Background 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 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. Results Spectral 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. Conclusions Variables 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.

Background and aims The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0–30 cm and 30–60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0–30 cm and 30–60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results In the 0–30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30–60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3–21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction.

This article presents a methodology based on classification of images from Landsat 7 ETM + to classify Andean wetlands known as ¨Bofedal¨ (wetland) located in the Fauna Production Reserve Chimborazo. Five of the seven in-situ geo-referenced bofedales belong to this category and two belong to the altiplano. These georeferenced reservoirs are the principal habitat of the vicuñas that are located within the RPFCH in the jurisdictions of the province of Tungurahua: Río Blanco, ¨Mocha¨ Valley area, 472.26 ha, 4400 m.; Chimborazo Province: Bofedal Quebrada Toni, Urbina area, 16.74 ha, 4301 m, bofedal El Refugio (Hermanos Carrel) at the Nevado Chimborazo, 1.44 ha, 4800msnm, and Curi bofedal Pogyo, Chorrera Mirador, 0.34 ha., 4523 m. and in the Bolivar Province: the wetlands Chag Pogyo, Pulinguí San Pablo, 19.36 ha, 4064 meters above sea level. Bofedal Sinche1, the sector ¨antennas¨, 8.53 ha. 4167 m., And Sinche2, ¨Puente Ayora¨ area, 9.39 ha., 3981 meters, the latter being Chag Pogyo highland bofedales. The seven bofedales represent 0.93% (527.87 ha) of the total area of the RPFCH (56653, 27 ha.). Two images of the satellite Landsat 7 ETM +, from the years 2001 - EarthSat, 2004 - USGS and an orthophoto 2013-2014 - GIS land were used. Georeferenced and rectified to capture the spatial and temporal variability of these ecosystems and define the characterization of bofedales in the reserve. For each image two classification methods were used, the supervised classification being the most efficient when representing the four representative classes in the RPFCH: snow, rock, pajonal and bofedal. Since this classification is oriented to objects that takes into account aspects such as shape and texture and not just the spectral information of each pixel. Allowing to obtain information on the characteristics and spatial distribution of the bofedales which was verified and validated later in the field. This process allows the generation of digital cartography with the identification, delimited and distributed bofedales along the RPFCH, representing a total of approximately 1483.94 ha in the RPFCH. In addition, the Normalized Difference Vegetation Index (NDVI) was applied, which made it possible to differentiate water bodies from other coverages, as well as specifically to know the extent of the reservoirs present in the Reserve, in order to better infer Distribution of vicuñas.