Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 400204
Title Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy
Author(s) Bartholomeus, H.; Kooistra, L.; Stevens, A.; Leeuwen, M. van; Wesemael, B. van; Ben-Dor, E.; Tychon, B.
Source International Journal of applied Earth Observation and Geoinformation 13 (2011)1. - ISSN 0303-2434 - p. 81 - 88.
DOI http://dx.doi.org/10.1016/j.jag.2010.06.009
Department(s) Laboratory of Geo-information Science and Remote Sensing
Chair Disaster Studies
PE&RC
WASS
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) infrared reflectance spectroscopy - least-squares regression - spectral reflectance - aviris data - indexes - tm - spectrometry - parameters - prospect - nitrogen
Abstract Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.
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