The Experts below are selected from a list of 8955 Experts worldwide ranked by ideXlab platform
Vittorio E. Brando - One of the best experts on this subject based on the ideXlab platform.
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assessment of water quality in lake garda italy using Hyperion
Remote Sensing of Environment, 2007Co-Authors: Claudia Giardino, Niklas Strömbeck, Arnold G. Dekker, Vittorio E. Brando, Gabriele CandianiAbstract:Abstract For testing the integration of the remote sensing related technologies into the water quality monitoring programs of Lake Garda (the largest Italian lake), the spatial and spectral resolutions of Hyperion and the capability of physics-based approaches were considered highly suitable. Hyperion data were acquired on 22nd July 2003 and water quality was assessed ( i ) defining a bio-optical model, ( ii ) converting the Hyperion at-sensor radiances into subsurface irradiance reflectances, and ( iii ) adopting a bio-optical model inversion technique. The bio-optical model was parameterised using specific inherent optical properties of the lake and light field variables derived from a radiative transfer numerical model. A MODTRAN-based atmospheric correction code, complemented with an air/water interface correction was used to convert Hyperion at-sensor radiances into subsurface irradiance reflectance values. These reflectance values were comparable to in situ reflectance spectra measured during the Hyperion overpass, except at longer wavelengths (beyond 700 nm), where reflectance values were contaminated by severe atmospheric adjacency effects. Chlorophyll-a and tripton concentrations were retrieved by inverting two Hyperion bands selected using a sensitivity analysis applied to the bio-optical model. The sensitivity analysis indicated that the assessment of coloured dissolved organic matter was not achievable in this study due to the limited coloured dissolved organic matter concentration range of the lake, resulting in reflectance differences below the environmental measurement noise of Hyperion. The chlorophyll-a and tripton image-products were compared to in situ data collected during the Hyperion overpass, both by traditional sampling techniques (8 points) and by continuous flow-through systems (32 km). For chlorophyll-a the correlation coefficient between in situ point stations and Hyperion-inferred concentrations was 0.77 (data range from 1.30 to 2.16 mg m − 3 ). The Hyperion-derived chlorophyll-a concentrations also match most of the flow-through transect data. For tripton, the validation was constrained by variable re-suspension phenomena. The correlation coefficient between in situ point stations and Hyperion-derived concentrations increased from 0.48 to 0.75 (data range from 0.95 to 2.13 g m − 3 ) if the sampling data from the re-suspension zone was avoided. The comparison of Hyperion-derived tripton concentrations and flow-through transect data exhibited a similar mismatch. The results of this research suggest further studies to address compatibilities of validation methods for water body features with a high rate of change, and to reduce the contamination by atmospheric adjacency effects on Hyperion data at longer wavelengths in Alpine environment. The transferability of the presented method to other sensors and the ability to assess water quality independent from in situ water quality data, suggest that management relevant applications for Lake Garda (and other subalpine lakes) could be supported by remote sensing.
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satellite hyperspectral remote sensing for estimating estuarine and coastal water quality
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: Vittorio E. Brando, A G DekkerAbstract:The successful launch of Hyperion in November 2000 bridged the gap between the high-resolution (spatial and spectral) airborne remote sensing and the lower resolution satellite remote sensing. Although designed as a technical demonstration for land applications, Hyperion was tested for its capabilities over a range of water targets in Eastern Australia, including Moreton Bay in southern Queensland. Moreton Bay was the only Australian Earth Observing 1 (EO-1) Hyperion coastal site used for calibration/validation activities. This region was selected due to its spatial gradients in optical depth, water quality, bathymetry, and substrate composition. A combination of turbid and humic river inputs, as well as the open ocean flushing, determines the water quality of the bay. The field campaigns were coincident with Hyperion overpasses, retrieved inherent optical properties, apparent optical properties, substrate reflectance spectra, and water quality parameters. Environmental noise calculations demonstrate that Hyperion has sufficient sensitivity to detect optical water quality concentrations of colored dissolved organic matter, chlorophyll, and suspended matter in the complex waters of Moreton Bay. A methodology was developed integrating atmospheric and hydrooptical radiative transfer models (MODTRAN-4, Hydrolight) to estimate the underwater light field. A matrix inversion method was applied to retrieve concentrations of chlorophyll, colored dissolved organic matter, and suspended matter, which were comparable to those estimated in the field on the days of the overpass.
Wenhao Zhang - One of the best experts on this subject based on the ideXlab platform.
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prediction of soil properties using laboratory vis nir spectroscopy and Hyperion imagery
Journal of Geochemical Exploration, 2013Co-Authors: Peng Lu, Linghao Li, Li Wang, Wenhao ZhangAbstract:Abstract Conventional analyses of soil characteristic are expensive and time-consuming. Visible and near infrared reflectance spectroscopy (VIS–NIR) have been useful tools for quantitative analysis of numerous soil attributes. In this study, the fidelity of spatial structure of soil attributes was evaluated by geostatistical methods and elemental concentrations were mapped using Hyperion hyperspectral reflectance data (400–2500 nm). Forty-nine soil samples were used to analyze soil organic carbon (SOC), total phosphorus (TP), pH, and cation exchange capacity (CEC). The performance of three different instrumental settings (laboratory, Hyperion and simulated Hyperion spectroscopy) was assessed using either partial least squares regression (PLSR) or stepwise multiple linear regression (SMLR). Models for SOC, TP, and pH showed moderate accuracy (R 2 > 0.6, RPD > 1.5), whereas that for CEC exhibited low efficiency (R 2
Alexander B. Mcbratney - One of the best experts on this subject based on the ideXlab platform.
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soil organic carbon prediction by hyperspectral remote sensing and field vis nir spectroscopy an australian case study
Geoderma, 2008Co-Authors: Cécile Gomez, Raphael Viscarra A Rossel, Alexander B. McbratneyAbstract:Abstract This paper compares predictions of soil organic carbon (SOC) using visible and near infrared reflectance (vis–NIR) hyperspectral proximal and remote sensing data. Soil samples were collected in the Narrabri region, dominated by Vertisols, in north western New South Wales (NSW), Australia. Vis–NIR spectra were collected over this region proximally with an AgriSpec portable spectrometer (350–2500 nm) and remotely from the Hyperion hyperspectral sensor onboard satellite (400–2500 nm). SOC contents were predicted by partial least-squares regression (PLSR) using both the proximal and remote sensing spectra. The spectral resolution of the proximal and remote sensing data did not affect prediction accuracy. However, predictions of SOC using the Hyperion spectra were less accurate than those of the Agrispec data resampled to similar resolution as the Hyperion spectra. Finally, the SOC map predicted using Hyperion data shows similarity with field observations. There is potential for the use of hyperspectral remote sensing for predictions of soil organic carbon. The use of these techniques will facilitate the implementation of digital soil mapping.
Martin Herold - One of the best experts on this subject based on the ideXlab platform.
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Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields
International Journal of Applied Earth Observation and Geoinformation, 2016Co-Authors: Saeid Hamzeh, Abd Ali Naseri, Harm Bartholomeus, Seyed Kazem Alavipanah, Martin HeroldAbstract:This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.
A G Dekker - One of the best experts on this subject based on the ideXlab platform.
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satellite hyperspectral remote sensing for estimating estuarine and coastal water quality
IEEE Transactions on Geoscience and Remote Sensing, 2003Co-Authors: Vittorio E. Brando, A G DekkerAbstract:The successful launch of Hyperion in November 2000 bridged the gap between the high-resolution (spatial and spectral) airborne remote sensing and the lower resolution satellite remote sensing. Although designed as a technical demonstration for land applications, Hyperion was tested for its capabilities over a range of water targets in Eastern Australia, including Moreton Bay in southern Queensland. Moreton Bay was the only Australian Earth Observing 1 (EO-1) Hyperion coastal site used for calibration/validation activities. This region was selected due to its spatial gradients in optical depth, water quality, bathymetry, and substrate composition. A combination of turbid and humic river inputs, as well as the open ocean flushing, determines the water quality of the bay. The field campaigns were coincident with Hyperion overpasses, retrieved inherent optical properties, apparent optical properties, substrate reflectance spectra, and water quality parameters. Environmental noise calculations demonstrate that Hyperion has sufficient sensitivity to detect optical water quality concentrations of colored dissolved organic matter, chlorophyll, and suspended matter in the complex waters of Moreton Bay. A methodology was developed integrating atmospheric and hydrooptical radiative transfer models (MODTRAN-4, Hydrolight) to estimate the underwater light field. A matrix inversion method was applied to retrieve concentrations of chlorophyll, colored dissolved organic matter, and suspended matter, which were comparable to those estimated in the field on the days of the overpass.