicable. Our objective is to develop an approach that can help overcome these hurdles. In this paper, we present the Wageningen Soil Carbon STOck pRotocol (SoilCASTOR). SoilCASTOR uses a novel approach fusing satellite data, direct proximal sensing-based soil measurements, and machine learning to yield soil carbon stock estimates. The method has been tested and applied in the USA on fields with agricultural land use. Results show that the estimates are precise and repeatable and that the approach could be rapidly scalable. The precision of farm C stocks is below 5% enabling detection of soil organic carbon changes desired for the 4 per 1000 initiative. The assessment can be done robustly with as few as 0.5 sample per hectare for farms varying from 20 to 150 hectares. These findings could enable the structural implementation of carbon farming.

将农业土壤工具化以封存大气碳的主要障碍是开发稳健、可扩展和广泛适用的土壤碳储量测量方法。我们的目标是开发一种可以帮助克服这些障碍的方法。在本文中，我们介绍了 Wageningen Soil Carbon STOck pRotocol (SoilCASTOR)。SoilCASTOR 使用一种融合卫星数据、基于直接近端传感的土壤测量和机器学习的新方法来估算土壤碳储量。该方法已在美国的农业土地利用领域进行了测试和应用。结果表明，估计是精确且可重复的，并且该方法可以快速扩展。农场碳库的精确度低于 5%，能够检测千分之四计划所需的土壤有机碳变化。对于面积从 20 到 150 公顷不等的农场，只需每公顷 0.5 个样本就可以稳健地进行评估。这些发现可以实现碳农业的结构性实施。