全球红树林土壤有机碳库存数据集以30米分辨率为2020年,基于时空预测机器学习
Tania L Maxwell1, Tomislav Hengl2, Leandro L Parente2
1Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK.
Data in brief
|October 12, 2023
概括
这项研究使用整体机器学习绘制了全球红树林中的土壤有机碳库存. 这些发现为了解这些重要生态系统中的碳储存提供了关键数据.
科学领域:
- 环境科学 环境科学
- 土壤科学 土壤科学
- 遥感 遥感 遥感 遥感
背景情况:
- 红树林是关键的碳汇,但关于它们的土壤有机碳 (SOC) 库存的全面全球数据仍然有限.
- 准确量化SOC对于气候变化减缓战略和生态系统管理至关重要.
研究的目的:
- 为了在2020年生成一个高分辨率 (30米) 的全球数据集,用于树林中的土壤有机碳库存.
- 通过先进的机器学习技术,提高SOC库存估计的准确性和可靠性.
主要方法:
- 利用时空集团机器学习模型,在10331个土壤样本的SOC和散装密度 (BD) 全球数据集上进行训练.
- 集成的时间序列遥感数据和静态共变层,用于强大的预测土壤映射,深度高达1米.
- 采用空间交叉验证来确保模型性能并最大限度地减少偏差.
主要成果:
- 制作了2020年红树林土壤有机碳库存和30米分辨率的散装密度的详细预测.
- 使用预测间隔的SOC股票预测中的量化不确定性.
- 数据集可作为可下载的云优化GeoTIFFs在CC-BY许可证下提供.
结论:
- 开发的数据集为绘制全球红树林SOC库存的地图提供了重大进展,这对气候和保护研究至关重要.
- 集成机器学习方法提高了生态预测的准确性和可靠性.
- 该资源支持全球气候变化减缓和红树林生态系统监测工作.
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