Pedosphere is the largest terrestrial carbon reservoir. Soil organic carbon (SOC) is a critical attribute for soil quality and crop productivity, being directly linked to climate change mitigation and food security. Brazil boasts a significant agricultural production area and substantial potential for carbon sequestration. Nevertheless, the spatial-temporal distribution of SOC across the country is poorly understood, hindering the implementation of low-carbon agriculture public policies. We aimed to map the spatio-temporal distribution of SOC at from 0.00 to 0.20 cm depth over two periods. We assessed the SOC variation over seven years, generating a time series with five periods, obtaining the average SOC values. The Cubist algorithm was used to calibrate two short period (two years) and a long period (seven years/all period) models for SOC spatial prediction. Remote sensing data and soil particle size distribution maps were used as environmental covariates. We found in validation R2 values of 0.47 and 0.25 for short period models, and 0.34 for the long period model. The SOC content decreased by 54.97 % in the area according to the mapping by short period models and 53.72 % according to mapping by the long-period model. The predicted maps showed the same trend of the database (soil samples with observed SOC values) for the study areas using both short period and long period models.

中文翻译：

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通过遥感和机器学习对土壤有机碳进行时空制图


Pedosphere 是最大的陆地碳库。土壤有机碳 （SOC） 是土壤质量和作物生产力的关键属性，与减缓气候变化和粮食安全直接相关。巴西拥有重要的农业生产区和巨大的碳封存潜力。然而，人们对 SOC 在全国的时空分布知之甚少，阻碍了低碳农业公共政策的实施。我们旨在绘制两个时期 SOC 在 0.00 至 0.20 cm 深度的时空分布图。我们评估了 7 年的 SOC 变化，生成了一个具有五个周期的时间序列，获得了平均 SOC 值。Cubist 算法用于校准两个短期（两年）和一个长期（七年/所有周期）模型，用于 SOC 空间预测。遥感数据和土壤粒径分布图用作环境协变量。我们在验证中发现短期模型的 R2 值为 0.47 和 0.25，长期模型的 R2 值为 0.34。根据短期模型映射，该地区 SOC 含量降低了 54.97 %，根据长期模型映射，该区域的 SOC 含量降低了 53.72 %。预测地图显示了使用短期和长期模型的研究区域的数据库（具有观察到的 SOC 值的土壤样本）的相同趋势。