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Schaeffer BA, Whitman P, Vandermeulen R, Hu C, Mannino A, Salisbury J, Efremova B, Conmy R, Coffer M, Salls W, Ferriby H, Reynolds N. Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States. MARINE POLLUTION BULLETIN 2023; 196:115558. [PMID: 37757532 PMCID: PMC10845072 DOI: 10.1016/j.marpolbul.2023.115558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 09/13/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
The Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr-1, while oil films require glint >10-5 sr-1. Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites, such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations.
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Affiliation(s)
- Blake A Schaeffer
- US EPA, Office of Research and Development, Durham, NC 27709, United States of America.
| | - Peter Whitman
- Oak Ridge Institute for Science and Education, US EPA, Durham, NC 27709, United States of America
| | - Ryan Vandermeulen
- National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Silver Spring, MD, United States of America; Science Systems and Applications, Inc., Lanham, MD, United States of America
| | - Chuanmin Hu
- College of Marine Science, University of South Florida, St. Petersburg, FL, United States of America
| | - Antonio Mannino
- National Aeronautics and Space Administration, Goddard Space Flight Center, Greenbelt, MD, United States of America
| | - Joseph Salisbury
- University of New Hampshire, Durham, NH, United States of America
| | | | - Robyn Conmy
- US EPA, Office of Research and Development, Cincinnati, OH 45268, United States of America
| | - Megan Coffer
- National Oceanic and Atmospheric Administration, NESDIS Center for Satellite Applications and Research, Greenbelt, MD, United States of America; Global Science and Technology Inc., Durham, NC, United States of America
| | - Wilson Salls
- US EPA, Office of Research and Development, Durham, NC 27709, United States of America
| | - Hannah Ferriby
- Tetra Tech, Research Triangle Park, NC 27709, United States of America
| | - Natalie Reynolds
- RTI International, Research Triangle Park, NC, United States of America
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Podlejski W, Berline L, Nerini D, Doglioli A, Lett C. A new Sargassum drift model derived from features tracking in MODIS images. MARINE POLLUTION BULLETIN 2023; 188:114629. [PMID: 36860021 DOI: 10.1016/j.marpolbul.2023.114629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 06/18/2023]
Abstract
Massive Sargassum stranding events affect erratically numerous countries from the Gulf of Guinea to the Gulf of Mexico. Forecasting transport and stranding of Sargassum aggregates require progress in detection and drift modelling. Here we evaluate the role of currents and wind, i.e. windage, on Sargassum drift. Sargassum drift is computed from automatic tracking using MODIS 1 km Sargassum detection dataset, and compared to reference surface current and wind estimates from collocated drifters and altimetric products. First, we confirm the strong total wind effect of ≈3 % (≈2 % of pure windage), but also show the existence of a deflection angle of ≈10° between Sargassum drift and wind directions. Second, our results suggest reducing the role of currents on drift to 80 % of its velocity, likely because of Sargassum resistance to flow. These results should significantly improve our understanding of the drivers of Sargassum dynamics and the forecast of stranding events.
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Affiliation(s)
- Witold Podlejski
- Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, Marseille, France; Marbec, Université de Monpellier, CNRS, Ifremer, IRD, Sète, France.
| | - Léo Berline
- Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, Marseille, France
| | - David Nerini
- Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, Marseille, France
| | - Andrea Doglioli
- Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, Marseille, France
| | - Christophe Lett
- Marbec, Université de Monpellier, CNRS, Ifremer, IRD, Sète, France
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Revisited Estimation of Moderate Resolution Sargassum Fractional Coverage Using Decametric Satellite Data (S2-MSI). REMOTE SENSING 2021. [DOI: 10.3390/rs13245106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Since 2011, massive stranding of the brown algae Sargassum has regularly affected the coastal waters of the West Caribbean, Brazil and West Africa, leading to significant environmental and socio-economic impacts. The AFAI algal index (Alternative Floating Algae Index) is often used with remote sensing data in order to estimate the Sargassum coverage, and more precisely the AFAI deviation, which consists of the difference between AFAI and AFAI of the Sargassum-free background. In this study, the AFAI deviation is computed using NASA’s 1 km Terra/MODIS (Moderate-Resolution Imaging Spectroradiometer) and ESA/Copernicus’s 20 m Sentinel-2/MSI (Multi Spectral Instrument) for the same sites and at the same time. Both MODIS and MSI AFAI deviations are compared to confirm the relevance of AFAI deviation technique for two very different spatial resolutions. A high coefficient of determination was found, thus confirming a satisfactory downsampling from 20 m (MSI) to 1 km (MODIS). Then, AFAI deviations are used to estimate the fractional coverage of Sargassum (noted FC). A new linear relationship between the MODIS AFAI deviation and FC is established using the dense Sargassum aggregations observed by MSI data. The AFAI deviation is proportional to FC with a factor of proportionality close to 0.08. Finally, it is shown that the factor is dependent on the Sargassum spectral reflectance, submersion or physiological state.
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Meng L, Liu H, Ustin SL, Zhang X. Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings. SENSORS 2021; 21:s21155184. [PMID: 34372418 PMCID: PMC8347763 DOI: 10.3390/s21155184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 11/16/2022]
Abstract
Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the 'mixed pixels' nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.
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Affiliation(s)
- Linghua Meng
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huanjun Liu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China;
- Correspondence: ; Tel.: +86-188-4556-5736
| | - Susan L. Ustin
- Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA;
| | - Xinle Zhang
- School of Information Technology, Jilin Agricultural University, Changchun 130102, China;
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