@Article{RochaNetoTeiLeãMorGal:2017:HyReSe,
author = "Rocha Neto, Od{\'{\i}}lio Coimbra da and Teixeira, Adunias dos
Santos and Le{\~a}o, Raimundo Al{\'{\i}}pio de Oliveira and
Moreira, Luis Clenio Jario and Galv{\~a}o, L{\^e}nio Soares",
affiliation = "{Universidade Federal do Cear{\'a} (UFC)} and {Universidade
Federal do Cear{\'a} (UFC)} and {Universidade Federal do
Cear{\'a} (UFC)} and {Universidade Federal do Cear{\'a} (UFC)}
and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Hyperspectral remote sensing for detecting soil salinization using
ProSpecTIR-VS aerial imagery and sensor simulation",
journal = "Remote Sensing",
year = "2017",
volume = "9",
number = "1",
pages = "UNSP 42",
month = "Jan.",
keywords = "soil salinization, electrical conductivity, reflectance
spectroscopy, hyperspectral remote sensing, Extreme Learning
Machine (ELM), Ordinary Least Square Regression (OLS), Multilayer
Perceptron (MLP), Partial Least Squares Regression (PLSR).",
abstract = "Soil salinization due to irrigation affects agricultural
productivity in the semi-arid region of Brazil. In this study, the
performance of four computational models to estimate electrical
conductivity (EC) (soil salinization) was evaluated using
laboratory reflectance spectroscopy. To investigate the influence
of bandwidth and band positioning on the EC estimates, we
simulated the spectral resolution of two hyperspectral sensors
(airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager
(HyspIRI)) and three multispectral instruments (RapidEye/REIS,
High Resolution Geometric (HRG)/SPOT-5, and Operational Land
Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and
the first-order derivative analysis were applied to the data to
generate metrics associated with soil brightness and spectral
features, respectively. The three sets of data (reflectance, PCA,
and derivative) were tested as input variable for Extreme Learning
Machine (ELM), Ordinary Least Square regression (OLS), Partial
Least Squares Regression (PLSR), and Multilayer Perceptron (MLP).
Finally, the laboratory models were inverted to a ProSpecTIR-VS
image (400-2500 nm) acquired with 1-m spatial resolution in the
northeast of Brazil. The objective was to estimate EC over exposed
soils detected using the Normalized Difference Vegetation Index
(NDVI). The results showed that the predictive ability of the
linear models and ELM was better than that of the MLP, as
indicated by higher values of the coefficient of determination
(R-2) and ratio of the performance to deviation (RPD), and lower
values of the root mean square error (RMSE). Metrics associated
with soil brightness (reflectance and PCA scores) were more
efficient in detecting changes in the EC produced by soil
salinization than metrics related to spectral features
(derivative). When applied to the image, the PLSR model with
reflectance had an RMSE of 1.22 dS.m(-1) and an RPD of 2.21, and
was more suitable for detecting salinization (10-20 dS.m(-1)) in
exposed soils (NDVI < 0.30) than the other models. For all
computational models, lower values of RMSE and higher values of
RPD were observed for the narrowband-simulated sensors compared to
the broadband-simulated instruments. The soil EC estimates
improved from the RapidEye to the HRG and OLI spectral
resolutions, showing the importance of shortwave intervals (SWIR-1
and SWIR-2) in detecting soil salinization when the reflectance of
selected bands is used in data modelling.",
doi = "10.3390/rs9010042",
url = "http://dx.doi.org/10.3390/rs9010042",
issn = "2072-4292",
language = "en",
targetfile = "neto.pdf",
urlaccessdate = "27 abr. 2024"
}