Reflectance spectroscopy in the prediction of soil organic carbon associated with humic substances

被引:1
|
作者
Ribeiro, Sharon Gomes [1 ]
de Oliveira, Marcio Regys Rabelo [2 ]
Lopes, Leticia Machado [1 ]
Costa, Mirian Cristina Gomes [3 ]
Toma, Raul Shiso [3 ]
Araujo, Isabel Cristina da Silva [4 ]
Moreira, Luis Clenio Jario [5 ]
Teixeira, Adunias dos Santos [4 ]
机构
[1] Univ Fed Ceara, Programa Posgrad Ciencia Solo, Fortaleza, Ceara, Brazil
[2] Univ Fed Ceara, Programa Posgrad Engn Agr, Fortaleza, Ceara, Brazil
[3] Univ Fed Ceara, Dept Ciencia Solo, Fortaleza, Ceara, Brazil
[4] Univ Fed Ceara, Dept Engn Agr, Fortaleza, Ceara, Brazil
[5] Inst Fed Educ Ciencia & Tecnol Ceara, Dept Agron, Limoeiro Norte, Ceara, Brazil
来源
关键词
spectroradiometry; pedometrics; organic matter; PARTIAL LEAST-SQUARES; NEAR-INFRARED SPECTROSCOPY; FRACTIONS; SPECTRA; MATTER;
D O I
10.36783/18069657rbcs20220143
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
Understanding organic carbon and predominant humic fractions in the soil allows contributes to soil quality management. Conventional fractionation techniques require time, excessive sampling, and high maintenance costs. In this study, predictive models for organic carbon in humic substances (HS) were evaluated using hyperspectral data as an alternative to chemical fractionation and quantification by wet digestion. Twenty-nine samples of Neossolos Fluvicos (Fluvents) -A1, and 36 samples of Cambissolos (Inceptisols) -A2 were used. The samples were also analyzed jointly, creating a third sample group -A1 & A2. Untransformed spectral reflectance factors were obtained using the FieldSpec Pro FR 3 hyperspectral sensor (350-2500 nm). Pre-processing techniques were employed, including Savitzky-Golay smoothing and first-and second-order derivative analysis. After selecting variables using the Backward method, which removes spectral variables that are not statistically significant for the regression. Estimation models were built by Principal Components Regression (PCR) and Partial Least Squares Regression (PLSR). The spectral data were evaluated individually for soil classes A1 and A2, and jointly for A1 & A2. The PLSR was more efficient than PCR, especially for the estimation models that used the first derivative of reflectance employing the three sample groups. For samples of A1, the best estimate was seen for humic acid (RPD = 6.09) and humin (RPD = 2.38); for A2, the best models estimated the OC in fulvic acid (RPD = 2.35) and humin (RPD = 2.51); and for the joint spectral data (A1 & A2), the prediction was robust for humin only (RPD = 2.01). The most representative wavelengths were observed using the first derivative with PLSR and PCR, centred on the region between 1600 and 1800 nm. The first-derivative of reflectance calculated more-robust predictive models using PLSR than PCR. The best predictions occurred for organic carbon associated with humic acid in Neossolos Fluvicos, with fulvic acid in Cambissolos, and with humin in both soil classes.
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Fluorescence spectroscopy of humic substances
    Miano, T.M.
    Sposito, Garrison
    Martin, J.P.
    Soil Science Society of America Journal, 1988, 52 (04): : 1016 - 1019
  • [42] Estimating soil organic carbon from soil reflectance: a review
    Ladoni, Moslem
    Bahrami, Hosein Ali
    Alavipanah, Sayed Kazem
    Norouzi, Ali Akbar
    PRECISION AGRICULTURE, 2010, 11 (01) : 82 - 99
  • [43] HUMIC SUBSTANCES IN ACID ORGANIC SOILS - MODELING THEIR RELEASE TO THE SOIL SOLUTION IN TERMS OF HUMIC CHARGE
    TIPPING, E
    WOOF, C
    JOURNAL OF SOIL SCIENCE, 1990, 41 (04): : 573 - 586
  • [44] BIOSYNTHESIS OF SOIL HUMIC SUBSTANCES
    VIMAL, OP
    JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, 1971, 30 (02): : 81 - +
  • [45] STUDIES ON SOIL HUMIC SUBSTANCES
    HAYES, MHB
    JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 1985, 36 (04) : 272 - 274
  • [46] Vindication of humic substances as a key component of organic matter in soil and water
    Hayes, Michael H. B.
    Swift, Roger S.
    ADVANCES IN AGRONOMY, VOL 163, 2020, 163 : 1 - 37
  • [47] Prediction of soil carbon levels in calcareous soils of Iran by mid-infrared reflectance spectroscopy
    Sepahvand, Hanyeh
    Mirzaeitalarposhti, Reza
    Beiranvand, Kianoush
    Feizian, Mohammad
    Mueller, Torsten
    ENVIRONMENTAL POLLUTANTS AND BIOAVAILABILITY, 2019, 31 (01) : 9 - 17
  • [48] Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library
    Gomez, Cecile
    Chevallier, Tiphaine
    Moulin, Patricia
    Bouferra, Imane
    Hmaidi, Kaouther
    Arrouays, Dominique
    Jolivet, Claudy
    Barthes, Bernard G.
    GEODERMA, 2020, 375
  • [49] Potential of near-infrared reflectance spectroscopy and chemometrics to predict soil organic carbon fractions
    Cozzolino, D
    Morón, A
    SOIL & TILLAGE RESEARCH, 2006, 85 (1-2): : 78 - 85
  • [50] Soil Organic Carbon Variation in Alpine Landscape (Northern Italy) as Evaluated by Diffuse Reflectance Spectroscopy
    Colombo, Claudio
    Palumbo, Giuseppe
    Di Iorio, Erika
    Sellitto, Vincenzo Michele
    Comolli, Roberto
    Stellacci, Anna Maria
    Castrignano, Annamaria
    SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 2014, 78 (03) : 794 - 804