Assessment of Soil Carbon Stocks Using Remote Sensing Images in a Carbon Rich Ecosystem: the Everglades, Florida, U.S.

Wetland soils play an important role in regulation of the global carbon (C) cycle. Yet it is challenging to accurately evaluate the actual amount of C storages in wetlands. The incorporation of remote sensing (RS) data into digital soil models has great potential to assess C dynamics in wetland soils. Our objectives were to (i) develop C stock prediction models and (ii) assess the amount of C stored in a prominent nutrient-enriched wetland. We collected a total of 108 soil cores at two soil depths (0 – 10 cm and 10 – 20 cm) in a C-rich ecosystem: Water Conservation Area-2A (WCA-2A), Florida, U.S. Random Forest (RF) models to predict soil C stocks were developed using field observation data, environmental ancillary data, and spectral data derived from RS images including SPOT (spatial resolution: 10 m), Landsat ETM+ (30 m), and MODIS (250 m). The RF models showed high performance to predict total C stocks with a R² between 0.85 to 0.92 and a root mean squared error between 0.75 to 0.86 kg m^-2. The variable importance of the RF models showed that hydrology was the major environmental factor controlling the spatial distribution of soil C stocks in WCA-2A. Our results showed that WCA-2A stores about 4.2 mega tons of C in the top 20 cm soils.