Abstract
BackgroundSoil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador.ResultsSpectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R2 of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R2 of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature.ConclusionsVariables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.
Highlights
Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability
In this research, a soil organic carbon multi-predictor model for the complex Andean páramo area was calibrated with an accuracy level of 82% for SOC in weight % and 77% for SOC in Mg/ha
By optimizing a Random Forest (RF) automatic learning algorithm, nine environmental variables related to the dynamics of SOC sequestration were selected
Summary
Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The largest amount of SOC is typically stored into the top organic horizon layer of the soil (0 to 30 cm), and gradual decreases towards deeper soil profile sections [8,9,10,11] These organic soils have high water retaining capacity, and in turn they accumulate water from thawing, rain, and fog condensation. The accumulated water is further released to the lowlands providing essential ecosystem services for the larger region of these ecosystems [7, 12] Identifying such priority ecosystems and quantifying their SOC stock is a priority for the climate goals. Since annual changes in SOC are considered small compared to the SOC stocks, such continuous monitoring of SOC could be at intervals of 5–10 years [8] in case robust prediction models are available
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