Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning

• Published in: Data in brief 2023-10, Vol.50, p.109621, Article 109621
• Main Authors: Maxwell, Tania L., Hengl, Tomislav, Parente, Leandro L., Minarik, Robert, Worthington, Thomas A., Bunting, Pete, Smart, Lindsey S., Spalding, Mark D., Landis, Emily
• Format: Article
• Language: English
• Subjects: Blue carbon; Carbon sequestration; Coastal ecosystem; Data; Mangroves; Spatial modelling
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2023.109621

This dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve performance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).