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Validation and Evaluation of Global Ocean Satellite Products

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 16405

Special Issue Editors


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Guest Editor
Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan
Interests: satellite oceanography; ocean dynamics processes and ocean environment; ocean–typhoon interactions; upper ocean and submesoscale processes
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Guest Editor
Atmosphere and Ocean Research Institute, The University of Tokyo, Tokyo 113-8654, Japan
Interests: salinity; mixed layer; water mass
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Guest Editor
School of Geosciences, University of Louisiana at Lafayette, Lafayette, LA 70503, USA
Interests: ocean color remote sensing; carbon cycling of land–ocean interactions; phytoplankton community dynamics
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Guest Editor
Oceans Graduate School, The University of Western Australia, Crawley, WA 6009, Australia
Interests: physical oceanography; submesoscale frontal dynamics and instabilities; surface boundary layer turbulence and mixing; air–sea interactions; diurnal effects; and upwelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean science for sustainable development is an important issue in ocean research in the present decade. With the development of science and technology, ocean satellite products continue to provide higher spatial resolution and more frequent repeated observations, and geosynchronous satellites can further observe continuous sea surface changes in the same place. Satellite ocean data can effectively be used to observe sea surface variations at various spatial scales in a short period of time, help scientists make new scientific discoveries and clarify various scientific issues, and provide reference materials for scientists to plan before cruises and observations. In this Special Issue, we welcome any papers using ocean satellite products and particularly encourage research comparing in situ observation data, and breakthrough discoveries in regional oceanography.

The topics of interest include but are not limited to:

  • Data verification and analysis between in situ observation and ocean satellite products;
  • Data algorithm and verification of ocean satellite products;
  • Data comparison and correctness evaluation of various satellite products;
  • Comparison of numerical simulation and satellite data;
  • Feedback of climate variability to the ocean;
  • Breakthrough of scientific issues in regional oceanography.

Dr. Po-Chun Hsu
Dr. Chung-Ru Ho
Dr. Shota Katsura
Dr. Bingqing Liu
Dr. Jen-Ping Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sea surface current, temperature, and salinity
  • Ocean color and chlorophyll-a concentration
  • Oceanic front
  • Internal wave
  • New approaches, methods, and algorithms
  • Climatic variables
  • Ocean–typhoon interaction
  • Multiscale ocean dynamics

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Published Papers (10 papers)

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Research

18 pages, 9893 KiB  
Article
Quantitative Retrieval of Chlorophyll-a Concentrations in the Bohai–Yellow Sea Using GOCI Surface Reflectance Products
by Jiru Wang, Jiakui Tang, Wuhua Wang, Yanjiao Wang and Zhao Wang
Remote Sens. 2023, 15(22), 5285; https://doi.org/10.3390/rs15225285 - 08 Nov 2023
Viewed by 1010
Abstract
As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed [...] Read more.
As an environmental parameter, the chlorophyll-a concentration (Chl-a) is essential for monitoring water quality and managing the marine ecosystem. However, current mainstream Chl-a inversion algorithms have limited accuracy and poor spatial and temporal generalization in Case II waters. In this study, we constructed a quantitative model for retrieving the spatial and temporal distribution of Chl-a in the Bohai–Yellow Sea area using Geostationary Ocean Color Imager (GOCI) spectral remote sensing reflectance (Rrsλ) products. Firstly, the GOCI Rrsλ correction model based on measured spectral data was proposed and evaluated. Then, the feature variables of the band combinations with the highest correlation with Chl-a were selected. Subsequently, Chl-a inversion models were developed using three empirical ocean color algorithms (OC4, OC5, and YOC) and four machine learning methods: BP neural network (BPNN), random forest (RF), AdaBoost, and support vector regression (SVR). The retrieval results showed that the machine learning methods were much more accurate than the empirical algorithms and that the RF model retrieved Chl-a with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.916, a root mean square error (RMSE) of 0.212 mg·m−3, and a mean absolute percentage error (MAPE) of 14.27%. Finally, the Chl-a distribution in the Bohai–Yellow Sea using the selected RF model was derived and analyzed. Spatially, Chl-a was high in the Bohai Sea, including in Laizhou Bay, Bohai Bay, and Liaodong Bay, with a value higher than 4 mg·m−3. Chl-a in the Bohai Strait and northern Yellow Sea was relatively low, with a value of less than 3 mg·m−3. Temporally, the inversion results showed that Chl-a was considerably higher in winter and spring compared to autumn and summer. Diurnal variation retrieval effectively demonstrated GOCI’s potential as a capable tool for monitoring intraday changes in chlorophyll-a concentrations. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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16 pages, 1180 KiB  
Article
Coastal Assessment of Sentinel-6 Altimetry Data during the Tandem Phase with Jason-3
by Marcello Passaro, Florian Schlembach, Julius Oelsmann, Denise Dettmering and Florian Seitz
Remote Sens. 2023, 15(17), 4161; https://doi.org/10.3390/rs15174161 - 24 Aug 2023
Viewed by 1132
Abstract
This study presents a comparative analysis of the coastal performances of Sentinel-6 and Jason-3 altimeters during their tandem phase, considering their different processing modes. We examine the measurements available in the standard geophysical data records (GDR) and also perform dedicated reprocessing using coastal [...] Read more.
This study presents a comparative analysis of the coastal performances of Sentinel-6 and Jason-3 altimeters during their tandem phase, considering their different processing modes. We examine the measurements available in the standard geophysical data records (GDR) and also perform dedicated reprocessing using coastal retracking algorithms applied to the original waveforms. The performances are evaluated, taking into account the quality of retrievals (outlier analysis), their precision (along-track noise analysis), potential systematic biases, and accuracy (comparison against tide gauges). The official SAR altimetry product of Sentinel-6 demonstrates improved coastal monitoring capabilities compared to Jason-3, except for the remaining issues related to significant wave height, which have already been identified. These findings highlight the significance of dedicated coastal retracking algorithms for enhancing the capabilities of both traditional, pulse-limited altimeters and more recent developments utilizing SAR altimetry. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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17 pages, 13978 KiB  
Article
Validation and Evaluation of GRACE-FO Estimates with In Situ Bottom Pressure Array Measurements in the South China Sea
by Xuecheng Wang, Hua Zheng, Xiao-Hua Zhu, Ruixiang Zhao, Min Wang, Juntian Chen, Yunlong Ma, Feng Nan and Fei Yu
Remote Sens. 2023, 15(11), 2804; https://doi.org/10.3390/rs15112804 - 28 May 2023
Cited by 2 | Viewed by 1334
Abstract
The Gravity Recovery and Climate Experiment (GRACE), and its follow-on mission (GRACE-FO), provides a novel measurement of the variations in ocean bottom pressure (OBP) at global and basin scales, including those in marginal seas. However, these measurements have not yet been validated rigorously [...] Read more.
The Gravity Recovery and Climate Experiment (GRACE), and its follow-on mission (GRACE-FO), provides a novel measurement of the variations in ocean bottom pressure (OBP) at global and basin scales, including those in marginal seas. However, these measurements have not yet been validated rigorously for the South China Sea (SCS). In this study, the accuracy in the monthly GRACE-FO mascon solutions in the SCS from the Jet Propulsion Laboratory (JPL), Center for Space Research (CSR), and Goddard Space Flight Center (GSFC) was validated with the results of the comparison with the in situ OBP records from an array of 25 pressure-recording inverted echo sounders (PIESs) that are located west of the Luzon Strait (LS). The correlation coefficient (Cor) and root mean square difference (RMSD) between the 10-month period of GSFC and PIES, spanning from July 2018 to June 2019 (with missing satellite data for August and September 2018), were 0.77 (p-value = 0.005) and 0.41 mbar (1 mbar = 100 Pa), respectively. These values suggest that the accuracy of GSFC in the SCS in this period was substantially better than that of JPL (Cor = 0.35, p-value = 0.16; RMSD = 0.74 mbar) and CSR (Cor = 0.25, p-value = 0.24; RMSD = 0.89 mbar). Moreover, the volume transport anomaly of the SCS abyssal circulation was estimated and compared based on the OBP records from GSFC and PIES observations, indicating that the GRACE-FO OBP (GSFC) can be used to monitor seasonal or longer-period variations in the SCS abyssal volume transport. Additionally, the variations in OBP from GRACE-FO were significantly overestimated on the continental shelf of the SCS, which may be attributed to signal leakage. Our findings provide reliable evidence for the application of long-term, fully covered OBP records from GRACE-FO in the SCS, and also offer a valuable reference for the application of GRACE-FO in other regions. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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20 pages, 12143 KiB  
Article
On Modelling Sea State Bias of Jason-2 Altimeter Data Based on Significant Wave Heights and Wind Speeds
by Jinyun Guo, Huiying Zhang, Zhen Li, Chengcheng Zhu and Xin Liu
Remote Sens. 2023, 15(10), 2666; https://doi.org/10.3390/rs15102666 - 20 May 2023
Cited by 2 | Viewed by 1042
Abstract
Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for [...] Read more.
Altimeter data processing is very important to improve the quality of sea surface height (SSH) measurements. Sea state bias (SSB) correction is a relatively uncertain error correction due to the lack of a clear theoretical model. At present, the commonly used methods for SSB correction are polynomial models (parametric models) and non-parametric models. The non-parametric model usually was constructed by collinear data. However, the amount of collinear data was enormous, and it contained redundant information. In this study, the non-parametric regression estimation model was optimized by using the parameter replacement method of ascending and descending tracks based on the crossover data. In this method, significant wave heights from the Jason-2 altimeter data during cycles 200–301 and wind speed from the ERA5 reanalysis data were used. The non-parametric regression estimation model of Jason-2 was constructed by combining it with local linear regression, Epanechnikov kernel function and local window width. At the same time, based on the significant wave height and wind speed at the crossover points, the SSB polynomial model containing six parameters was constructed by using the Taylor series expansion, and the model was optimized. By comparing polynomial model construction with different parameters, the optimized model was obtained. The SSH of the crossover points and the tide gauge records were used to validate these results derived from two models and GDR. Compared with the crossover discrepancies of SSH corrected by the polynomial model, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 7.9%. Compared with the crossover discrepancies of SSH corrected by the conventional non-parametric model from GDR, the RMS of the crossover discrepancies of SSH corrected by the non-parametric regression estimation model was reduced by 4.1%. This shows that the precision of the SSHs derived by after the SSB correction, as calculated by the non-parametric regression estimation model, was better than that of the polynomial model and the SSB correction from GDR. Using the Jason-2 altimeter data, the along-track geoid gradient and the sea level change rate of the global ocean were determined by using two models to correct the SSB. By comparing the results of the two models, the accuracy of the geoid gradient along the orbit that was obtained by the non-parametric regression estimation model was better than that of the polynomial model and GDR. The global average sea level change rate after the non-parametric regression estimation model correction was 3.47 ± 0.09 mm/y, which was the closest to the average sea level change rate that has been published in the international literature within this field. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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16 pages, 2202 KiB  
Article
Merged Multi-Sensor Ocean Colour Chlorophyll Product Evaluation for the British Columbia Coast
by Sejal Pramlall, Jennifer M. Jackson, Marta Konik and Maycira Costa
Remote Sens. 2023, 15(3), 687; https://doi.org/10.3390/rs15030687 - 24 Jan 2023
Cited by 3 | Viewed by 2204
Abstract
Phytoplankton phenology studies require a dataset that is continuous in time and space since missing data have been shown to affect the accuracy of seasonality metrics. The interpolated GlobColour product provided by the Copernicus Marine Environment Monitoring Service (CMEMS) meets these requirements by [...] Read more.
Phytoplankton phenology studies require a dataset that is continuous in time and space since missing data have been shown to affect the accuracy of seasonality metrics. The interpolated GlobColour product provided by the Copernicus Marine Environment Monitoring Service (CMEMS) meets these requirements by being ‘gap filled’, thus yielding the highest spatial coverage. Despite being validated on a global scale, a regional comparison to in situ Chl-a concentrations should be conducted to enable product application in optically complex waters. This study aims to evaluate the performance of the GlobColour interpolated product in British Columbia coastal waters via a statistical match-up analysis and a qualitative analysis to determine whether the data reflect the region’s large-scale seasonal trends and latitudinal dynamics. Additionally, the statistical performance of the GlobColour interpolated product was compared to the original GlobColour and Ocean Colour Climate Change Initiative (OC-CCI) merged chlorophyll-a products based on in situ observations. The GlobColour interpolated product performed relatively well and was comparable to the best-performing product for each water type (RMSE = 0.28, r2 = 0.77, MdAD = 1.5, BIAS = 0.90). The statistics for all the products degraded in Case 2 waters, thus highlighting the dilemma of applying algorithms designed for Case 1 waters in Case 2 waters. Our results indicate how the quality of products can vary in different environmental conditions. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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20 pages, 10846 KiB  
Article
The Atmospheric Correction of COCTS on the HY-1C and HY-1D Satellites
by Zhihua Mao, Yiwei Zhang, Bangyi Tao, Jianyu Chen, Zengzhou Hao, Qiankun Zhu and Haiqing Huang
Remote Sens. 2022, 14(24), 6372; https://doi.org/10.3390/rs14246372 - 16 Dec 2022
Cited by 1 | Viewed by 1149
Abstract
The data quality of the remote sensing reflectance (Rrs) from the two ocean color satellites HaiYang-1C (HY-1C) and HaiYang-1D (HY-1D) and the consistency with other satellites are critical for the products. The Layer Removal Scheme for Atmospheric Correction (LRSAC) has [...] Read more.
The data quality of the remote sensing reflectance (Rrs) from the two ocean color satellites HaiYang-1C (HY-1C) and HaiYang-1D (HY-1D) and the consistency with other satellites are critical for the products. The Layer Removal Scheme for Atmospheric Correction (LRSAC) has been applied to process the data of the Chinese Ocean Color and Temperature Scanner (COCTS) on HY-1C/1D. The accuracy of the Rrs products was evaluated by the in situ dataset from the Marine Optical BuoY (MOBY) with a mean relative error (MRE) of −1.56% and a mean absolute relative error (MAE) of 17.31% for HY-1C. The MRE and MAE of HY-1D are 1.05% and 15.68%, respectively. The comparisons of the global daily Rrs imagery with the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra show an MRE of 10.94% and an MAE of 21.38%. The comparisons between HY-1D and Aqua exhibit similar results, with an MRE of 13.31% and an MAE of 21.46%. The percentages of valid pixels of the global daily images of HY-1C and HY-1D are 32.3% and 32.6%, much higher than that of Terra (11.9%) and Aqua (11.9%). The gaps in the 8-day composite images have been significantly reduced, with 83.9% of valid pixels for HY-1C and 85.4% for HY-1D, which are also much higher than that of Terra (52.9%) and Aqua (50.9%). The gaps due to the contamination of sun glint have been almost removed from the 3-day composite imagery, with valid pixels of 63.5% for HY-1C and 65.6% for HY-1D, which are higher than that of the 8-day imagery of Terra and Aqua. The patterns of HY-1C imagery exhibit a similarity with those of HY-1D, but they are different on a pixel scale, mainly due to the changes in the ocean dynamic features within 3 h. The evaluations of the COCTS indicate that the imagery of HY-1C/1D can be used as a kind of standard product. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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17 pages, 6640 KiB  
Article
Satellite-Observed Time and Length Scales of Global Sea Surface Salinity Variability: A Comparison of Three Satellite Missions
by Daling Li Yi, Oleg Melnichenko, Peter Hacker and Ke Fan
Remote Sens. 2022, 14(21), 5435; https://doi.org/10.3390/rs14215435 - 28 Oct 2022
Viewed by 1439
Abstract
Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns [...] Read more.
Sea surface salinity (SSS) observations from Aquarius, Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) satellite missions are compared to characterize the time and length scales of SSS variability globally. Overall, there is general agreement between the global patterns of the time and length scales of SSS variability estimated from the three satellite missions. The temporal scales of SSS variability vary from more than 90 days in the tropics to ~15 days in the Southern Ocean. The very short temporal scales (close to the Nyquist period) in some parts of the ocean are probably due to the high level of noise in the satellite data or the high noise-to-signal ratio. The longest temporal scales are observed along the South Pacific Convergence Zone (SPCZ) and in the central and western tropical Pacific. These areas are also related to the strongest ENSO-related signal in SSS. The processes governing the SSS variability and distribution are also non-stationary, such that the scales determined over different observation periods may differ. Dominant spatial scales of SSS variability are generally the longest (up to 150 km) in the tropics and the shortest (<60 km) in the subpolar regions. The distribution of the dominant spatial scales is not simply latitudinal but exhibits a more complex spatial pattern. In the tropics, there is slight east-west and inter-hemispheric asymmetry observed in the Pacific but absent in the other two oceans. The analysis also reveals that the length scales of SSS variability are highly anisotropic in the tropics (the zonal scales are generally shorter than the meridional ones) and become more isotropic towards higher latitudes. Regional differences in the estimates of the scales from the three satellite SSS datasets may arise due to differences in the observation duration, spatial resolution and/or different level of noise. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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18 pages, 7323 KiB  
Article
Evaluation of Wind and Solar Insolation Influence on Ocean Near-Surface Temperature from In Situ Observations and the Geostationary Himawari-8 Satellite
by Po-Chun Hsu
Remote Sens. 2022, 14(19), 4975; https://doi.org/10.3390/rs14194975 - 06 Oct 2022
Cited by 2 | Viewed by 1689
Abstract
The skin sea surface temperature (SST) observed by the geostationary Himawari-8 satellite and bulk SST, including four in situ observations from ships, drifters, Argo, and buoys constitute more than 90,000 SST pairs used to analyze near-surface temperature variations. From July 2015 to May [...] Read more.
The skin sea surface temperature (SST) observed by the geostationary Himawari-8 satellite and bulk SST, including four in situ observations from ships, drifters, Argo, and buoys constitute more than 90,000 SST pairs used to analyze near-surface temperature variations. From July 2015 to May 2022, an average SST bias of 0.10 °C and root mean square error of 0.99 °C were observed in the waters adjacent to Taiwan. This study effectively observed that the skin effect generated by ocean wind and solar shortwave radiation caused the occurrence of a cool skin layer and diurnal warm layer (DWL), and 90% of the SST bias was in a range of −1.55~1.71 °C. In the daytime, the skin layer received solar shortwave radiation, thus increasing temperature and causing a DWL. With the increase in insolation, the SST bias in the DWL became more obvious. During winter, strong wind, or low shortwave radiation, the DWL may disappear and turn into a cool skin layer. At night, the near-surface SST was dominated by the cool skin effect, but the DWL generated in the daytime would remain if the wind speed was weak. However, the different hydrological characteristics of the observation position and its distance from the coast could affect the results of the skin effect. Whether there is a rapid change in ocean stratification in a spatial grid of nearly four square kilometers needs to be explored in the future. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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27 pages, 9862 KiB  
Article
Improvement and Assessment of Ocean Color Algorithms in the Northwest Pacific Fishing Ground Using Himawari-8, MODIS-Aqua, and VIIRS-SNPP
by Chuanyang Huang, Yang Liu, Yanping Luo, Yuntao Wang, Xudong Liu, Yong Zhang, Yunyun Zhuang and Yongjun Tian
Remote Sens. 2022, 14(15), 3610; https://doi.org/10.3390/rs14153610 - 28 Jul 2022
Cited by 2 | Viewed by 1798
Abstract
Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three [...] Read more.
Chlorophyll-a (Chl-a) is an important marine indicator, and the improvement in Chl-a concentration retrieval for ocean color remote sensing is always a major challenge. This study focuses on the northwest Pacific fishing ground (NPFG) to evaluate and improve the Chl-a products of three mainstream remote sensing satellites, Himawari-8, MODIS-Aqua, and VIIRS-SNPP. We analyzed in situ data and found that an in situ Chl-a concentration of 0.3 mg m−3 could be used as a threshold to distinguish the systematic deviation of remote sensing Chl-a data in the NPFG. Based on this threshold, we optimized the Chl-a algorithms of the three satellites by data grouping, and integrated multisource satellite Chl-a data by weighted averaging to acquire high-coverage merged data. The merged data were thoroughly verified by Argo Chl-a data. The Chl-a front of merged Chl-a data could be represented accurately and completely and had a good correlation with the distribution of the NPFG. The most important marine factors for Chl-a are nutrients and temperature, which are affected by mesoscale eddies and variations in the Kuroshio extension. The variation trend of merged Chl-a data is consistent with mesoscale eddies and Kuroshio extension and has more sensitive responses to the marine climatic conditions of ENSO. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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27 pages, 10706 KiB  
Article
Assessing the Relationship between Freshwater Flux and Sea Surface Salinity
by Hao Liu, Zexun Wei and Xunwei Nie
Remote Sens. 2022, 14(9), 2149; https://doi.org/10.3390/rs14092149 - 30 Apr 2022
Cited by 2 | Viewed by 1691
Abstract
Exploring the relationship between evaporation (E)-minus-precipitation (P) and sea surface salinity (SSS) is vital for understanding global hydrological cycle changes and investigating the salinity budget. This study quantifies the uncertainty in the relationship between EP and SSS [...] Read more.
Exploring the relationship between evaporation (E)-minus-precipitation (P) and sea surface salinity (SSS) is vital for understanding global hydrological cycle changes and investigating the salinity budget. This study quantifies the uncertainty in the relationship between EP and SSS based on satellite data over the 50°S–50°N ocean from 2012 to 2017 in 140 sets of combinations of E, P and SSS. We find that the uncertainty (10%) in the variability of freshwater flux (FWF) over 2012–2017 is smaller than that in SSS (15%). The difference in the combination of sets of “E-P-SSS” products can lead to the 10% difference in RMSD and 25% difference in area-weighted mean correlation coefficients between SSS tendency and FWF. There is a 24.1~58% area over the global ocean with a significant (p value < 0.05) positive correlation between the FWF and SSS tendency derived from satellite products. The seasonal EMP and SSS tendencies show larger correlation coefficients and lower RMSDs over most sets compared with those on nonseasonal time scales. Large uncertainty in the FWF-SSS tendency relation associated with spread among products prevents the use of one combination of E, P and SSS from satellite-based products for salinity budget analysis. Full article
(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products)
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