A database of global coastal conditions
1.Horning, N., Robinson, J. A., Sterling, E. J., Turner, W. & Spector, S. Remote sensing for ecology and conservation. Techniques in Ecology & Conservation Series (Oxford University Press, 2010).2.Li, J. et al. A review of remote sensing for environmental monitoring in China. Remote Sens. 12, 1130 (2020).ADS
Google Scholar
3.Carter, W. D. & Paulson, R. W. Introduction to monitoring dynamic environmental phenomena of the world using satellite data collection systems. (U.S. Geological Survey, 1979).4.Nurdin, S., Mustapha, M. A. & Lihan, T. The relationship between sea surface temperature and chlorophyll-a concentration in fisheries aggregation area in the archipelagic waters of spermonde using satellite images. AIP Conf. Proc. 1571, 466–472 (2013).ADS
Google Scholar
5.Ward, D., Phinn, S. R. & Murray, A. T. Monitoring growth in rapidly urbanizing areas using remotely sensed data. Prof. Geogr. 52, 371–386 (2000).
Google Scholar
6.Singh, A. Review article: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10, 989–1003 (1989).
Google Scholar
7.Dewan, A. M. & Yamaguchi, Y. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Appl. Geogr. 29, 390–401 (2009).
Google Scholar
8.Green, K., Kempka, D. & Lackey, L. Using remote sensing to detect and monitor land-cover and land-use change. Photogramm. Eng. Remote Sens. 60, 331–337 (1994).
Google Scholar
9.Nagendra, H. Using remote sensing to assess biodiversity. Int. J. Remote Sens. 22, 2377–2400 (2001).
Google Scholar
10.Rosenqvist, Å., Milne, A., Lucas, R., Imhoff, M. & Dobson, C. A review of remote sensing technology in support of the Kyoto Protocol. Environ. Sci. Policy 6, 441–455 (2003).
Google Scholar
11.Liu, J. A process-based boreal ecosystem productivity simulator using remote sensing inputs. Remote Sens. Environ. 62, 158–175 (1997).ADS
Google Scholar
12.Colwell, R. R. Global climate and infectious disease: The cholera paradigm. Science 274, 2025–2031 (1996).ADS
PubMed
CAS
Google Scholar
13.Escobar, L. E. et al. A global map of suitability for coastal Vibrio cholerae under current and future climate conditions. Acta Trop. 149, 202–211 (2015).PubMed
Google Scholar
14.Watts, N. et al. The 2019 report of The Lancet Countdown on health and climate change: Ensuring that the health of a child born today is not defined by a changing climate. Lancet 394, 1836–1878 (2019).PubMed
Google Scholar
15.Alesheikh, A. A., Ghorbanali, A. & Nouri, N. Coastline change detection using remote sensing. Int. J. Environ. Sci. Technol. 4, 61–66 (2007).
Google Scholar
16.Specter, C. & Gayle, D. Managing technology transfer for coastal zone development: Caribbean experts identify major issues. Int. J. Remote Sens. 11, 1729–1740 (1990).
Google Scholar
17.Green, E. P., Mumby, P. J., Edwards, A. J. & Clark, C. D. A review of remote sensing for the assessment and management of tropical coastal resources. Coast. Manag. 24, 1–40 (1996).
Google Scholar
18.NASA. MODIS (Moderate Resolution Imaging Spectroradiometer). https://modis.gsfc.nasa.gov/about/ (2021).19.Kilpatrick, K. A. et al. A decade of sea surface temperature from MODIS. Remote Sens. Environ. 165, 27–41 (2015).ADS
Google Scholar
20.Esaias, W. E. et al. An overview of MODIS capabilities for ocean science observations. IEEE Trans. Geosci. Remote Sens. 36, 1250–1265 (1998).ADS
Google Scholar
21.Donlon, C. J. et al. Toward improved validation of satellite SST measurements for climate research. J. Clim. 15, 353–369 (2002).ADS
Google Scholar
22.Minnett, P. J. Satellite infrared scanning radiometers — AVHRR and ATSR/M. in Microwave Remote Sensing for Oceanographic and Marine Weather-Forecast Models 141–163 (Springer Netherlands, 1990).23.Hillger, D. et al. First-Light Imagery from Suomi NPP VIIRS. Bull. Am. Meteorol. Soc. 94, 1019–1029 (2013).ADS
Google Scholar
24.O’Brien, J. From MODIS to VIIRS – Making the Switch for Air Quality Professionals. NASA Earth Science/Applied Science https://appliedsciences.nasa.gov/our-impact/news/modis-viirs-making-switch-air-quality-professionals (2020).25.Minnett, P. J., Evans, R. H., Podestá, G. P. & Kilpatrick, K. A. Sea-surface temperature from Suomi-NPP VIIRS: Algorithm development and uncertainty estimation. in SPIE 9111, Ocean Sensing and Monitoring VI (eds. Hou, W. W. & Arnone, R. A.) 91110C (2014).26.Drusch, M. et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 120, 25–36 (2012).ADS
Google Scholar
27.Donlon, C. et al. The global ocean data assimilation experiment high-resolution sea surface temperature pilot project. Bull. Am. Meteorol. Soc. 88, 1197–1214 (2007).ADS
Google Scholar
28.NOAA. Ocean Facts: Why do scientists measure sea surface temperature? https://oceanservice.noaa.gov/facts/sea-surface-temperature.html (2020).29.Wei, G. F., Tang, D. L. & Wang, S. Distribution of chlorophyll and harmful algal blooms (HABs): A review on space based studies in the coastal environments of Chinese marginal seas. Adv. Sp. Res. 41, 12–19 (2008).ADS
CAS
Google Scholar
30.O’Reilly, J. E. et al. Ocean color chlorophyll algorithms for SeaWiFS. J. Geophys. Res. Ocean. 103, 24937–24953 (1998).ADS
Google Scholar
31.Hu, C., Lee, Z. & Franz, B. Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. J. Geophys. Res. Ocean. 117, C01011 (2012).ADS
Google Scholar
32.Vezzulli, L. et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proc. Natl. Acad. Sci. 113, E5062–E5071 (2016).PubMed
PubMed Central
CAS
Google Scholar
33.Lipp, E. K., Huq, A. & Colwell, R. R. Effects of global climate on infectious disease: The Cholera model. Clin. Microbiol. Rev. 15, 757–770 (2002).PubMed
PubMed Central
Google Scholar
34.Grimes, J. D. et al. Viewing marine bacteria, their activity and response to environmental drivers from orbit: Satellite remote sensing of bacteria. Microb. Ecol. 67, 489–500 (2014).PubMed
PubMed Central
Google Scholar
35.Shen, L., Xu, H. & Guo, X. Satellite remote sensing of harmful algal blooms (HABs) and a potential synthesized framework. Sensors 12, 7778–803 (2012).36.Hayashi, M., Jin, F. & Stuecker, M. F. Dynamics for El Niño-La Niña asymmetry constrain equatorial-Pacific warming pattern. Nat. Commun. 11, 1–10 (2020).
Google Scholar
37.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).38.Minnett, P. J. et al. Sea-surface temperature measurements from the moderate-resolution imaging spectroradiometer (MODIS) on Aqua and Terra. in IEEE International Geoscience and Remote Sensing Symposium Proceedings. 2004 7, 4576–4579 (2004).39.Minnett, P. J. The validation of sea surface temperature retrievals from spaceborne infrared radiometers. in Oceanography from Space (Springer Netherlands, 2010).40.Minnett, P. J. & Corlett, G. K. A pathway to generating climate data records of sea-surface temperature from satellite measurements. Deep Sea Res. Part II Top. Stud. Oceanogr. 77–80, 44–51 (2012).ADS
Google Scholar
41.Castaneda-Guzman, M., Mantilla-Saltos, G., Murray, K. A., Settlage, R. & Escobar, L. E. A database of global coastal conditions. Figshare https://doi.org/10.6084/m9.figshare.c.5660263.v1 (2021).42.R Core Team. R: A Language and Environment for Statistical Computing. (2020).43.NOAA. National Oceanic and Atmospheric Administration (NOAA) Coastal Watch. https://coastwatch.pfeg.noaa.gov/erddapinfo/ (2021).44.Castaneda-Guzman, M., Mantilla-Saltos, G., Murray, K. A., Settlage, R. & Escobar, L. E. Methods and code. Figshare https://doi.org/10.6084/m9.figshare.13708642.v4 (2021).45.Stanford. Best practices for file formats. https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats (2021).46.UCAR Community Programs. Network Common Data Form (NetCDF). https://www.unidata.ucar.edu/software/netcdf/ (2021).47.Michna, P. & Woods, M. RNetCDF: Interface to ‘NetCDF’ Datasets. (2019).48.Hijmans, R. J. raster: Geographic Data Analysis and Modeling. (2020).49.ArcGIS. What is a raster data? https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm (2021).50.United Nations. United Nations Convention on the Law of the Sea. 1833 U.N.T.S. 397 (1982).51.Tilstone, G. H. et al. Assessment of MODIS-Aqua chlorophyll-a algorithms in coastal and shelf waters of the eastern Arabian Sea. Cont. Shelf Res. 65, 14–26 (2013).ADS
Google Scholar
52.Hoge, F. E. et al. Validation of Terra-MODIS phytoplankton chlorophyll fluorescence line height. I. Initial airborne Lidar results. Appl. Opt. 42, 2767-2771 (2003).ADS
PubMed
Google Scholar
53.Remer, L. A. Validation of MODIS aerosol retrieval over ocean. Geophys. Res. Lett. 29, 8008 (2002).ADS
Google Scholar
54.Gentemann, C. L. Three way validation of MODIS and AMSR-E sea surface temperatures. J. Geophys. Res. Ocean. 119, 2583–2598 (2014).ADS
Google Scholar
55.Fang, H., Wei, S. & Liang, S. Validation of MODIS and CYCLOPES LAI products using global field measurement data. Remote Sens. Environ. 119, 43–54 (2012).ADS
Google Scholar
56.Hosoda, K., Murakami, H., Sakaida, F. & Kawamura, H. Algorithm and validation of sea surface temperature observation using MODIS sensors aboard terra and aqua in the western North Pacific. J. Oceanogr. 63, 267–280 (2007).
Google Scholar
57.Hao, Y. et al. Validation of MODIS sea surface temperature product in the coastal waters of the Yellow Sea. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 1667–1680 (2017).ADS
Google Scholar
58.Sims, D. A. et al. On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. J. Geophys. Res. Biogeosciences 111 (2006).59.Miles, T. N. & He, R. Temporal and spatial variability of Chl-a and SST on the South Atlantic Bight: Revisiting with cloud-free reconstructions of MODIS satellite imagery. Cont. Shelf Res. 30, 1951–1962 (2010).ADS
Google Scholar
60.Ma, S., Zhang, X., Ding, C., Han, W. & Lu, Y. Comparison of the spatiotemporal variation of Chl-a in the East China Sea and Bohai Sea based on long time series satellite data. in 2021 9th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) 1–6 (2021).61.Watts, N. et al. The 2020 report of The Lancet Countdown on health and climate change: Responding to converging crises. Lancet 6736 (2020).62.Moradi, M. & Kabiri, K. Spatio-temporal variability of SST and Chlorophyll-a from MODIS data in the Persian Gulf. Mar. Pollut. Bull. 98, 14–25 (2015).PubMed
CAS
Google Scholar
63.Golder, M. R. et al. Chlorophyll-a, SST and particulate organic carbon in response to the cyclone Amphan in the Bay of Bengal. J. Earth Syst. Sci. 130, 157 (2021).ADS
CAS
Google Scholar
64.Minnett, P. J., Evans, R. H., Kearns, E. J. & Brown, O. B. Sea-surface temperature measured by the Moderate Resolution Imaging Spectroradiometer (MODIS). in IEEE International Geoscience and Remote Sensing Symposium vol. 2, 1177–1179 (IEEE, 2002).65.Qin, H., Chen, G., Wang, W., Wang, D. & Zeng, L. Validation and application of MODIS-derived SST in the South China Sea. Int. J. Remote Sens. 35, 4315–4328 (2014).
Google Scholar
66.Saulquin, B., Gohin, F. & Garrello, R. Regional Objective Analysis for Merging High-Resolution MERIS, MODIS/Aqua, and SeaWiFS Chlorophyll-a Data From 1998 to 2008 on the European Atlantic Shelf. IEEE Trans. Geosci. Remote Sens. 49, 143–154 (2011).ADS
Google Scholar
67.Chen, J. & Quan, W. An improved algorithm for retrieving chlorophyll-a from the Yellow River Estuary using MODIS imagery. Environ. Monit. Assess. 185, 2243–2255 (2013).PubMed
Google Scholar
68.Hanafin, J. A. & Minnett, P. J. Thermal profiling of the sea surface skin layer using FTIR measurements. in Gas Transfer at Water Surfaces 161–166 (Blackwell Publishing, 2002).69.Wong, E. W. & Minnett, P. J. The response of the ocean thermal skin layer to variations in incident infrared radiation. J. Geophys. Res. Ocean. 123, 2475–2493 (2018).ADS
Google Scholar
70.Ward, B. Near-surface ocean temperature. J. Geophys. Res. 111, C02004 (2006).ADS
Google Scholar
71.Kilpatrick, K. A., Podestá, G. P. & Evans, R. Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res. Ocean. 106, 9179–9197 (2001).ADS
Google Scholar
72.Hollstein, A., Segl, K., Guanter, L., Brell, M. & Enesco, M. Ready-to-use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images. Remote Sens. 8, 666 (2016).ADS
Google Scholar
73.Luo, B., Minnett, P. J., Gentemann, C. & Szczodrak, G. Improving satellite retrieved night-time infrared sea surface temperatures in aerosol contaminated regions. Remote Sens. Environ. 223, 8–20 (2019).ADS
Google Scholar
74.Moore, T. S., Campbell, J. W. & Dowell, M. D. A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sens. Environ. 113, 2424–2430 (2009).ADS
Google Scholar
75.Pieri, M. et al. Assessment of three algorithms for the operational estimation of [CHL] from MODIS data in the Western Mediterranean Sea. Eur. J. Remote Sens. 48, 383–401 (2015).
Google Scholar
76.Tilstone, G. H. et al. Performance of Ocean Colour Chlorophyll-a algorithms for Sentinel-3 OLCI, MODIS-Aqua and Suomi-VIIRS in open-ocean waters of the Atlantic. Remote Sens. Environ. 260, 112444 (2021).ADS
Google Scholar More