Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic

Authors: DUARTE, EFRAIN - University Of Concepcion; ZAGAL, ERICK - University Of Concepcion; BARRERA, JUAN - University Of Concepcion; DUBE, FRANCIS - University Of Concepcion; CASCO, FABLO - Food And Agriculture Organization Of The United Nations (FAO); Hernandez, Alexander

Submitted to: European Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2021
Publication Date: 3/20/2022
Citation: Duarte, E., Zagal, E., Barrera, J.A., Dube, F., Casco, F., Hernandez, A.J. 2022. Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic. European Journal of Remote Sensing. 55(1):213-231. https://doi.org/10.1080/22797254.2022.2045226.
DOI: https://doi.org/10.1080/22797254.2022.2045226

Interpretive Summary: Problem: The largest carbon (C) pools on earth are found in the oceans and then on the soils. The soil organic carbon (SOC) directly influences the soil's nutrient retention capacity and how soils infiltrate water. While global carbon pools are well understood, SOC pools in tropical lands are still not well documented, and maps that depict the distribution of SOC along with estimates of the error are difficult to obtain for large areas. Accomplishment: A transparent protocol to map SOC across large areas was developed using a completely cost-free cloud-base platform, and the code is provided for anybody to replicate on other areas. Contribution to solving the problem: Users worldwide can utilize the mapping protocol to apply it on their own area of interest. The only element that users will need to provide is their SOC survey measurements and locations. With the code that is provided, users can replicate and obtain results for areas of any size, thereby augmenting the opportunities for a wide audience to contribute to the understanding of SOC distribution on the earth.

Technical Abstract: Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0-15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha-1, respectively. Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha-1. There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region.