Johanna Elizabeth Ayala Izurieta, Carmen Omaira Márquez, Víctor Julio García, Carlos Arturo Jara Santillán, Jorge Marcelo Sisti, Nieves Pasqualotto, Shari Van Wittenberghe, Jesús Delegido
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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.</p><h3>Results</h3><p>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<sup>2</sup> of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R<sup>2</sup> 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.</p><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"16 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543914/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo\",\"authors\":\"Johanna Elizabeth Ayala Izurieta, Carmen Omaira Márquez, Víctor Julio García, Carlos Arturo Jara Santillán, Jorge Marcelo Sisti, Nieves Pasqualotto, Shari Van Wittenberghe, Jesús Delegido\",\"doi\":\"10.1186/s13021-021-00195-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>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.</p><h3>Results</h3><p>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<sup>2</sup> of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R<sup>2</sup> 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.</p><h3>Conclusions</h3><p>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. 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Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo
Background
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.
Results
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 R2 of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R2 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.
Conclusions
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.
期刊介绍：
Carbon Balance and Management is an open access, peer-reviewed online journal that encompasses all aspects of research aimed at developing a comprehensive policy relevant to the understanding of the global carbon cycle.
The global carbon cycle involves important couplings between climate, atmospheric CO2 and the terrestrial and oceanic biospheres. The current transformation of the carbon cycle due to changes in climate and atmospheric composition is widely recognized as potentially dangerous for the biosphere and for the well-being of humankind, and therefore monitoring, understanding and predicting the evolution of the carbon cycle in the context of the whole biosphere (both terrestrial and marine) is a challenge to the scientific community.
This demands interdisciplinary research and new approaches for studying geographical and temporal distributions of carbon pools and fluxes, control and feedback mechanisms of the carbon-climate system, points of intervention and windows of opportunity for managing the carbon-climate-human system.
Carbon Balance and Management is a medium for researchers in the field to convey the results of their research across disciplinary boundaries. Through this dissemination of research, the journal aims to support the work of the Intergovernmental Panel for Climate Change (IPCC) and to provide governmental and non-governmental organizations with instantaneous access to continually emerging knowledge, including paradigm shifts and consensual views.
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