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

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

dc.contributor.author	Ayala Izurieta, Johanna Elizabeth
dc.contributor.author	Jara Santillán, Carlos Arturo
dc.contributor.author	Van Wittenberghe, Shari
dc.contributor.author	Delegido Gómez, Jesús
dc.contributor.author	Verrelst, Jochem
dc.date.accessioned	2023-07-03T13:29:44Z
dc.date.available	2023-07-03T13:29:44Z
dc.date.issued	2022
dc.identifier.citation	Ayala Izurieta, Johanna Elizabeth Jara Santillán, Carlos Arturo Van Wittenberghe, Shari Delegido Gómez, Jesús Verrelst, Jochem 2022 Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel‑2 and GIS using Gaussian processes regression Plant and Soil 479 159 183
dc.identifier.uri	https://hdl.handle.net/10550/88607
dc.description.abstract	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.
dc.language.iso	eng
dc.relation.ispartof	Plant and Soil, 2022, vol. 479, p. 159-183
dc.subject	Ciències de la terra
dc.title	Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel‑2 and GIS using Gaussian processes regression
dc.type	journal article
dc.date.updated	2023-07-03T13:29:44Z
dc.identifier.doi	10.1007/s11104-022-05506-1
dc.identifier.idgrec	154754
dc.rights.accessRights	open access