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a Université catholique de Louvain, Département de Géographie, Place Pasteur, 3, 1348 Louvain-La-Neuve, Belgium
b Univerité de Liège, Campus d'Arlon, Département des Sciences et Gestion de l'Environnement, Av. de Longwy, 185, 6700 Arlon, Belgium
* Corresponding author (stevens{at}geog.ucl.ac.be)
Soil organic carbon (SOC) represents one of the major pools in the global C cycle. Therefore, even small changes in SOC stocks cause important CO2 fluxes between terrestrial ecosystems and the atmosphere. However, SOC stocks are difficult to quantify accurately due to their high spatial variability. The aim of this paper is to evaluate the potential of Imaging Spectroscopy (IS) using the Compact Airborne Spectrographic Imager (CASI; 405950 nm) and field spectroscopy with an Analytical Spectral Devices spectrometer (ASD; 3502500 nm) to measure SOC content in heterogeneous agricultural soils. We used both stepwise and partial least square (PLS) regression analysis to relate spectral measurements to SOC contents. Standard Error of Prediction (SEP) for the ASD ranged from 2.4 to 3.3 g C kg1 depending on soil moisture content of the surface layer. Imaging spectroscopy performed poorly, mainly due to the narrow spectral range of the CASI. Tests using both the CASI and the Shortwave infrared Airborne Spectrographic Imager (SASI; 9002500 nm) showed better results. The variation in soil texture and soil moisture content degrades the spectral response to SOC contents. Currently, SEP allows to detect a SOC stock change of 7.29.9 Mg C ha1 in the upper 30 cm of the soil, and is therefore still somewhat high in comparison with changes in SOC stocks as a result of management or land conversion (0.31.9 Mg C ha1 yr1). A detailed SOC maps produced by IS reflected the patterns in SOC contents due to the recent conversion from grassland to cropland.
Abbreviations: ASD, analytical spectral devices spectrometer ASDd, ASD data in dry conditions CASI, Compact Airborne Spectrographic Imager IS, imaging spectroscopy PLS, partial least square RPD, ratio of performance to deviation SASI, Shortwave infrared Airborne Spectrographic Imager SD, standard deviation SEC, standard error of calibration SEL, standard error of laboratory measures SEP, standard error of prediction SOC, soil organic carbon VIS-NIR-SWIR, visiblenear infraredshort wave infrared
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F. Chen, D. E. Kissel, L. T. West, W. Adkins, D. Rickman, and J. C. Luvall Mapping Soil Organic Carbon Concentration for Multiple Fields with Image Similarity Analysis Soil Sci. Soc. Am. J., January 11, 2008; 72(1): 186 - 193. [Abstract] [Full Text] [PDF] |
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