In Situ VIS-NIR Spectroscopy for a Basic and Rapid Soil Investigation

被引:6
|
作者
Debaene, Guillaume [1 ]
Bartminski, Piotr [2 ]
Siluch, Marcin [2 ]
机构
[1] State Res Inst, Inst Soil Sci & Plant Cultivat, Dept Soil Sci Eros & Land Protect, Ul Czartoryskich 8, PL-24100 Pulawy, Poland
[2] Marie Curie Sklodowska Univ, Inst Earth & Environm Sci, Dept Geol Soil Sci & Geoinformat, Ul Krasnicka 2cd, PL-20718 Lublin, Poland
关键词
field measurements; near-infrared spectroscopy; PLS; SVM; soil properties; soil mapping; NEAR-INFRARED SPECTROSCOPY; PARTIAL LEAST-SQUARES; ORGANIC-CARBON; MOISTURE-CONTENT; PREDICTION; FIELD; REGRESSION; PRETREATMENTS; TEMPERATURE; SPECTRA;
D O I
10.3390/s23125495
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Visible and near-infrared (VIS-NIR) spectroscopy is extensively used in the field of soil science to predict several soil properties, mostly in laboratory conditions. When measured in situ, contact probes are used, and, very often, time-consuming methods are applied to generate better spectra. Unfortunately, spectra obtained by these methods differ greatly from spectra remotely acquired. This study tried to address this issue by measuring reflectance spectra directly with a fibre optic or a 4 & DEG; lens on bare untouched soils. C, N content and soil texture (sand, silt, and clay) prediction models were established using partial least-square (PLS) and support vector machine (SVM) regression. With spectral pre-processing, some satisfactory models were obtained, i.e., for C content (R-2 = 0.57; RMSE = 0.09%) and for N content (R-2 = 0.53; RMSE = 0.02%). Some models were improved when using moisture and temperature as auxiliary data for the modelling. Maps of C, N and clay content generated with laboratory and predicted values were presented. Based on this study, VIS-NIR spectra acquired with bare fibre optic and/or a 4 & DEG; lens could be used to build prediction models in order to obtain basic preliminary information on soil composition at the field scale. The predicting maps seem suitable for a fast but rough field screening.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] Feasibility of Vis-NIR spectroscopy approach to predict soil biological attributes in arid land soils
    Hosseini, Elias
    Zarei, Mehdi
    Moosavi, Ali Akbar
    Ghasemi-Fasaei, Reza
    Baghernejad, Majid
    Mozaffari, Hasan
    PLOS ONE, 2024, 19 (09):
  • [22] Effect of calibration set size on prediction at local scale of soil carbon by Vis-NIR spectroscopy
    Luca, Federica
    Conforti, Massimo
    Castrignano, Annamaria
    Matteucci, Giorgio
    Buttafuoco, Gabriele
    GEODERMA, 2017, 288 : 175 - 183
  • [23] Development of a soil fertility index using on-line Vis-NIR spectroscopy
    Munnaf, Muhammad Abdul
    Mouazen, Abdul Mounem
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 188
  • [24] On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning
    Nawar, S.
    Mouazen, A. M.
    SOIL & TILLAGE RESEARCH, 2019, 190 : 120 - 127
  • [25] Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques
    Douglas, R. K.
    Nawar, S.
    Alamar, M. C.
    Mouazen, A. M.
    Coulon, F.
    SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 616 : 147 - 155
  • [26] Evaluation and implementation of vis-NIR spectroscopy models to determine workability
    Mahmood, Hafiz Sultan
    Bartholomeus, Harm M.
    Hoogmoed, Willem B.
    van Henten, Eldert J.
    SOIL & TILLAGE RESEARCH, 2013, 134 : 172 - 179
  • [27] Estimation of andic properties from Vis-NIR diffuse reflectance spectroscopy for volcanic soil classification
    Di Iorio, Erika
    Circelli, Luana
    Lorenzetti, Romina
    Costantini, Edoardo A. C.
    Egendorf, Sara Perl
    Colombo, Claudio
    CATENA, 2019, 182
  • [28] Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: A Meta-Analysis
    Chinilin, A. V.
    Vindeker, G. V.
    Savin, I. Yu.
    EURASIAN SOIL SCIENCE, 2023, 56 (11) : 1605 - 1617
  • [29] Mapping the Salt Content in Soil Profiles using Vis-NIR Hyperspectral Imaging
    Wu, Shiwen
    Wang, Changkun
    Liu, Ya
    Li, Yanli
    Liu, Jie
    Xu, Aiai
    Pan, Kai
    Li, Yichun
    Pan, Xianzhang
    SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 2018, 82 (05) : 1259 - 1269
  • [30] Prediction of soil organic carbon for different levels of soil moisture using Vis-NIR spectroscopy
    Nocita, Marco
    Stevens, Antoine
    Noon, Carole
    van Wesemael, Bas
    GEODERMA, 2013, 199 : 37 - 42