Table 3 - uploaded by Javier A. Concha
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The Landsat 8 satellite, recently launched (February 2013), carries the next generation of Landsat sensors and extends over 40 years of continuous imaging acquisition. Landsat 8, with its improved spectral coverage and radiometric resolution, has the potential to dramatically improve our ability to simultaneously retrieve the three primary Color Pr...
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... is expected the Landsat reflectance product will be available for Landsat 8 in the near future, so the previous step would not be necessary. Finally, the reflectance spectrum for the bright pixel including this coastal band estimation and the zenith angle correction is shown in Table 3. As was mentioned previously, the corresponding radiance spectrum for the bright pixel is obtained by applying the master PIF mask to the Landsat 8 image. ...
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... Ainsi les réflectances ρ w correspondant aux modèles présentant le moins d'écart sont conservées et moyennéesGao et al., 2000;Moulin et al., 2001;Shanmugam et Ahn, 2007;Mao et al., 2013Mao et al., , 2014Sterckx et al., 2015;Rouquié et al., 2017;De Keukelaere et al., 2018].9. Analyse statistique multivariée qui consiste à utiliser diverses méthodes d'analyses multivariées en se basant soit sur la méthode Bayésienne, soit sur la méthode d'analyse en composantes principales (PCA) ou sur la méthode de la ligne empirique afin d'avoir les prédictions statistiques de ρ w en fonction de la distribution statistique et la variation statistique de ρ A suivant les fonctions statistiques de chaque méthode[Bourdet et Frouin, 2014;Concha et Schott, 2014;Frouin et Pelletier, 2015;Saulquin et al., 2016;Thompson et al., 2016]. ...
The ocean color remote sensing involves the removal of the atmospheric contribution, the so-called atmospheric correction (AC). Over clear waters, the latter is based on the hypothesis that the sea water is totally absorbent in the Near Infra-Red (NIR), to estimate the atmospheric reflectance and to determine the water reflectance. By contrast, over coastal turbid waters, the marine signal is not negligible in the NIR. Accordingly, different alternative methods were proposed. The thesis is committed to evaluate the proposed AC algorithms and their improvement for the Ocean and Land Colour Instrument (OLCI) sensor over coastal waters exploiting its synergy with the Sea and Land Surface Temperature Radiometer (SLSTR) sensor. For this purpose, radiometric in-situ measurements were acquired in two contrasted French coastal areas : Eastern English Channel and FrenchGuiana, with the ASD spectro-radiometer, according to a newly developed measurement and post-processing protocol. The post-processing was based on the coefficient of variability and the median relative difference, in addition to the Quality Assurance Score (QAS). Following the statistical analysis in part based on the Spectral Angle Mean (SAM), the radiometric inter-comparison of the ASD and other radiometers (TriOS-above and TriOS in-water), shows the consistency of the ASD measurements. The use of these measurement leads to carry out the performance inter-comparison of five AC algorithms, where the Polymer algorithm is the most efficient according to a unique metric scoring system. However, neither algorithm obtained the maximum score, highlighting the big room for improvement, especially for coastal waters. With this in mind; three spectral relationships of aerosols reflectance were tested with a simulated data set based on OLCI/SLSTR synergy. Another relationship, Full Spectrum AC (FSAC) was initially developed combining two existing relationships, after excluding the black pixel hypothesis in the Ultra-Violet and integrating a iterative scheme. The relationships inter-comparison shows consistency ofFSAC which is slightly less performing than one published relationship. The application of FSAC on OLCI/SLSTR images could have perspectives to improve the AC over coastal waters.
... An overview of the atmospheric correction method used in the work is described by Concha and Schott (2014a), and it is explained in more detail in this paper. This is a model-based empirical line method (MoB-ELM) based on the work done for simulated OLI data by Gerace and Schott (2012). ...
New approaches need to be considered to solve the current high demand for color producing agent (CPA) retrievals over inland and coastal waters (Case 2 waters). Traditional retrieval algorithms are known to fail over highly turbid Case 2 waters because they were developed specifically for the open ocean (Case 1 waters). Landsat 8 provides an improved signal-to-noise ratio (SNR) and a new spectral coastal aerosol band in the blue. This additional information provides means to tackle this retrieval endeavor. A look-up-table (LUT) and spectrum-matching methodology was implemented to simultaneously retrieve CPAs, taking advantage of Landsat 8's new features. A LUT of spectral remote-sensing reflectances (R rs) with different concentrations of CPAs was produced using the in-water radiative transfer model HydroLight. A model-based empirical line method (MoB-ELM) algorithm was developed to atmospherically correct the Landsat 8 imagery and allow direct comparison with the LUT of R rs. This MoB-ELM atmospheric correction algorithm uses pseudo-invariant features (PIFs) from the image, ground-truth data and the HydroLight model. The retrieval algorithm was applied over three Landsat 8 scenes and shows a normalized root mean squared error (NRMSE) of about 14% for chlorophyll-a, 11% for total suspended solids (TSS), and 7% for colored dissolved organic matter (CDOM) when compared with ground-truth data. The CPA concentration maps exhibit expected trends of low concentrations in clear water and higher concentrations in turbid water.These results demonstrate that the developed algorithm allows the simultaneous mapping of concentration of all CPAs in Case 2 waters and over areas where the traditional algorithms fail or are not available due to spatial resolution. Therefore, this study shows that the Landsat 8 satellite can be utilized over Case 2 waters as long as a careful atmospheric correction is applied.
... This could be particular important for OLI since it has a lower SNR than the heritage ocean color instruments have, and so derived ocean color products can be noisy. 9 developed the model-based empirical line method (MoB-ELM) as an alternative for the standard atmospheric correction algorithms that does not rely on a negligible water-leaving signal assumption, and therefore it is not as sensitive to the SNR. The MoB-ELM algorithm uses a bright and dark pixel from the image to perform the atmospheric correction. ...
... The first atmospheric correction tested in this study was the model-based empirical line method (MoB-ELM) developed by Concha and Schott 9 for Landsat 8 imagery. The MoB-ELM method is based on the traditional empirical line method (ELM). ...
The most interaction between humankind and water occurs in coastal and inland waters (Case 2 waters) at a scale of tens or hundred of meters, but there is not yet an ocean color product at this spatial scale. Landsat 8 is a promising candidate to address the remote sensing of these kinds of waters due to its improved signal-to-noise ratio (SNR), spectral resolution, 12-bit quantization, and high spatial resolution. Standard atmospheric correction algorithms developed for heritage ocean color instruments (e.g. MODIS, SeaWiFS) require a sufficient SNR in two bands where the water-leaving signal is negligible, which is not always possible, particularly for Landsat 8's bands. The model-based empirical line method (MoB-ELM) atmospheric algorithm for Landsat 8 imagery does not rely on this assumption. In this work, we evaluate the performance of this algorithm. We compare the MoB-ELM algorithm with in situ data and with three standard atmospheric correction algorithms. The results from our algorithm are comparable with the standard algorithms in some bands when comparing remote-sensing reflectances. When compared with in situ remote-sensing reflectance, the MoB-ELM perform similar to the standard algorithm in most cases. A comparison of retrieved chlorophyll-a concentration was perform as well, showing that the MoB- ELM outperforms the rest at high concentrations commonly found in Case 2 waters. These results show that our atmospheric correction algorithm allows one to use Landsat 8 to study Case 2 waters as an alternative to heritage ocean color satellites.
... Most of the atmospheric correction algorithms applied to ocean color satellites are not suitable for highly turbid coastal waters because the black pixel assumption cannot be applied to these types of waters [2]. The atmospheric correction algorithm used in this work is a model-based empirical line method (ELM) and it is described by Concha and Schott [3]. This method is based on previous work done by Gerace et al. [1,4] for simulated OLI data. ...
Ocean color satellites (i.e. MODIS, SeaWiFS) are designed for studying large-scale water bodies and therefore perform well for retrieving water quality parameters at a global scale (e.g. MODIS Chlorophyll-a product). However, medium scale (i.e. hundred of meters) water bodies are not resolvable by these kinds of satellites due to their spatial resolution (i.e. 1000m). This work presents a methodology to address this problem using the Landsat 8 satellite for color producing agents (CPAs) (chlorophyll-a, sediments and colored dissolved organic matter (CDOM)) retrieval over inland and coastal waters (Case 2 waters). First, a model-based empirical line method (ELM) is used to correct the Landsat 8 imagery for atmospheric effects, having water reflectance pixels as output. Then, the concentration of these water pixels are estimated by a nonlinear optimization routine that searches in a look-up table (LUT) created with the in water radiative transfer model, Hydrolight.
The Geospatial Information System (GIS) and remote sensing being very modern and effective environmental assessment tools provide major sources of spatial information about the Earth surface’s cover and constitution by using different sensors in order to capture sufficient and relevant information of the different components of the Earth. The scientists across the world have been using these techniques on standardizing them for monitoring the environmental changes based on spatial information in tune with the requirements of a particular region in well-timed and cost-effective manner. These techniques have been applied successfully for monitoring wetlands by acquiring spatial information in a timely manner during the last five decades. Remote sensing for the assessment of environmental status involves the techniques for the measurement, of any spectral object of the Earth’s surface and atmosphere with distinct features by some special instruments carried by satellites or aircraft. All the generated data and information are used to infer the environmental status with proper quantified interpretations. The successful application of remote sensing techniques to understand several ecological states of wetlands has become emerged as a popular means to study environment and ecology. The eco-dynamics of wetlands are determined by the seasonal oscillation of physico-chemical parameters of the water bodies along with the areal dynamicity. Multispectral satellite data and various band algebra could provide valuable synoptic data on the dynamics of wetlands. This chapter presents basics of the uses of GIS and remote sensing technologies for wetland mapping and monitoring. Few case studies are being highlighted to depict the scopes of applicability of medium-resolution digital satellite data for mapping of different types of wetlands along with surface hydrologic flow pathways. Remote sensing-satellite imageries have been used successfully to delineate the different environmental characteristics of the wetlands in respect of their extent, geomorphology and ecological features. Quantification of major water quality parameters and valuation of ecosystem service of wetland ecosystem in different time scales not only substantiated the ground truth information but also opened up new vistas in classifying different water bodies constituting the EKW into different categories based on their sizes, like small, medium, large, etc., by evaluating the trend and extent of shrinkages of wetlands. In such context, it can be concluded that simultaneous recording of biodiversity and physico-chemical parameters through ground truth study along with application of GIS and remote sensing to record the ecological changing patterns can justify the need of undertaking basic of holistic approach towards integrated eco-management of the sensitive, vulnerable and fragile wetland ecosystems.
Frequent and continuous water quality monitoring of Olushandja Dam in Namibia is needed to inform timely decision making. This study was carried out from November 2014 to June 2015 with Landsat 8 reflectance values and field measured water quality data that were used to develop regression-analysis-based retrieval algorithms. Water quality parameters considered included turbidity, total suspended solids (TSS), nitrates, ammonia, total nitrogen (TN), total phosphorus (TP) and total algae counts. Results show that turbidity levels exceeded the recommended limits for raw water for potable water treatment while TN and TP values are within acceptable values. Turbidity, TN, and TP and total algae count showed a medium to strong positive linear relationship between Landsat predicted and measured water quality data while TSS showed a weak linear relationship. The regression coefficients between predicted and measured values were: turbidity (R2 = 0.767); TN (R2 = 0.798,); TP (R2 = 0.907); TSS (R2 = 0.284,) and total algae count (R2 = 0.851). Prediction algorithms are generally the best fit to derive water quality parameters. Remote sensing is recommended for frequent and continuous monitoring of Olushandja Dam as it has the ability to provide rapid information on the spatio-temporal variability of surface water quality. HIGHLIGHTS
Over past years, frequent and continuous water quality monitoring has been problematic in Namibia.;
A linear regression can now be used to develop algorithms for retrieving water quality data.;
Good prediction accuracy for turbidity, TN, TP and total algae count.;
More sampling points needed to further improve regression model accuracy.;
Remote sensing provides rapid information on water quality spatio-temporal variability.;
Dissertation Defense title: The Use of Landsat 8 for Monitoring of Inland and Coastal Waters
