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

Gomez, Cécile; Oltra-Carrió, Rosa; Bacha, Sinan; Lagacherie, Philippe; Briottet, Xavier

doi:10.1016/j.rse.2015.02.019

Cited by 60 publications

(34 citation statements)

References 67 publications

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“…More specific for location A coefficient of correlation is R The correlation between swelling pressure and the quotient (ratio) of liquid limit minus moisture content divided by liquid limit minus shrinkage limit, (Figures 8-10 The Multiple regression analyses between swelling pressure and free swell with the rest five water depended variableς, liquid limit (LL), clay content (2 μm), free swell index (FS), bar linear shrinkage (LS), water content (WC) ( Table 5 it is observed that swell pressure and free swell index are equally predicted using the considered factors [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Resultsmentioning

confidence: 99%

Engineering Properties and Shrinkage Limit of Swelling Soils in Greece

Christodoulias¹

2015

J Earth Sci Clim Change

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Section: Resultsmentioning

confidence: 99%

Engineering Properties and Shrinkage Limit of Swelling Soils in Greece

Christodoulias¹

2015

J Earth Sci Clim Change

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“…Like Steinberg et al [6], Lu et al [3] predicted SOC, but also other soil properties, such as total phosphorus content, pH and cation exchange capacity and obtained moderate accuracy. Furthermore, clay content could be accurately predicted with data from 5-30-m spatial resolution, such as SHALOM, PRISMA, EnMAP, HyspIRI and HypXIM images [125].…”

Section: Soil Applicationsmentioning

confidence: 99%

“…Small targets such as urban areas are also difficult to identify [140], while finer resolution sensors (i.e., TG-1 of 10-m GSD) provided encouraging results for mapping complex urban land cover [142]. For these reasons, the even coarser HyspIRI GSD of 60 m was pointed out by many studies [42,101,118,119,123,125,152,183,184] and was therefore recently reduced to reach 30 m [34,155]. However, this reduction may not be adequate enough to study small and patchy areas such as coastal wetlands [155].…”

Section: Spatial Resolutionmentioning

confidence: 99%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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“…These limitations include: PLS models that need manual fine-tuning, use of non transferable local/regional soil models, the need of local ground truth databases, and also the effects of noise (vegetation, moisture, roughness, etc.). For this, recent studies looked at the potential of the future EnMAP sensor for local land cover and vegetation mapping based on simulated EnMAP data [29,30], and one study looked at the potential of future sensors for soil properties mapping based on noise-and spatially-degraded spectral images enhancing the effects of spatial scale resolution [31]. In addition, few studies looked at the issue of the operationability of the predictions linked with harmonized methodologies for applications at regional to global scale [32][33][34][35], or at the issue of whether many local/regional soil prediction models or a global model could be set up [36].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

et al. 2016

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Abstract:With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fe d ), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R 2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R 2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.

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

Cited by 60 publications

References 67 publications

Engineering Properties and Shrinkage Limit of Swelling Soils in Greece

Engineering Properties and Shrinkage Limit of Swelling Soils in Greece

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

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