Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces

Ouerghemmi, Walid; Gomez, Cécile; Naceur, Saber Mohamed; Lagacherie, Philippe

doi:10.1016/j.geoderma.2011.04.019

Cited by 44 publications

(37 citation statements)

References 36 publications

(46 reference statements)

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“…Only two studies in remote sensing soil mapping have focused on diversified surface conditions, including partially vegetated surfaces (Bartholomeus et al, 2010;Ouerghemmi, Gomez, Naceur, & Lagacherie, 2011), and performed marginally well. Therefore, in the absence of effective techniques for predicting soil properties over semi-vegetated pixels, having a suitable mapping surface remains an important criterion of hyperspectral image quality.…”

Section: Band Selections and Urban Vegetation And Water Masksmentioning

confidence: 99%

Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery

Gomez

Oltra-Carrió

Bacha³

et al. 2015

Remote Sensing of Environment

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Section: Band Selections and Urban Vegetation And Water Masksmentioning

confidence: 99%

Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery

Gomez

Oltra-Carrió

Bacha³

et al. 2015

Remote Sensing of Environment

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“…Recently, partial least squares regression (PLSR) has been used widely to estimate soil properties (Bartholomeus et al, 2011;Farifteh et al, 2007;Ge, Morgan, & Ackerson, 2014;Ouerghemmi, Gomez, Naceur, & Lagacherie, 2011). If it is proven that surface reflectance and subsurface soil salt content are related, PLSR may serve as an alternative for quantifying the soil salinity of subsurface soil.…”

Section: Discussionmentioning

confidence: 99%

Can subsurface soil salinity be predicted from surface spectral information? – From the perspective of structural equation modelling

Liu

Pan

Wang

et al. 2016

Biosystems Engineering

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“…Mineral composite maps, including ferrous minerals, iron oxide and clay minerals, could be produced using remotely sensed data such as Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) through composite indices [18][19][20][21]. Other researchers [8,22] have used hyper spectral data for clay content prediction, calcium carbonate (CaCO3), iron and cation-exchange capacity (CEC). Models for soil attribute prediction from spectral data, also named spectral transfer functions, have been developed recently [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

et al. 2015

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Clay content (fraction < 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season. Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a OPEN ACCESSRemote Sens. 2015, 7 6060 time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R 2 = 0.65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas. In-situ observations at those locations showed that the Landsat TM mapping was more consistent with observations than the TerraSAR-X mapping.

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Applying blind source separation on hyperspectral data for clay content estimation over partially vegetated surfaces

Cited by 44 publications

References 36 publications

Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery

Evaluating the sensitivity of clay content prediction to atmospheric effects and degradation of image spatial resolution using Hyperspectral VNIR/SWIR imagery

Can subsurface soil salinity be predicted from surface spectral information? – From the perspective of structural equation modelling

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

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