Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression,Plant and Soil

当前位置： X-MOL 学术 › Plant Soil › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel-2 and GIS using Gaussian processes regression
Plant and Soil ( IF 3.9 ) Pub Date : 2022-06-03 , DOI: 10.1007/s11104-022-05506-1
Johanna Elizabeth Ayala Izurieta _{1,

2} , Carlos Arturo Jara Santillán _{1,

3} , Carmen Omaira Márquez _{4,

5} , Víctor Julio García _{4,

6} , Juan Pablo Rivera-Caicedo ₇ , Shari Van Wittenberghe ₁ , Jesús Delegido ₁ , Jochem Verrelst ₁

Affiliation

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/C_ab, 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.

中文翻译：

使用高斯过程回归改进 Sentinel-2 和 GIS 复杂生态系统中土壤有机碳的远程估算

背景和目标

土壤有机碳 (SOC) 储存量的定量检索，特别是对于具有巨大碳封存潜力的土壤，由于其与碳循环和减缓气候变化的联系而引起全球关注。然而，对于土壤有机碳储存具有良好土壤质量的复杂生态系统研究很少。

方法

SOC 与各种植被遥感驱动因素之间的相互关系被理解为表明植被层中储存的碳与表层土壤层的 SOC 之间的联系。基于两个层位（0-30 cm 和 30-60 cm）的 SOC 映射，我们预测了厄瓜多尔复杂多山的异质 páramo 系统中的 SOC。分别从 0-30 厘米和 30-60 厘米土壤剖面的 493 和 494 个 SOC 采样数据点的大型 SOC 数据库（以重量百分比和 Mg/ha 为单位）用于使用 Sentinel-2 和 GIS 预测器校准 GPR 模型（即温度、海拔、土壤分类、地质单位、坡度和陡度（LS 因子）、方向和降水）。