Ayala Izurieta Johanna Elizabeth, Márquez Carmen Omaira, García Víctor Julio, Jara Santillán Carlos Arturo, Sisti Jorge Marcelo, Pasqualotto Nieves, Van Wittenberghe Shari, Delegido Jesús
Image Processing Laboratory (IPL), University of Valencia, 46980, Paterna, Valencia, Spain.
Faculty of Engineering, National University of Chimborazo, Riobamba, 060150, Ecuador.
Carbon Balance Manag. 2021 Oct 24;16(1):32. doi: 10.1186/s13021-021-00195-2.
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 R of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R 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.
土壤有机碳（SOC）影响着土壤的基本生物学、生物化学和物理功能，如养分循环、保水、水分分布以及土壤结构稳定性。安第斯帕拉莫生态系统以其高碳和高储水能力而闻名，是一个复杂、异质且偏远的生态系统，这使得收集SOC数据的实地研究变得复杂。在此，我们提出使用随机森林回归对SOC进行多预测因子遥感定量，以绘制厄瓜多尔钦博拉索省草本帕拉莫地区的SOC储量图。
利用来自Landsat - 8（L8）传感器OLI和TIRS的光谱指数、地形、地质、土壤分类和气候变量，并结合500个现场SOC采样数据，用于训练和校准合适的SOC预测模型。最终选定的预测模型使用九个预测因子，以重量百分比表示的SOC的均方根误差（RMSE）为1.72%，相关系数（R）为0.82；以Mg/ha为单位的模型RMSE为25.8 Mg/ha，R为0.77。未发现诸如VARIG、SLP、NDVI、NDWI、SAVI、EVI2、WDRVI、NDSI、NDMI、NBR和NBR2等卫星衍生指数是强大的SOC预测因子。相反，相关预测因子按重要性排序为：地质单元、土壤分类、降水量、海拔、方位、坡长和坡度（LS因子）、裸土指数（BI）、年均温度和大气顶层亮度温度。
诸如卫星图像衍生的BI指数和数字高程模型（DEM）的LS因子等变量提高了SOC制图的准确性。制图结果表明，研究区域超过57%的面积含有高浓度的SOC，介于150至205 Mg/ha之间，这表明草本帕拉莫是一个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的SOC制图，提供一种无需密集现场采样的高效且准确的方法。
