School of Environment and Life Sciences, The University of Newcastle, Australia.
School of Engineering, The University of Newcastle, Australia.
Sci Total Environ. 2022 Apr 15;817:152690. doi: 10.1016/j.scitotenv.2021.152690. Epub 2021 Dec 30.
Soil organic carbon (SOC) is an important soil component. However, examining SOC at the large catchment scale is difficult due to the intensive labour requirements. This study examines SOC distribution at large (>500 km) catchment scales using field-sampled SOC data and remote sensed vegetation indices located in eastern Australia (Krui River catchment - 562 km; Merriwa River catchment - 808 km) on grazing land-use basalt soil. The SOC data obtained was compared to digital elevation model (DEM) derived elevation and insolation data, as well as Normalised Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) values corresponding to each sample site. These indices were obtained from the MODIS sensor (Terra/Aqua) and Landsat series satellites. Vegetation Indices (VI) captured immediately prior to sampling demonstrated a poor correlation with SOC. The use of multiple, aggregated, prior VI data sets provided a good match with SOC. The strongest match occurred for Landsat 8 EVI, indicating that VIs with higher spatial and spectral resolution, which can account for atmospheric interference, have the potential to produce more accurate SOC mapping (Krui samples in 2006, R = 0.31, P < 0.01; Krui sampled in 2014, R = 0.41, P < 0.01; Merriwa samples in 2015, R = 0.37, P < 0.01). A sensitivity test for both remote sensing platforms demonstrated that the findings were robust. The results demonstrate that VIs are a reliable surrogate for historical vegetation growth in pasture dominated landscapes and therefore soil carbon inputs allowing for mapping of SOC across large catchment scales. Both Landsat and MODIS produced similar results and demonstrate that SOC can be reliably predicted at the large catchment scale and for different catchments in this environment with RMSE range of 0.79 to 1.06. The method and data can be applied globally and provides a new method for environmental assessment.
土壤有机碳（SOC）是土壤的重要组成部分。然而，由于需要大量的劳动力，在大流域尺度上研究 SOC 是困难的。本研究利用位于澳大利亚东部（Krui 河流域-562km；Merriwa 河流域-808km）放牧土地利用玄武岩土壤的实地采样 SOC 数据和遥感植被指数，研究了大（>500km）流域尺度上 SOC 的分布情况。所获得的 SOC 数据与数字高程模型（DEM）衍生的海拔和太阳辐射数据以及归一化差异植被指数（NDVI）和对应于每个采样点的增强植被指数（EVI）值进行了比较。这些指数是从 MODIS 传感器（Terra/Aqua）和 Landsat 系列卫星获得的。采样前获取的植被指数（VI）与 SOC 的相关性较差。使用多个聚合的、先前的 VI 数据集与 SOC 很好地匹配。与 SOC 匹配最好的是 Landsat 8 EVI，这表明具有更高空间和光谱分辨率的 VI，可以更好地反映大气干扰，有潜力产生更准确的 SOC 制图（2006 年 Krui 采样，R=0.31，P<0.01；2014 年 Krui 采样，R=0.41，P<0.01；2015 年 Merriwa 采样，R=0.37，P<0.01）。对两种遥感平台的敏感性测试表明，这些发现是稳健的。结果表明，VI 是牧场景观中历史植被生长的可靠替代物，因此是土壤碳输入的替代物，允许在大流域尺度上绘制 SOC 图。Landsat 和 MODIS 都产生了类似的结果，表明可以在这种环境中以 RMSE 范围为 0.79 到 1.06 的不同流域可靠地预测 SOC。该方法和数据可在全球范围内应用，为环境评估提供了一种新方法。
