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LMDCZ project: 3D Modeling (WP2)

Satellite Image Analysis

Analysing satellite images for turbidity distribution in LMDCZ based on available satellite images

1. Introduction

Due to high variability of the physical and biogeochemical processes occurring in coastal areas, traditional approaches based on oceanographic cruises and in situ time series, although essential, are very time-consuming, expensive and sometimes uncertain to yield meaningful results on the studied phenomena, especially at a large synoptic scale. In this context, remote sensing of biological and physical parameters is a very powerful tool for performing large scale studies. Satellite data are not as accurate as in situ measurements and are limited to the surface layer. However, the latter limitations are largely compensated by the spatial and temporal coverage offered by the satellite observations. In situ data, such as those gathered in the frame of this project, but also from previous oceanographic cruises performed in the Mekong delta areas, remain necessary to validate the satellite products.

The interaction of light field within visible part of spectrum (i.e. 400-700 nm) with different optically significant constituents of sea water (water molecules, salt, particulate and organic dissolved matters) modifies the color of the water. These spectral variations, which bring qualitative and quantitative information about water constituents, can be recorded by a passive radiometric sensor onboard a satellite platform. However, the conversion of the radiometric signal measured by the sensor at the top of atmosphere (TOA) to the end-user parameters is not straightforward. The contribution of reflected photons at air-sea interface and atmosphere should first be removed from the TOA measured signal to assess the remote sensing reflectance, Rrs(λ), which is the only radiometric parameter encompassing useful information on the water mass composition (λ represents wavelength of light in nanometer, nm). The removal of the atmospheric path radiance, named atmospheric correction, represents the most challenging part of the ocean color atmospheric correction procedure (Gordon and Wang, 1994). This signal, including Rayleigh and aerosols components, can contribute to up to 90% of the TOA signal depending on λ, the geometry of illumination and observation, the aerosol optical thickness, and the water leaving signal (Antoine and Morel, 1999). The importance of the latter task is particularly crucial when considering the level of accuracy required for being able to derive accurate estimates of the desired water components from Rrs(λ). In coastal waters, such as the one studied in the frame of this project, atmospheric corrections are very challenging as standard approaches developed for open ocean waters and based on a zero Rrs(λ) signal in the near infrared are no longer applicable (due to highly scattering properties of suspended sediments).

The retrieval of suspended particulate matter concentration, SPM, (in g.m -3) from the remote sensing reflectance spectral values is performed through bio-optical algorithms. Considering that about 90%

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of the Rrs(λ) signal originates from the upper layer of the water column (the so-called first attenuation layer), SPM which can be retrieved from Rrs(λ) is assumed to be weighted averaged parameter within this upper layer. The thickness of this water layer in the visible part of the spectrum typically varies from less than one meter (as in turbid waters, or in the red part of the spectrum) to about 60 meters (as for oligotrophic waters in the green), depending the amount of optically significant constituents within the water mass and the measured light wavelength. Considering the level of turbidity of the studied area, the SPM retrieved values over coastal waters impacted by the Mekong river are only representative of the surface layer (about one meter).

The main objective of this task is to provide SPM satellite maps for the validation/calibration of the sediment transport model for the whole study area. For that purpose, satellite data sets available, as well as, different algorithms used to assess SPM from the TOA signal (the one measured by the sensor aboard the satellite) will be presented. The choice of final algorithm will be based on the temporal coverage and the validation of the SPM products relied on availability of in situ SPM data set. At last, the temporal patterns of SPM will be presented.

2. Satellite data sets

High (about 20 meters) and medium (1 km) spatial resolutions of the satellite observations are available for the study area. However, due to the small swath of high spatial resolution available sensor (OLI on Landsat-8), the temporal coverage is relatively poor (with one observation every 15 days, in absence of clouds), standard ocean color sensors, with almost daily temporal coverage, are privileged for this study. For that purpose, two different sensors are used: MERIS (2002-2012) and VIIRS (2013-now) sensors. A long time series of MERIS allows temporal patterns to be extracted, while VIIRS sensor images allow to compare the generated SPM maps with in-situ measurements performed over the study area in July 2014.

3. Selection of different algorithms applied to these data sets

The standard atmospheric correction algorithms, used to assess the remote sensing reflectance, Rrs(), which bring information on water optically significant components, from the TOA signal , is based on exploitation of near infrared bands where water signal is assumed to be negligible, the table of aerosols, and the extrapolation of such information towards the visible (Gordon and Wang, 1994; Antoine and Morel, 1999). The MERIS sensor does not have a tilting capacity, which makes it drastically impacted by sun glint. Observations over sub-tropical areas are then contaminated by bright patterns due to the specular reflexion of the sun by wavy sea surface. It results in a large reduction of the spatial coverage at such latitudes by nearly a half, when standard atmospheric corrections are used. For that purpose, we will use the POLYMER algorithm (Steinmetz et al., 2011), which allows to recover Rrs() over areas impacted by sun glint. This algorithm, originally developed for open ocean waters, has been selected among others to process MERIS and MODIS data in the Ocean Colour Climate Change Initiative (OC-CCI), an international inter-comparison exercises of all existing algorithms aiming at getting a uniform ocean color data set for climate studies. In OC-CCI,

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POLYMER has shown to be robust to perturbation by sun glint, thin clouds, adjacency effect, absorbing aerosols, and significant improved the spatial coverage.

The standard version of POLYMER has been developed for open ocean waters, where the black pixel assumption in the NIR is verified. This algorithm is based on a new decomposition of the TOA signal, where the residue of sun-glint, aerosol scattering and coupling terms is modeled through a polynomial function in -4, and -1, and -0. In the GlobCoast project (Loisel, 2013) this algorithm has been adapted to coastal waters, where scattering by mineral particles hasto be taken into account. This atmospheric correction algorithm has then been adapted for the Mekong delta area in this study. An example of the spatial coverage improvement using the new POLYMER algorithm, compared to the standard product delivered by NASA (and ESA), is provided in Figure 1.

Figure 1: Eight days composite of Rrs(665) (in sr-1) obtained by the NASA/SeaDASS (left panel) and POLYMER-new (right panel) algorithms for Vietnam coastal waters from MERIS observations.

Suspended particulate matter concentration, SPM, (in g.m-3) is estimated from the remote sensing reflectance in the red part of the spectrum (Rrs(655) for MERIS, and Rrs(671) for VIIRS) using the new algorithm of Han et al. (2016). Compared to other previous algorithm (Doxaran et al., 2003; Gohin et al., 2005; Nechad et al., 2010; Siswanto et al., 2011), this algorithm allows SPM to be retrieved with a good accuracy over 4 orders of magnitude.

The new POLYMER atmospheric correction algorithm, coupled with the bio-optical algorithm of Han et al. (2016), have also been applied to process the VIIRS images. As seen in figure 2, the new processing provides slightly better spatial resolution, and much less noisy images.

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Figure 2: Monthly SPM images from VIIRS observation in June 2014 using the proposed new processing (left) compared with the standard NASA processing (right).

4. Validation of SPM products

The Han et al. (2016) algorithm has already been validated against in situ SPM measurements, using in situ Rrs measurements as input parameters of the model. Based on this previous exercise it has been shown that in situ and inversed SPM values agree by about 30%. It is now important to validate the whole processing chain, from the TOA signal acquired by the spatial sensor to the final SPM product. This is a well-known match-up analysis, performed from in situ measurements acquired simultaneously to the satellite overpass over the same location (more or less 1 hour).

For the MERIS data (2002-2012), no in situ measurements was made available over the study area. The match-up analysis has therefore been performed for different coastal locations where in situ SPM data are available (Figure 3). Based on this exercise the root mean square error (rmse) is 0.312 g.m-3.

Figure 3: Comparison between SPM measured in situ, and estimated using the POLYMER-new and Han et al. (2016) algorithms for the MERIS sensors at different coastal waters

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Field measurements have been performed in the Mekong delta area from the 19 to the 27 of June 2014 (see WP1). For that purpose, VIIRS observations have been processed with the new POLYMER and Han et al. (2016) algorithms, and the retrieved SPM values have been compared with in situ available SPM values. A very clear agreement appears between inversed and in situ measured SPM data (Figure 4).

SPM in situ [3; 5] g.m-3


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Figure 4: Daily VIIRS images of SPM. The black diamonds represent the in-situ stations, for which the SPM values measured at these stations are provided on the right side

5. SPM temporal patterns

Responding to the modelers’ requests, VIIRS satellite data were also processed for January, June, and October 2014 which correspond to some specific states of the river outflows (Figure 5). In October, the Mekong river outflows are maximum which can be clearly seen at the outlets of the different river branches. In January, the river outflow has drastically decreased, while the turbidity remains relatively high, due to resuspension of bottom sediments performed by waves action during winter monsoon. At last, in June, when the river outflow and the significant wave heights are still very low at the beginning of the summer monsoon, the concentration of SPM at the sea surface has drastically decreased.

The SPM variation coefficient calculated over the entire MERIS archive using the new algorithm exhibits a very specific spatial pattern (Figure 6). The main temporal variability, with a variation coefficient higher than 60%, is observed offshore and decreases towards the coast. The main reason of the relatively lower temporal variability in coastal areas compared to offshore waters is the presence of the river plume and shallow waters promoting a nearly permanent sediment


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resuspension process under tide and wave forcing. The impact of strong seasonality in the river water discharge is however clearly noticeable in front of the different rivers outlets and is characterized by a strong variation coefficient of about 200%. In contrast to the East sea, a thin turbidity belt with high coefficient of variation (60%) is observed along the coastline the West sea of the study area (Gulf of Thailand).

Figure 5: Monthly SPM maps from VIIRS observation for the months of January (upper left), June (upper right), and October (bottom left) 2014

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Figure 6: Variation coefficient (standard deviation divided by mean) of the suspended particulate matter concentration over the entire study period from MERIS observations, and calculated using the

new proposed approach

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REFERENCES

Antoine D, Morel A. A Multiple Scattering Algorithm for Atmospheric Correction of Remotely-Sensed Ocean Colour (MERIS Instrument): Principle and Implementation for Atmospheres Carrying Various Aerosols Including Absorbing Ones. International Journal of Remote Sensing 1999; 20(9) 1875-1916.

Doxaran, D.; Froidefond, J.M.; Castaing, P. Remote-sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects by use of reflectance ratios. Appl. Opt.2003,42, 2623–2634.

Gohin, F.; Loyer, S.; Lunven, M. Satellite-derived parameters for biological modelling in coastal waters: Illustration over the eastern continental shelf of the Bay of Biscay. Remote Sens. Environ.2005, 95, 29–46.

Gordon HR, and Wang M. Retrieval of Water-Leaving Radiance and Aerosol Optical Thickness Over the Oceans with SeaWiFS: A Preliminary Algorithm. Applied Optics 1994; 33 443-452.

Han Bing, Hubert Loisel, Vincent Vantrepotte , Xavier Mériaux, Philippe Bryère , David Dessailly , Qianguo Xing and Jianhua Zhu. Development and validation of a semi-analytical algorithm for the retrieval of Suspended Particulate Matter from remote sensing over clear to very turbid waters. Remote Sensing. 2016, 8, 211.

Loisel H., 2013. GLobCoast : Remote sensing of Coastal water biogeochemical characteristics. International innovation, Research Media Ltd Ed., UK, August 2013, 38-41.

Nechad, B.; Ruddick, K.; Park, Y. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. RemoteSens. Environ.2010, 114, 854–866.

Siswanto,E.;Tang,J.;Yamaguchi,H.;Yuhwan,A.;Ishizaka,J.;Yoo,S.;Sangwoo,K.;Kiyomoto,Y.;Yamada,K.; Chiang,C.; Kawamura,H. Empirical ocean color algorithms to retrieve chlorophyll-a, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. J. Oceanogr. 2011,67, 627–650.

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