[en] Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible spectrometers could provide the large amount of spatial data needed. In particular, the ability of airborne imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution represents an opportunity for improving the monitoring of soil characteristics and soil threats such as the decline of soil organic matter in the topsoil. However, airborne imaging spectroscopy has been generally applied over small areas with homogeneous soil types and surface conditions. Here, five hyperspectral images acquired with the AHS-160 sensor (430 nm–2540 nm) were analysed with the objective to map soil organic carbon (SOC) at a regional scale. The study area, covering a surface of ∼420 km2 and located in Luxembourg, is characterized by different soil types and a high variation in SOC contents. Reflectance data were related to surface SOC contents of bare croplands by means of 3 different multivariate calibration techniques: partial least square regression (PLSR), penalized-spline signal regression (PSR) and support vector machine regression (SVMR). The performance of these statistical tools was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to agro-geological zones, soil type and image number). Under global calibration, the Root Mean Square Error in the Predictions reached 5.3–6.2 g C kg−1. Under local calibrations, this error was reduced by a factor up to 1.9. SOC maps of bare agricultural fields were produced using the best calibration model. Two map excerpts were shown, which display intra- and inter-field variability of SOC contents possibly related to topography and land management.
Stevens, Antoine; Université Catholique de Louvain - UCL > Département de géographie
Udelhoven, Thomas
Denis, Antoine ; Université de Liège - ULiège > Département des sciences et gestion de l'environnement > Département des sciences et gestion de l'environnement
Tychon, Bernard ; Université de Liège - ULiège > Département des sciences et gestion de l'environnement > Département des sciences et gestion de l'environnement
Lioy, Rocoo
Hoffmann, Lucien
Van Wesemael, Bas
Language :
English
Title :
Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy
Alternative titles :
[fr] Mesure du carbone organique des soils en terrains agricoles à l'échelle régionale par spectroscopie aéroportée
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Bajwa S.G., and Tian L.F. Soil fertility characterization in agricultural fields using hyperspectral remote sensing. Transactions of the ASAE 48 (2005) 2399-2406
Barnes R.J., Dhanoa M.S., and Lister S.J. Standard normal variation transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy 43 (1989) 772-777
Bartholomeus H.M., Schaepman E.M., Kooistra L., Stevens A., Hoogmoed B.W., and Spaargaren O.S.P. Spectral reflectance based indices for soil organic carbon quantification. Geoderma 145 (2008) 28-36
Ben-Dor E., and Banin A. Near-infrared analysis (NIRA) as a method to simultaneously evaluate spectral featureless constituents in soils. Soil Science 159 (1995) 259-270
Ben-Dor E., Irons J.R., and Epema J.F. Soil reflectance. In: Rencz N. (Ed). Remote sensing for the Earth Science (1999), Wiley and Sons Inc., New-York, USA 111-188
Ben-Dor E., and Levin N. Determination of surface reflectance from raw hyperspectral data without simultaneous ground data measurements: a case study of the GER 63-channel sensor data acquired over Naan, Israel. International Journal of Remote Sensing 21 (2000) 2053-2074
Ben-Dor E., Patkin K., Banin A., and Karnieli A. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel. International Journal of Remote Sensing 23 (2002) 1043-1062
Ben-Dor E., Kindel B., and Goetz A.F.H. Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data. Remote Sensing of Environment 90 (2004) 389-404
Ben-Dor E., Taylor R.G., Hill J., Dematte J.A.M., Whiting M.L., Chabrillat S., and Sommer S. Imaging spectrometry for soil applications (2008) 321+
Berk A., Anderson G.P., Acharya P.K., Chetwind J.H., Bernstein L.S., Shettle E.P., Matthew M.W., and Alder-Golden S.M. Modtran4 User's Manual (1999), Air Force Research Laboratory, Hanscom, USA 93
Brown D.J., Bricklemyer R.S., and Miller P.R. Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana. Geoderma 129 (2005) 251-267
Cécillon L., Cassagne N., Czarnes S., Gros S., Vennetier M., and Brun J.J. Predicting soil quality indices with near infrared analysis in a wildfire chronosequence. Science of the Total Environment 407 (2008) 1200-1205
Canu S., Grandvalet Y., Guigue V., and Rakotomamonjy A. SVM and Kernel Methods Matlab Toolbox. Perception Systèmes et Information (2005), INSA de Rouen, Rouen, France
Chang C.W., Laird D.A., Mausbach M.J., and Hurburgh C.R. Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society of America Journal 65 (2001) 480-490
Chang G.W., Laird D.A., and Hurburgh C.R. Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties. Soil Science 170 (2005) 244-255
Clark R.N., Gallagher A.J., and Swayze G.A. Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-squares fit with library reference spectra. Proceedings of the Second Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop. JPL Publication 90-54 (1990), Pasadena, USA 176-186
Cohen M., Mylavarapu R.S., Bogrekci I., Lee W.S., and Clark M.W. Reflectance spectroscopy for routine agronomic soil analyses. Soil Science 172 (2007) 469-485
Cristianini N., and Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods (2003), Cambridge University Press, New-York
Dalal R.C., and Henry R.J. Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Science Society of America Journal 50 (1986) 120-123
De Gryze S., Six J., Bossuyt H., Kristof V.O., and Merckx R. The relationship between landform and the distribution of soil C, N and P under conventional and minimum tillage. Geoderma 144 (2008) 180-188
De Tar W.R., Chesson J.H., Penner J.V., and Ojala J.C. Detection of soil properties with airborne hyperspectral measurements of bare fields. Transactions of the ASABE 51 (2008) 463-470
Eilers P.H.C., and Marx B.D. Flexible smoothing with B-splines and penalties. Statistical Science 11 (1996) 89-102
Eilers P.H.C. A perfect smoother. Analytical Chemistry 75 (2003) 3631-3636
Faber N.M., and Rajko R. How to avoid over-fitting in multivariate calibration - The conventional validation approach and an alternative. Analytica Chimica Acta 595 (2007) 98-106
FAO. World reference base for soil resources. World Soil Resources Report 84 (1998), FAO, Rome, Italy
Follain S., Minasny B., McBratney A.B., and Walter C. Simulation of soil thickness evolution in a complex agricultural landscape at fine spatial and temporal scales. Geoderma 133 (2006) 71-86
Goidts E., and van Wesemael B. Regional assessment of soil organic carbon changes under agriculture in Southern Belgium (1955-2005). Geoderma 141 (2007) 341-354
Gomez C., Rossel R.A.V., and McBratney A.B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 146 (2008) 403-411
Henderson T.L., Szilagyi A., Baumgardner M.F., Chen C.C.T., and Landgrebe D.A. Spectral band selection for classification of soil organic matter content. Soil Science Society of America Journal 53 (1989) 1778-1784
Henderson T.L., Baumgardner M.F., Franzmeier D.P., Stott D.E., and Coster D.C. High dimensional reflectance analysis of soil organic-matter. Soil Science Society of America Journal 56 (1992) 865-872
Idowu O.J., van Es H.M., Abawi G.S., W., W. D., Ball J.I., Gugino B.K., Moebius B.N.a., Schindelbeck R.R., and Bilgili A.V. Farmer-oriented assessment of soil quality using field, laboratory, and VNIR spectroscopy methods. Plant and Soil 307 (2008) 243-253
Koshoubu J., Iwata T., and Minami S. Elimination of the uninformative calibration sample subset in the modified UVE (Uninformative Variable Elimination)-PLS (Partial Least Squares) method. Analytical Sciences 17 (2001) 319-322
Lagacherie P., Baret F., Feret J., Netto J.M., and Robbez-Masson J.M. Estimation of soil clay and calcium carbonate using laboratory, field and airborne hyperspectral measurements. Remote Sensing of Environment 112 (2008) 825-835
Lobell D.B., and Asner G.P. Moisture effects on soil reflectance. Soil Science Society of America Journal 66 (2002) 722-727
Malley D., Martin P., and Ben-Dor E. Application in analysis of soils. In: Roberts C.A., Workman J., and Reeves III J.B. (Eds). Near-Infrared Spectroscopy in Agriculture. A Three Society Monograph (ASA, SSSA, CSSA), Madison, USA (2004) 729-784
Marx B.D., and Eilers P.H.C. Generalized linear regression on sampled signals and curves: A P-spline approach. Technometrics 41 (1999) 1-13
Marx B.D., and Eilers P.H.C. Multivariate calibration stability: a comparison of methods. Journal of Chemometrics 16 (2002) 129-140
Norris K.H., and Williams P.C. Optimization of mathematical treatments of raw near-infrared signal in the measurement of protein in hard red spring wheat. 1. Influence of particle-size. Cereal Chemistry 61 (1984) 158-165
Odlare M., Svensson K., and Pell M. Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field. Geoderma 126 (2005) 193-202
Palacios-Orueta A., and Ustin S.L. Remote sensing of soil properties in the Santa Monica Mountains. I. Spectral analysis. Remote Sensing of Environment 65 (1999) 170-183
Patzold S., Mertens F.M., Bornemann L., Koleczek B., Franke J., Feilhauer H., and Welp G. Soil heterogeneity at the field scale: a challenge for precision crop protection. Precision Agriculture 9 (2008) 367-390
Powlson D.S., Smith P., Coleman K., Smith J.U., M. J, G., Korschens M., and Franko U. A European network of long-term sites for studies on soil organic matter. Soil and Tillage Research 47 (1998) 263-274
R Development Core Team. R: A language and environment for statistical computing (2007), R Foundation for Statistical Computing, Vienna, Austria. 3-900051-07-0. http://www.R-project.org URL: http://www.R-project.org
Richter R., Schlapfer D., and Muller A. An automatic atmospheric correction algorithm for visible/NIR imagery. International Journal of Remote Sensing 27 (2006) 2077-2085
Rodger A., and Lynch M.J. Determining atmospheric column water vapour in the 0.4-2.5 μm spectral region. Proceedings of the AVIRIS Workshop 2001 (2001), Pasadena, California, USA
Rossel R.A.V., Walvoort D.J.J., McBratney A.B., Janik L.J., and Skjemstad J.O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131 (2006) 59-75
Savitzky A., and Golay M.J.E. Smoothing and differentiation of data by simplified least-square procedures. Analytical Chemistry 36 (1964) 1627-1638
Schölkopf B., and Smola A. Learning with Kernels. Massachusetts Institute of Technology (2002), MIT Press
Selige T., Boehner J., and Schmidhalter U. High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma 136 (2006) 235-244
Shenk J.S., and Westerhaus M.O. Population structuring of near-infrared spectra and modified partial least-squares regression. Crop Science 31 (1991) 1548-1555
Smith P. Monitoring and verification of soil carbon changes under Article 3.4 of the Kyoto Protocol. Soil Use and Management 20 (2004) 264-270
Sorensen L.K., and Dalsgaard S. Determination of clay and other soil properties by near infrared spectroscopy. Soil Science Society of America Journal 69 (2005) 159-167
Stevens A., Van Wesemael B., Vandenschrick G., Toure S., and Tychon B. Detection of carbon stock change in agricultural soils using spectroscopic techniques. Soil Science Society of America Journal 70 (2006) 844-850
Stevens A., van Wesemael B., Bartholomeus H., Rosillon D., Tychon B., and Ben-Dor E. Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma 144 (2008) 395-404
Stoner E.R., and Baumgardner M.F. Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal 45 (1981) 1161-1165
Udelhoven T., Emmerling C., and Jarmer T. Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-square regression: a feasibility study. Plant and Soil 251 (2003) 319-329
Uno Y., Prasher S.O., Patel R.M., Strachan I.B., Pattey E., and Karimi Y. Development of field-scale soil organic matter content estimation models in Eastern Canada using airborne hyperspectral imagery. Canadian Biosystems Engineering 47 (2005) 9-14
Vapnik V.N. The nature of statistical learning theory. Information Science and Statistics (1995), Springer, New York
Van Waes C., Mestdagh I., Lootens P., and Carlier L. Possibilities of near infrared reflectance spectroscopy for the prediction of organic carbon concentrations in grassland soils. Journal of Agriculture Science 143 (2005) 487-492
van Wesemael B., Rambaud X., Poesen J., Mark M., Cammeraat E., Stevens, and Stevens A. Spatial patterns of land degradation and their impacts on the water balance of rainfed treecrops: a case study in South East Spain. Geoderma 133 (2006) 43-56
Vasques G.M., Grunwald S., and Sickman J.O. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146 (2008) 14-25
Venkoba Rao B., and Gopalakrishna S.J. Hardgrove grindability index prediction using support vector regression. International Journal of Mineral Processing 9 (2009) 55-59
Wehrens, R. and Mevik, B.-H., 2007. pls: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). R package version 2.1-0, URL: http://mevik.net/work/software/pls.html.
Wold S., Sjostrom M., and Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58 (2001) 109-130
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.