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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Soil organic carbon (SOC) stocks are a remarkable property for soil and environmental monitoring. The understanding of their dynamics in crop soils must go forward. The objective of this study was to determine the impact of temporal environmental controlling factors obtained by satellite images over the SOC stocks along soil depth, using machine learning algorithms. The work was carried out in São Paulo state (Brazil) in an area of 2577 km2. We obtained a dataset of boreholes with soil analyses from topsoil to subsoil (0–100 cm). Additionally, remote sensing covariates (30 years of land use history, vegetation indexes), soil properties (i.e., clay, sand, mineralogy), soil types (classification), geology, climate and relief information were used. All covariates were confronted with SOC stocks contents, to identify their impact. Afterwards, the abilities of the predictive models were tested by splitting soil samples into two random groups (70 for training and 30% for model testing). We observed that the mean values of SOC stocks decreased by increasing the depth in all land use and land cover (LULC) historical classes. The results indicated that the random forest with recursive features elimination (RFE) was an accurate technique for predicting SOC stocks and finding controlling factors. We also found that the soil properties (especially clay and CEC), terrain attributes, geology, bioclimatic parameters and land use history were the most critical factors in controlling the SOC stocks in all LULC history and soil depths. We concluded that random forest coupled with RFE could be a functional approach to detect, map and monitor SOC stocks using environmental and remote sensing data.

Details

Title
Drivers of Organic Carbon Stocks in Different LULC History and along Soil Depth for a 30 Years Image Time Series
Author
Tayebi, Mahboobeh 1   VIAFID ORCID Logo  ; Jorge Tadeu Fim Rosas 1   VIAFID ORCID Logo  ; Wanderson de Sousa Mendes 2   VIAFID ORCID Logo  ; Poppiel, Raul Roberto 1   VIAFID ORCID Logo  ; Ostovari, Yaser 3 ; Luis Fernando Chimelo Ruiz 1   VIAFID ORCID Logo  ; Natasha Valadares dos Santos 1   VIAFID ORCID Logo  ; Pellegrino Cerri, Carlos Eduardo 1   VIAFID ORCID Logo  ; Sérgio Henrique Godinho Silva 4   VIAFID ORCID Logo  ; Curi, Nilton 4   VIAFID ORCID Logo  ; Nélida Elizabet Quiñonez Silvero 1   VIAFID ORCID Logo  ; Demattê, José A M 1   VIAFID ORCID Logo 

 Department of Soil Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Pádua Dias Av., 11, Piracicaba, P.O. Box 09, São Paulo 13416-900, Brazil; [email protected] (M.T.); [email protected] (J.T.F.R.); [email protected] (R.R.P.); [email protected] (L.F.C.R.); [email protected] (N.V.d.S.); [email protected] (C.E.P.C.); [email protected] (N.E.Q.S.) 
 Leibniz Centre for Agricultural Landscape Research (ZALF) Müncheberg, Research Area1 “Landscape Functioning”, Working Group Landscape Pedology, 15374 Müncheberg, Germany; [email protected] 
 Soil Science, Research Department of Ecology and Ecosystem Management, TUM School of Life Science Weihenstephan, Technical University of Munich, 85354 Freising, Germany; [email protected] 
 Department of Soil Science, Federal University of Lavras, P.O. Box 3037, Lavras 37200-900, Brazil; [email protected] (S.H.G.S.); [email protected] (N.C.) 
First page
2223
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2539967894
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.