Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data

Doxaran, David; Lamquin, Nicolas; Park, Young-Je; Mazeran, Constant; Ryu, Joo-Hyung; Wang, Menghua; Poteau, Antoine

doi:10.1016/j.rse.2013.06.020

Cited by 79 publications

(40 citation statements)

References 44 publications

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“…The dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a high estimation accuracy (R 2 = 0.81, RMSE = 0.99 m −1 , RMSErel = 25.36%, MNB = 2.81% and NRMS = 25.73%). This is because the NIR, red, and green bands are the key bands for deriving the water quality parameters of inland waters from remote sensing data [25], particularly for deriving Chla and SPM concentrations [55][56][57]. The single band ratio (681 nm/560 nm) algorithm also had a relatively high estimation accuracy (R 2 = 0.66, RMSE = 1.32 m −1 , RMSErel = 31.58%, MNB = 6.41% and NRMS = 31.56%).…”

Section: Comparison With Existing Algorithms Using the Olci-derived R Rsmentioning

confidence: 99%

Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI

et al. 2017

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Abstract:The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400-1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (K d (490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available K d (490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R 2 ) = 0.81, root-mean-square error (RMSE) = 0.99 m −1 and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R 2 = 0.83, RMSE = 1.06 m −1 and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI K d (490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of K d (490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The K d (490) values for Lake Taihu exhibit notable spatial and temporal variations. K d (490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind-and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties.

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Section: Comparison With Existing Algorithms Using the Olci-derived R Rsmentioning

confidence: 99%

Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI

et al. 2017

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“…The original algorithm GDPS first implemented in GDPS v1.0.0 was based on the algorithm established for SeaWiFS data by Gordan and Wang [26], which did not perform well in turbid water. Wang [27] put forward an improved atmospheric correction using a near-infrared algorithm for GOCI in the turbid western Pacific region, which performed well [28]. To improve the performance of the atmospheric correction algorithm in turbid waters, the MUMM algorithm developed by Ruddick [29] was implemented in the latest version of GDPS (v.1.3.0).…”

Section: Goci Imagesmentioning

confidence: 99%

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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Abstract:Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE.

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“…The new capabilities of recent ocean color satellite sensors represent an efficient way to complement scarce field measurements and to monitor the surface transport of SPM through river mouths, in river plumes and estuaries (e.g., [4][5][6][7][8][9][10][11][12][13]). These sensors offer a good compromise between revisit time (about a daily revisit at mid-latitudes, depending on cloud cover) and spatial resolution (typically ranging from 250 to 1000 m).…”

Section: Introductionmentioning

confidence: 99%

Potential of High Spatial and Temporal Ocean Color Satellite Data to Study the Dynamics of Suspended Particles in a Micro-Tidal River Plume

et al. 2016

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Ocean color satellite sensors are powerful tools to study and monitor the dynamics of suspended particulate matter (SPM) discharged by rivers in coastal waters. In this study, we test the capabilities of Landsat-8/Operational Land Imager (OLI), AQUA&TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS) and MSG-3/Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensors in terms of spectral, spatial and temporal resolutions to (i) estimate the seawater reflectance signal and then SPM concentrations and (ii) monitor the dynamics of SPM in the Rhône River plume characterized by moderately turbid surface waters in a micro-tidal sea. Consistent remote-sensing reflectance (Rrs) values are retrieved in the red spectral bands of these four satellite sensors (median relative difference less than~16% in turbid waters). By applying a regional algorithm developed from in situ data, these Rrs are used to estimate SPM concentrations in the Rhône river plume. The spatial resolution of OLI provides a detailed mapping of the SPM concentration from the downstream part of the river itself to the plume offshore limits with well defined small-scale turbidity features. Despite the low temporal resolution of OLI, this should allow to better understand the transport of terrestrial particles from rivers to the coastal ocean. These details are partly lost using MODIS coarser resolutions data but SPM concentration estimations are consistent, with an accuracy of about 1 to 3 g¨m´3 in the river mouth and plume for spatial resolutions from 250 m to 1 km. The MODIS temporal resolution (2 images per day) allows to capture the daily to monthly dynamics of the river plume. However, despite its micro-tidal environment, the Rhône River plume shows significant short-term (hourly) variations, mainly controlled by wind and regional circulation, that MODIS temporal resolution failed to capture. On the contrary, the high temporal resolution of SEVIRI makes it a powerful tool to study this hourly river plume dynamics. However, its coarse resolution prevents the monitoring of SPM concentration variations in the river mouth where SPM concentration variability can reach 20 g¨m´3 inside the SEVIRI pixel. Its spatial resolution is nevertheless sufficient to reproduce the plume shape and retrieve SPM concentrations in a valid range, taking into account an underestimation of about 15%-20% based on comparisons with other sensors and in situ data. Finally, the capabilities, advantages and limits of these satellite sensors are discussed in the light of the spatial and temporal resolution improvements provided by the new and future generation

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Retrieval of the seawater reflectance for suspended solids monitoring in the East China Sea using MODIS, MERIS and GOCI satellite data

Cited by 79 publications

References 44 publications

Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI

Determination of the Downwelling Diffuse Attenuation Coefficient of Lake Water with the Sentinel-3A OLCI

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

Potential of High Spatial and Temporal Ocean Color Satellite Data to Study the Dynamics of Suspended Particles in a Micro-Tidal River Plume

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