Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

Izurieta, Johanna Elizabeth Ayala; Márquez, Carmen O.; García, Vega; Santillán, Carlos Arturo Jara; Sisti, Jorge Marcelo; Pasqualotto, Nieves; Wittenberghe, Shari Van; Delegido, Jesús

doi:10.1186/s13021-021-00195-2

Cited by 13 publications

(15 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NDVI and EVI are plant indices that allow spatial estimation of soil organic matter and organic carbon and characterize vegetation information [ 54 , 55 ]. The vegetation indices can reduce the spectrum errors, and thus improve the accuracy of SOC estimation [ 56 ]. The equations used to calculate the EVI [ 50 , 57 ] and NDVI [ 50 , 58 ] are as follows (Eqs 2 and 3 ) …”

Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Kılıç

Gündoğan²,

Günal³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

This study aimed to produce a soil organic carbon (SOC) content map with high accuracy and spatial resolution using the most effective factors in the model. The spatial SOC estimation success of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), Multi-Layered Perception Network (MLP) and MLP-OK Hybrid models were compared to obtain the most reliable model in estimating the SOC content. The study area was located in Besni district in the Southeastern Anatolia Region of Turkey. Total of 132 surface (0–30 cm) soil samples were collected from the covers 1330 km2 land and analyzed for SOC, lime, clay and sand content and soil reaction included in the estimation models. Mean annual precipitation and temperature, elevation, compound topographic index, enhanced vegetation and normalized difference vegetation index, were also used as the inputs in the modelling. The spatial distribution of SOC was determined using a MLP and a two-stage ensemble model (MLP-OK) combining the estimation of OK residuals. Soil surveys and covariates were used to train and validate the MLP-OK hybrid model. The MLP-OK model provided a more accurate estimation of SOC content with minimal estimation errors (ME: -0.028, 45 MAE: 0.042, RMSE: 0.066) for validation points compared to the other models. The MLP-OK model outperformed other models by 75.09 to 77.92%. The MLP-OK model estimated the lower and upper limits of the estimated and the measured values in a consistent manner compared to the other models. The spatial distribution map of SOC content obtained by ANN-kriging approach was significantly affected by ancillary variables, and revealed more detail than other interpolation methods in the northern, central, southwestern and southeastern parts of the study area. The results revealed that the assembling of MLP with OK model can contribute to obtain more reliable regional, national and global spatial soil information.

show abstract

Section: Methodsmentioning

confidence: 99%

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Kılıç

Gündoğan²,

Günal³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…1 ). Extended between 78º39' west longitude and 1º39' south latitude, the study area covers approximately 1667.6 km 2 (Ayala Izurieta et al 2021 ). The ecosystem boundary is the Western and Eastern Cordilleras arranged in meridian direction (Moreno et al 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…A database with SOC data on two soil profiles (0–30 cm and 30–60 cm) collected in 2016 and 2017 was used. The initial database contains around 500 in situ SOC sampling points, with a random spatial sampling distribution taking into account the geological units, the taxonomy of the soil, and accessibility based on the steepness of the terrain and availability of ballast roads and trails (Ayala Izurieta et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…Cylinders of 88 cm 3 were used, taking undisturbed soil samples (Al-Shammary et al 2018 ) and then the common volume cylinder method was applied (Bongiorno et al 2019 ). With the soil sample SOC weight % obtained (g C/100 g of soil) and the soil bulk density of the sample, the SOC content was expressed in Mg/ha (Lee et al 2009 ; Tonon et al 2010 ; Al-Shammary et al 2018 ; Bongiorno et al 2019 ; Ayala Izurieta et al 2021 ; Bateni et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Following publication of the original article [ 1 ], the authors reported errors in the reference citation.…”

Section: Correction To: Carbon Balance Manage (2021) 16:32 Https://doiorg/101186/s13021-021-00195-2mentioning

confidence: 99%

Correction to: Multi‑predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

Izurieta

Márquez

García

et al. 2021

Carbon Balance Manage

Self Cite

View full text Add to dashboard Cite

Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

Cited by 13 publications

References 87 publications

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

Accuracy Assessment of Kriging, artificial neural network, and a hybrid approach integrating spatial and terrain data in estimating and mapping of soil organic carbon

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

Correction to: Multi‑predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo

Contact Info

Product

Resources

About