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. 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. 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.

中文翻译：

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高山苔原土壤有机碳的多预测图：以厄瓜多尔中部帕拉莫为例

土壤有机碳 (SOC) 影响基本的生物、生化和物理土壤功能，如养分循环、保水、水分分布和土壤结构稳定性。被称为如此高碳和水储存能力的生态系统的安第斯帕拉莫是一个复杂、异质和偏远的生态系统，使收集 SOC 数据的实地研究变得复杂。在这里，我们建议使用随机森林回归来绘制 SOC 的多预测器远程量化，以绘制厄瓜多尔钦博拉索省草本 páramo 的 SOC 储量。来自 Landsat-8 (L8) 传感器、OLI 和 TIRS、地形、地质、土壤分类和气候变量的光谱指数与 500 个原位 SOC 采样数据结合使用，用于训练和校准合适的预测 SOC 模型。选择的最终预测模型使用九个预测因子，其中以重量百分比表示的 SOC 的 RMSE 为 1.72% 和 0.82 的 R2，以 Mg/ha 为单位的模型的 RMSE 为 25.8 Mg/ha 和 R2 为 0.77。卫星衍生的指数，如 VARIG、SLP、NDVI、NDWI、SAVI、EVI2、WDRVI、NDSI、NDMI、NBR 和 NBR2 未被发现是强 SOC 预测因子。相关预测因子按重要性顺序排列：地质单元、土壤分类、降水、海拔、方向、坡长和陡度（LS 因子）、裸土指数 (BI)、年平均温度和 TOA 亮度温度。诸如来自卫星图像的 BI 指数和来自 DEM 的 LS 因子等变量提高了 SOC 映射精度。测绘结果表明，超过 57% 的研究区域含有高浓度的 SOC，在 150 至 205 Mg/ha 之间，将草本 páramo 定位为具有全球重要性的生态系统。本研究获得的结果可用于扩展厄瓜多尔整个草本生态系统中的 SOC 绘图，提供一种高效、准确的方法，而无需进行密集的原位采样。