Evaluation of four MERIS atmospheric correction algorithms in Lake Kasumigaura, Japan

Jaelani, Lalu Muhamad; Matsushita, Bunkei; Yang, Wei; Fukushima, Takehiko

doi:10.1080/01431161.2013.860660

Cited by 25 publications

(19 citation statements)

References 34 publications

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“…All this useful information is removed by the C2RCC. Ten years ago, there were problems with MERIS atmospheric correction and therefore it was suggested to retrieve chlorophyll a by using the height of the 709 nm peak taken from the TOA signal instead of BOA [47]. Later atmospheric correction procedures preserved the 709 nm peak better [48], but still not completely.…”

Section: Discussionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

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Abstract:The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

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Section: Discussionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

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“…By considering the spatial and temporal heterogeneity of water bodies, extracting water information by remote sensing techniques can be more effective approach than a direct field measurement (Liu, Islam, and Gao 2003) The estimation of water quality parameters such as the concentration of TSS (Total suspended sediments) and Chl-a (Chlorophyll-a) from satellite images is strongly depend on the accuracy of atmospheric correction and water quality parameter retrievals algorithms (Ruddick, Ovidio, and Rijkeboer 2000;Sathyendranath, Prieur, and Morel 1987;Yang et al 2011;Jaelani et al 2013;Jaelani, Matsushita, et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Since the development of first algorithm needs a comprehensive study and rigorous spectral data over study area Jaelani et al 2013), this paper only focus on the second issue. Even though, there were many existing water quality parameter retrieval algorithms to estimate TSS and Chl-a concentration of water from satellite images (Sathyendranath and Platt 1989;Gons, Auer, and Effler 2008;Sathyendranath, Prieur, and Morel 1987;Nas et al 2009;Dall'Olmo et al 2005;Han and Jordan 2005;Bhatti et al 2010;Bailey and Werdell 2006), those algorithms have been developed and validated using in situ data that was collected in some specific water area.…”

Section: Introductionmentioning

confidence: 99%

Development of Water Quality Parameter Retrieval Algorithms for Estimating Total Suspended Solids and Chlorophyll-a Concentration Using Landsat-8 Imagery at Poteran Island Water

Laili

Arafah

Jaelani

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Landsat-8 satellite imagery is now highly developed compares to the former of Landsat projects. Both land and water area are possibly mapped using this satellite sensor. Considerable approaches have been made to obtain a more accurate method for extracting the information of water area from the images. It is difficult to generate an accurate water quality information from Landsat images by using some existing algorithm provided by researchers. Even though, those algorithms have been validated in some water area, but the dynamic changes and the specific characteristics of each area make it necessary to get them evaluated and validated over another water area. This paper aims to make a new algorithm by correlating the measured and estimated TSS and Chla concentration. We collected in-situ remote sensing reflectance, TSS and Chl-a concentration in 9 stations surrounding the Poteran islands as well as Landsat 8 data on the same acquisition time of April 22, 2015. The regression model for estimating TSS produced high accuracy with determination coefficient (R 2 ), NMAE and RMSE of 0.709; 9.67 % and 1.705 g/m 3 respectively. Whereas, Chla retrieval algorithm produced R 2 of 0.579; NMAE of 10.40% and RMSE of 51.946 mg/m 3 . By implementing these algorithms to Landsat 8 image, the estimated water quality parameters over Poteran island water ranged from 9.480 to 15.801 g/m 3 and 238.546 to 346.627 mg/m 3 for TSS and Chl-a respectively.

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“…Since water-specific models consider water spectra at the lower boundary, significant errors may be introduced in the atmospheric correction over the highly dynamic and often complex near-shore and inland waters (Vidot & Santer, 2003;Goyens et al, 2013). On the other hand, land-surface models do not embrace any assumptions on the water spectra but they experience other shortcomings (e.g., assumptions on atmospheric homogeneity) and introduce large uncertainties over clear waters (Guanter et al, 2009;Jaelani et al, 2013).…”

Section: Constituent Retrievalmentioning

confidence: 99%

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

Tyler

Hunter

Spyrakos

et al. 2016

Science of The Total Environment

129

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The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production.Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelfsea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable.

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Evaluation of four MERIS atmospheric correction algorithms in Lake Kasumigaura, Japan

Cited by 25 publications

References 34 publications

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Development of Water Quality Parameter Retrieval Algorithms for Estimating Total Suspended Solids and Chlorophyll-a Concentration Using Landsat-8 Imagery at Poteran Island Water

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

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