86456 20200716101526.0 doi 10.1002/ldr.3250 sideral 110697 ART-2019-110697 eng (orcid)0000-0002-3104-0115 Rosero-Vlasova, O.A. Soil organic matter and texture estimation from visible–near infrared–shortwave infrared spectra in areas of land cover changes using correlated component regression 2019 Access copy available to the general public Unrestricted Land use changes due to natural and human-related factors, which include wildfires and crop abandonment, are among the most important drivers of soil degradation and demand regular monitoring. Proximal soil sensing in visible–near infrared–shortwave infrared spectral regions could offer a solution. However, to become operational, optimal combination of data and technique has to be defined. Thus, the purpose of this study was (a) to predict the soil organic matter (SOM) content and soil texture in areas of wildfire burns and crop abandonment in Aragón Province, Northern Spain, from their laboratory reflectance spectra using novel correlated components regression with a step-down variable selection algorithm (CCR-SD) and (b) to compare the CCR-SD and the partial least squares regression (PLSR) methods. The results obtained by the tested methods were similar. CCR-SD models showed high predictive capacity with coefficients of determination (R2) in the range of 0.80–0.86 and 0.70–0.87 for calibration and validation data sets, respectively, and the highest R2 value was attained in the SOM estimation. Moreover, the CCR-SD models stand out for the superior accuracy–parsimony relationship: the number of predictors varied from 16 (silt models) to 49 (SOM models). On average, the CCR-SD calibrations needed less than a half of the predictors employed in PLSR models. This research confirmed that CCR-SD can be used for monitoring SOM content and texture of soils from visible–near infrared–shortwave infrared spectra in the study area and, probably, in other areas of land use/land cover change and that CCR-SD can create highly parsimonious models that achieve results comparable with the commonly used PLSR method. info:eu-repo/grantAgreement/EC/FP7/624974/EU/Afforestation management in Mediterranean mountain areas: Evaluating the environmental consequences/MED-AFFOREST info:eu-repo/grantAgreement/ES/MINECO/RYC-2013-14371 info:eu-repo/grantAgreement/ES/UZ-SANTANDER/Ayudas Movilidad Latinoamericanos-2017-2018 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 3.775 2019 SOIL SCIENCE 6 / 38 = 0.158 2019 Q1 T1 ENVIRONMENTAL SCIENCES 75 / 265 = 0.283 2019 Q2 T1 1.331 2019 Development 2019 Q1 Soil Science 2019 Q1 Environmental Science (miscellaneous) 2019 Q1 Environmental Chemistry 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Vlassova, L. (orcid)0000-0003-4831-4060 Pérez-Cabello, F. Universidad de Zaragoza (orcid)0000-0001-7403-1764 Montorio, R. Universidad de Zaragoza (orcid)0000-0002-4651-7828 Nadal-Romero, E. 3006 010 Universidad de Zaragoza Dpto. Geograf. Ordenac.Territ. Área Análisis Geográfico Regi. 30, 5 (2019), 544-560 Land degrad. dev. LAND DEGRADATION & DEVELOPMENT 1085-3278 1926033 http://zaguan.unizar.es/record/86456/files/texto_completo.pdf Postprint 254199 http://zaguan.unizar.es/record/86456/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:86456 articulos driver 2020-07-16-09:30:39 ARTICLE