Evaluation of in-situ radiometric data processing for calibration and validation of satellite ocean color remote sensing

2014 ◽  
Author(s):  
Puneeta Naik ◽  
Menghua Wang
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Ocean Color ◽  
Ocean Color Remote Sensing ◽  
Calibration And Validation ◽  
Satellite Ocean Color ◽  
Radiometric Data

scholarly journals Global land mask for satellite ocean color remote sensing

2021 ◽  
Vol 257 ◽  
pp. 112356
Author(s):  
Karlis Mikelsons ◽  
Menghua Wang ◽  
Xiao-Long Wang ◽  
Lide Jiang
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Ocean Color Remote Sensing ◽  
Global Land ◽  
Satellite Ocean Color

scholarly journals Design and Experiments of a Water Color Remote Sensing-Oriented Unmanned Surface Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2183
Author(s):  
Yong Li ◽  
Liqiao Tian ◽  
Wenkai Li ◽  
Jian Li ◽  
Anna Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Obstacle Avoidance ◽  
Ocean Color ◽  
Water Color ◽  
Unmanned Surface Vehicle ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
In Situ Observations

Integrated and intelligent in situ observations are important for the remote sensing monitoring of dynamic water environments. To meet the field investigation requirements of ocean color remote sensing, we developed a water color remote sensing-oriented unmanned surface vehicle (WC-USV), which consisted of an unmanned surface vehicle platform with ground control station, data acquisition, and transmission modules. The WC-USV was designed with functions, such as remote controlling, status monitoring, automatic obstacle avoidance, and water and meteorological parameter measurement acquisition, transmission, and processing. The key data acquisition module consisted of four parts: A floating optical buoy (FOBY) for collecting remote sensing reflectance ( R r s ) via the skylight-blocked approach; a water sample autocollection system that can collect 12 1-L bottles for analysis in the laboratory; a water quality measurement system for obtaining water parameters, including Chlorophyll-a (Chl-a), turbidity, and water temperature, among others; and meteorological sensors for measuring wind speed and direction, air pressure, temperature, and humidity. Field experiments were conducted to validate the performance of the WC-USV on 23–28 March 2018 in the Honghu Lake, which is the seventh largest freshwater lake in China. The tests proved the following: (1) The WC-USV performed well in terms of autonomous navigation and obstacle avoidance; (2) the mounted FOBY-derived R r s showed good precision in terms of the quality assurance score (QAS), which was higher than 0.98; (3) the Chl-a and suspended matters (SPM) as ocean color parameters measured by the WC-USV were highly consistent with laboratory analysis results, with determination coefficients (R2) of 0.71 and 0.77, respectively; and (4) meteorological parameters could be continuously and stably measured by WC-USV. Results demonstrated the feasibility and practicability of the WC-USV for automatic in situ observations. The USV provided a new way of thinking for the future development of intelligent automation of the aquatic remote sensing ground verification system. It could be a good option to conduct field investigations for ocean color remote sensing and provide an alternative for highly polluted and/or shallow high-risk waters which large vessels have difficulty reaching.

Regional empirical algorithm for an improved retrieval of chlorophyll a concentrations in the Black Sea using Sentinel 3 ocean color data

2021 ◽  
Author(s):  
Violeta Slabakova ◽  
Snejana Moncheva ◽  
Nataliya Slabakova ◽  
Nina Dzembekova
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Black Sea ◽  
Ocean Color ◽  
The Black Sea ◽  
Ocean Color Remote Sensing ◽  
Color Data ◽  
Cubic Polynomial ◽  
Polynomial Formulation

<p>The Black Sea is an extraordinarily complex water body for ocean color remote sensing, as it belong to Case 2 waters, which are characterized by relatively high absorption by Colored Dissolved Organic Matter (CDOM) while the concentration of non-pigmented particulate matter does not co-vary in a predictable manner with chlorophyll <em>a</em> . The optical complexity of the Black Sea is the reason why the standard bio-optical algorithms developed for Case 1 waters, are the source of large uncertainties (of the order of hundreds of percent) of chlorophyll <em>a</em> concentration in the coastal and shelf regions. In the framework of ESA contract “BIO-OPTICS FOR OCEAN COLOR REMOTE SENSING OF THE BLACK SEA - Black Sea Color” we developed empirical ocean color algorithm for chlorophyll<em> a </em>retrieval from Sentinel 3A/OLCI primary ocean color products using the <em>in situ </em>reference bio-optical datasets collected in the Black Sea in the period 2012-2019. Results obtained from the assessment of operational S3A/OLCI chlorophyll products, highlighted and confirmed that the specific regional algorithm is essential for the Black Sea. The coefficients of the regional algorithm were derived from the regression of log-transformed pigment concentrations and remote sensing reflectance ratio at 490nm and 560 nm with determination coefficient R<sup>2</sup> =0.88 and number of samples N=186. The algorithm predicts chlorophyll a values using a cubic polynomial formulation. The result of assessment of the regional chlorophyll <em>a</em> product against independent in situ measurements from the data utilized for algorithm development, showed relatively high accuracy (31.7%), fewer underestimations (MPD=-9.2%) and a good agreement (R<sup>2</sup>=0.66) between datasets indicating that the regional algorithm is more effective in reproducing the  pigment concentration in the Black Sea waters in comparison to the standard Sentinel 3A/OLCI algorithms. Our analysis revealed the importance of providing regional algorithms strictly required to suit the peculiar bio-optical properties featuring this basin. However, this requires collection of accurate<em> in situ </em>measurements in the different parts of the Black Sea. The validity of the reported empirical algorithm obviously depends on the size of the dataset used for its development. The Black Sea waters vary at a basin level due to the sub-regional features, environmental factors and seasonal variability, consequently the presented regional algorithm might have a limited generalization capability. Clearly, more<em> in situ</em> data with improved spatial and temporal coverage are critically needed for further calibration and validation of the ocean color products in the Black Sea.</p>

scholarly journals Extending satellite ocean color remote sensing to the near-blue ultraviolet bands

2021 ◽  
Vol 253 ◽  
pp. 112228
Author(s):  
Yongchao Wang ◽  
Zhongping Lee ◽  
Jianwei Wei ◽  
Shaoling Shang ◽  
Menghua Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Ocean Color Remote Sensing ◽  
Satellite Ocean Color

scholarly journals Remote Sensing of Phytoplankton Pigments

2021 ◽  
Author(s):  
Guoqing Wang ◽  
John Moisan
Keyword(s):  
Remote Sensing ◽  
High Performance Liquid Chromatography ◽  
High Performance ◽  
Ocean Color ◽  
Phytoplankton Pigments ◽  
Ocean Color Remote Sensing ◽  
Phytoplankton Pigment ◽  
Challenges And Opportunities ◽  
History Of ◽  
Satellite Ocean Color

Pigments, as a vital part of phytoplankton, act as the light harvesters and protectors in the process of photosynthesis. Historically, most of the previous studies have been focused on chlorophyll a, the primary light harvesting pigment. With the advances in technologies, especially High-Performance Liquid Chromatography (HPLC) and satellite ocean color remote sensing, recent studies promote the importance of the phytoplankton accessory pigments. In this chapter, we will overview the technology advances in phytoplankton pigment identification, the history of ocean color remote sensing and its application in retrieving phytoplankton pigments, and the existing challenges and opportunities for future studies in this field.

scholarly journals Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach

2010 ◽  
Vol 18 (20) ◽  
pp. 20949 ◽  
Author(s):  
Hubert Loisel ◽  
Bertrand Lubac ◽  
David Dessailly ◽  
Lucile Duforet-Gaurier ◽  
Vincent Vantrepotte
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Ocean Color ◽  
Ocean Color Remote Sensing ◽  
Inherent Optical Properties
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