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See detailSoil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness
Denis, Antoine ULg; Stevens, Antoine; Van Wesemael, Bas et al

in Geoderma (2014), 226-227(August 2014), 94102

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC ... [more ▼]

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC assessment (SOCa) is usually done by using calibration samples and multivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect. A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD) spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas. Moreover, the study of laboratory ASD reflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire [350–2500 nm] spectral range was computed to translate this differential influence. Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa. [less ▲]

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See detailMEASURING SOIL ORGANIC CARBON IN CROPLANDS AT REGIONAL SCALE USING AIRBORNE IMAGING SPECTROSCOPY
Stevens, Antoine; Udelhoven, Thomas; Denis, Antoine ULg et al

in Geoderma (2010), 158

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 ... [more ▼]

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. [less ▲]

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See detailMonitoring soil organic carbon in croplands using imaging spectroscopy (moca project)
Stevens, Antoine; van Wesemael, Bas; Tychon, Bernard ULg et al

Conference (2008, February 12)

The detection of changes in soil organic carbon (SOC) concentration is essential in both the assessment of SOC sequestration and soil quality. Within the EU soil thematic strategy the depletion of organic ... [more ▼]

The detection of changes in soil organic carbon (SOC) concentration is essential in both the assessment of SOC sequestration and soil quality. Within the EU soil thematic strategy the depletion of organic matter is mentioned as one of the major threats to the soil resource. As one of the first countries Luxemburg has taken the initiative to monitor the SOC concentration of individual fields to allow for eventual CO2 credits and as an indicator for good agro-ecological conditions (GAEC). The aim of this project is to develop an efficient and operational methodology to detect SOC changes in croplands using Imaging Spectroscopy and to map the SOC contents of croplands with high resolution and minimal calibration. [less ▲]

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