Fechar
Metadados

@Article{DottoDamGruCatPer:2017:TwPrTe,
               author = "Dotto, Andre Carnieletto and Damolin, Ricardo Sim{\~a}o Diniz and 
                         Grunwald, Sabine and Caten, Alexandre ten and Pereira Filho, 
                         Waterloo",
          affiliation = "{Universidade Federal de Santa Maria (UFSM)} and {Universidade 
                         Federal de Santa Maria (UFSM)} and {University of Florid} and 
                         {Universidade Federal de Santa Catarina (UFSC)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Two preprocessing techniques to reduce model covariables in soil 
                         property predictions by Vis-NIR spectroscopy",
              journal = "Soil and Tillage Research",
                 year = "2017",
               volume = "172",
                pages = "59--68",
                month = "Sept.",
             keywords = "Visible-near infrared spectroscopy, Continuum removal, Detrend, 
                         Band ratio.",
             abstract = "Proximal sensing provides an alternative method to physical and 
                         chemical laboratory soil analyses. The aim of this study is to 
                         predict soil organic carbon (SOC), clay, sand, and silt content 
                         using reduced spectral features as covariables selected by two 
                         spectral preprocessing. A total of 299 soil samples were collected 
                         in Santa Catarina state, Brazil. Two preprocessing techniques, 
                         detrend transformation and continuum removal (CR), were applied to 
                         isolate particular absorption features in the reflectance 
                         spectrum. Two techniques were used to select the spectral features 
                         in the spectrum: hand and mathematical selection. Partial least 
                         squares regression (PLSR) and Support vector machines (SVM) were 
                         applied to predict the soil properties. The reduction of predictor 
                         covariables by hand selection technique contributed in developing 
                         a high-level prediction model for SOC. PLSR and SVM presented no 
                         statistical difference between the RMSE results, except for clay 
                         content, where SVM presented superior performance. The 
                         preprocessing techniques were statistically identical based on 
                         RMSE results. Overall, the prediction of SOC, clay, sand and silt 
                         presented suitable results using reduced spectral features as 
                         covariables in modeling process.",
                  doi = "10.1016/j.still.2017.05.008",
                  url = "http://dx.doi.org/10.1016/j.still.2017.05.008",
                 issn = "0167-1987",
             language = "en",
           targetfile = "dotto_two.pdf",
        urlaccessdate = "29 nov. 2020"
}


Fechar