@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"
}