Conference proceedings
O. Rosero-Vlasova, D. B. Alves, L. Vlassova, F. Pérez-Cabello, R. Montorio Llovería
Remote Sensing, 2017
Remote Sensing, 2017
Semantic Scholar
DOI
Cite
Cite
APA
Click to copy
Rosero-Vlasova, O., Alves, D. B., Vlassova, L., Pérez-Cabello, F., & Llovería, R. M. (2017). Modeling soil organic matter (SOM) from satellite data using VISNIR-SWIR spectroscopy and PLS regression with step-down variable selection algorithm: case study of Campos Amazonicos National Park savanna enclave, Brazil. Remote Sensing.
Chicago/Turabian
Click to copy
Rosero-Vlasova, O., D. B. Alves, L. Vlassova, F. Pérez-Cabello, and R. Montorio Llovería. Modeling Soil Organic Matter (SOM) from Satellite Data Using VISNIR-SWIR Spectroscopy and PLS Regression with Step-down Variable Selection Algorithm: Case Study of Campos Amazonicos National Park Savanna Enclave, Brazil. Remote Sensing, 2017.
MLA
Click to copy
Rosero-Vlasova, O., et al. “Modeling Soil Organic Matter (SOM) from Satellite Data Using VISNIR-SWIR Spectroscopy and PLS Regression with Step-down Variable Selection Algorithm: Case Study of Campos Amazonicos National Park Savanna Enclave, Brazil.” Remote Sensing, 2017.
BibTeX Click to copy
@proceedings{o2017a,
title = {Modeling soil organic matter (SOM) from satellite data using VISNIR-SWIR spectroscopy and PLS regression with step-down variable selection algorithm: case study of Campos Amazonicos National Park savanna enclave, Brazil},
year = {2017},
journal = {Remote Sensing},
author = {Rosero-Vlasova, O. and Alves, D. B. and Vlassova, L. and Pérez-Cabello, F. and Llovería, R. Montorio}
}
Abstract
Deforestation in Amazon basin due, among other factors, to frequent wildfires demands continuous post-fire monitoring of soil and vegetation. Thus, the study posed two objectives: (1) evaluate the capacity of Visible – Near InfraRed – ShortWave InfraRed (VIS-NIR-SWIR) spectroscopy to estimate soil organic matter (SOM) in fire-affected soils, and (2) assess the feasibility of SOM mapping from satellite images. For this purpose, 30 soil samples (surface layer) were collected in 2016 in areas of grass and riparian vegetation of Campos Amazonicos National Park, Brazil, repeatedly affected by wildfires. Standard laboratory procedures were applied to determine SOM. Reflectance spectra of soils were obtained in controlled laboratory conditions using Fieldspec4 spectroradiometer (spectral range 350nm– 2500nm). Measured spectra were resampled to simulate reflectances for Landsat-8, Sentinel-2 and EnMap spectral bands, used as predictors in SOM models developed using Partial Least Squares regression and step-down variable selection algorithm (PLSR-SD). The best fit was achieved with models based on reflectances simulated for EnMap bands (R2=0.93; R2cv=0.82 and NMSE=0.07; NMSEcv=0.19). The model uses only 8 out of 244 predictors (bands) chosen by the step-down variable selection algorithm. The least reliable estimates (R2=0.55 and R2cv=0.40 and NMSE=0.43; NMSEcv=0.60) resulted from Landsat model, while Sentinel-2 model showed R2=0.68 and R2cv=0.63; NMSE=0.31 and NMSEcv=0.38. The results confirm high potential of VIS-NIR-SWIR spectroscopy for SOM estimation. Application of step-down produces sparser and better-fit models. Finally, SOM can be estimated with an acceptable accuracy (NMSE~0.35) from EnMap and Sentinel-2 data enabling mapping and analysis of impacts of repeated wildfires on soils in the study area.
