scholarly journals Local, Daily, and Total Bio-Optical Models of Coastal Waters of Manfredonia Gulf Applied to Simulated Data of CHRIS, Landsat TM, MIVIS, MODIS, and PRISMA Sensors for Evaluating the Error

2020 ◽  
Vol 12 (9) ◽  
pp. 1428 ◽  
Author(s):  
Rosa Maria Cavalli
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Organic Material ◽  
Simulated Data ◽  
Local Models ◽  
Imaging Spectrometer ◽  
Mode 2 ◽  
Resolution Imaging ◽  
Remote Data

The spatial–temporal resolution of remote data covers coastal water variability, but this approach offers a lower accuracy than in situ observations. Two of the major error sources occur due to the parameterization of bio-optical models and spectral capability of the remote data. These errors were evaluated by exploiting data acquired in the coastal waters of Manfredonia Gulf. Chlorophyll-a concentrations, absorption of the colored dissolved organic material at 440 nm (aCDOM440nm), and tripton concentrations measured in situ varied between 0.09–1.76 mgm−3, 0.00–0.41 m−1, and 1.97–8.90 gm−3. In accordance with the position and time of in situ surveys, 36 local models, four daily models, and one total bio-optical model were parameterized and validated using in situ data before applying to Compact High-Resolution Imaging Spectrometer (CHRIS) mode 1, CHRIS mode 2, Landsat Thematic Mapper (TM), Multispectral Infrared and Visible Imaging Spectrometer (MIVIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Precursore Iperspettrale della Missione Applicativa (PRISMA) simulated data. Concentrations retrieved from PRISMA data using local models highlighted the smallest errors. Because tripton abundance is great and tripton absorptions were better resolved than those of chlorophyll-a and colored dissolved organic material (CDOM), tripton concentrations were adequately retrieved from all data using total models, while only local models adequately retrieved chlorophyll-a concentrations and aCDOM440nm from CHRIS mode 1, CHRIS mode 2, MIVIS, and MODIS data. Therefore, the application of local models shows smaller errors than those of daily and total models; however, the capability to resolve the absorption of water constituents and analyze their concentration range can dictate the model choice. Consequently, the integration of more models allows us to overcome the limitations of the data and sensors.

Studying Chlorophyll-A Concentration of Bosten Lake (Xinjiang, Northwest China) by MERIS Data

2013 ◽  
Vol 864-867 ◽  
pp. 2750-2755
Author(s):  
Ying Liu ◽  
An Ming Bao ◽  
Xi Chen
Keyword(s):  
Spatial Distribution ◽  
Chlorophyll A ◽  
Field Data ◽  
Northwest China ◽  
Imaging Spectrometer ◽  
Boundary Area ◽  
Bosten Lake ◽  
Medium Resolution ◽  
Resolution Imaging ◽  
Chla Concentration

The Chlorophyll-a (Chla) concentration in Bosten Lake was estimated and mapped using the data of the Medium Resolution Imaging Spectrometer (MERIS) on board the ENVIronmental SATellite (ENVISAT) platform. The fixed aerosol option was chosen and local aerosol optical thickness was used in SeaDAS. The Chla concentration was retrieved by the OC3E algorithm and verified by Field data with high correlation coefficient of 0.79. It showed strong horizontal heterogeneities, which is high at the Huangshuigou region, mediate along the boundary area, and low at the middle of the lake, and decreases from the boundary to the center of the Lake. Its spatial distribution is controlled by the location of inlet and outlet and the type and quantity of discharging around the lake. On sep. 22, 2010, its value is up to 10.98 mg m-3. The minimum, maximum, average and median value of Chla concentration on Aug. 6, 2011 from MERIS data in Bosten Lake is 2.72, 8.93, 3.90 and 3.69 mg m-3.

scholarly journals Bio-Optical Characteristics of the Black Sea Coastal Waters near Sevastopol: Assessment of the MODIS and VIIRS Products Accuracy

2021 ◽  
Vol 37 (2) ◽  
Author(s):  
E. Yu. Skorokhod ◽  
T. Ya. Churilova ◽  
T. V. Efimova ◽  
N. A. Moiseeva ◽  
V. V. Suslin ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Black Sea ◽  
Coastal Waters ◽  
Light Absorption ◽  
Ecological Monitoring ◽  
Optically Active ◽  
The Black Sea ◽  
Active Components ◽  
Chlorophyll A Concentration

Purpose. The purpose of the work is to evaluate accuracy of the satellite products for the coastal waters near Sevastopol, reconstructed by the standard algorithms based on the MODIS and VIIRS (installed at the artificial Earth satellites Aqua and Terra, and at Suomi NPP, respectively) data. Methods and Results. In situ sampling was carried out at the station (44°37'26" N and 33°26'05" E) located at a distance of two miles from the Sevastopol Bay. The chlorophyll a concentration was measured by the spectrophotometric method. The spectral light absorption coefficients by optically active components were measured in accordance with the current NASA protocol. The spectroradiometers MODIS and VIIRS Level 2 data with spatial resolution 1 km in nadir around the in situ station (44°37'26"±0°00'32" N and 33°26'05"±0°00'54" E) were used. The satellite products were processed by the SeaDAS 7.5.3 software developed in NASA. The research showed that the standard NASA algorithms being applied to the MODIS and VIIRS data, yielded incorrect values of the optically active components’ content in the Black Sea coastal waters near Sevastopol as compared to the data of in situ measurements in the same region: the satellite-derived “chlorophyll a concentration” was on average 1.6 times lower in spring, and 1.4 times higher in summer; the contribution of phytoplankton pigments to total light absorption at 443 nm was underestimated in 8.7 times; the light absorption by colored detrital organic matter was overestimated in 2.2 times. Conclusions. The NASA standard algorithms are inapplicable to calculating bio-optical indices in the coastal waters of the Black Sea near Sevastopol since they provide incorrect values of the satellite products (Ca-s, aph-s(443) and aCDM-s(443)). Operative ecological monitoring based on satellite data requires development of a regional algorithm taking into account the seawater optical features in the region and in the coastal zone, in particular.

Chlorophyll-a retrieval of coastal waters based on in situ hyperspectral data

2009 ◽  
Author(s):  
Wandong Ma ◽  
Ping Shi ◽  
Yuanzhi Zhang ◽  
Qianguo Xing ◽  
Jiakui Tang ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Hyperspectral Data

scholarly journals Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3004
Author(s):  
Antonia Ivanda ◽  
Ljiljana Šerić ◽  
Marin Bugarić ◽  
Maja Braović
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Coastal Waters ◽  
Optical Parameter ◽  
Data Set ◽  
Chl A ◽  
Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

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