{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T12:37:49Z","timestamp":1763210269428,"version":"3.45.0"},"publisher-location":"Singapore","reference-count":139,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819516254","type":"print"},{"value":"9789819516261","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-1626-1_10","type":"book-chapter","created":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T12:36:53Z","timestamp":1763210213000},"page":"285-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Remote Sensing Technologies in Aerospace: Applications and Opportunities"],"prefix":"10.1007","author":[{"given":"Carina L.","family":"Lopes","sequence":"first","affiliation":[]},{"given":"Renato","family":"Mendes","sequence":"additional","affiliation":[]},{"given":"Leonardo","family":"Azevedo","sequence":"additional","affiliation":[]},{"given":"Magda C.","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Am\u00e9rico S.","family":"Ribeiro","sequence":"additional","affiliation":[]},{"given":"Afonso","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Beatriz","family":"Biguino","sequence":"additional","affiliation":[]},{"given":"Ana C.","family":"Brito","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o M.","family":"Dias","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","unstructured":"J.L. Awange, J.B. Kyalo Kiema, Fundamentals of remote sensing, in Environmental Science and Engineering (2013). https:\/\/doi.org\/10.1007\/978-3-642-34085-7_7","DOI":"10.1007\/978-3-642-34085-7_7"},{"key":"10_CR2","unstructured":"CCRS, Fundamentals of Remote Sensing. A Canada Centre for Remote Sensing Tutorial, no. November. 1999"},{"key":"10_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-03978-6_1","author":"JA Richards","year":"1999","unstructured":"J.A. Richards, X. Jia, Sources and characteristics of remote sensing image data. Remote. Sens. Digit. Image Anal. (1999). https:\/\/doi.org\/10.1007\/978-3-662-03978-6_1","journal-title":"Remote. Sens. Digit. Image Anal."},{"key":"10_CR4","unstructured":"M. Barnsley, Digital remotely-sensed data and their characteristics. Geogr. Inf. Syst. 1(1) (1999)"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"C. Elachi, J. Zyl, Solid surfaces sensing in the visible and near infrared, in Introduction to the Physics and Techniques of Remote Sensing (2021). https:\/\/doi.org\/10.1002\/9781119523048.ch3","DOI":"10.1002\/9781119523048.ch3"},{"key":"10_CR6","doi-asserted-by":"publisher","unstructured":"C. Elachi, J. van Zyl, Solid\u2010surface sensing: thermal infrared, in Introduction to the Physics and Techniques of Remote Sensing (2006). https:\/\/doi.org\/10.1002\/0471783390.ch4","DOI":"10.1002\/0471783390.ch4"},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"C. Elachi, J. van Zyl, Solid\u2010surface sensing: microwave and radio frequencies, in Introduction to the Physics and Techniques of Remote Sensing (2006). https:\/\/doi.org\/10.1002\/0471783390.ch6","DOI":"10.1002\/0471783390.ch6"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"M.A. Wulder et al., Fifty years of Landsat science and impacts (2022). https:\/\/doi.org\/10.1016\/j.rse.2022.113195","DOI":"10.1016\/j.rse.2022.113195"},{"key":"10_CR9","doi-asserted-by":"publisher","unstructured":"S.L. Ustin, E.M. Middleton, Current and near-term advances in Earth observation for ecological applications (2021). https:\/\/doi.org\/10.1186\/s13717-020-00255-4","DOI":"10.1186\/s13717-020-00255-4"},{"key":"10_CR10","doi-asserted-by":"publisher","unstructured":"H. Astola, T. H\u00e4me, L. Sirro, M. Molinier, J. Kilpi, Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region. Remote Sens. Environ. 223 (2019). https:\/\/doi.org\/10.1016\/j.rse.2019.01.019","DOI":"10.1016\/j.rse.2019.01.019"},{"key":"10_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2015.12.024","author":"DP Roy","year":"2016","unstructured":"D.P. Roy et al., Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. (2016). https:\/\/doi.org\/10.1016\/j.rse.2015.12.024","journal-title":"Remote Sens. Environ."},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"V. Nasiri, A. Deljouei, F. Moradi, S.M.M. Sadeghi, S.A. Borz, Land use and land cover mapping using Sentinel-2, Landsat-8 satellite images, and google earth engine: a comparison of two composition methods. Remote Sens. 14(9) (2022). https:\/\/doi.org\/10.3390\/rs14091977","DOI":"10.3390\/rs14091977"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"G. Forkuor, K. Dimobe, I. Serme, J.E. Tondoh, Landsat-8 versus Sentinel-2: examining the added value of sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience Remote Sens. 55(3) (2018). https:\/\/doi.org\/10.1080\/15481603.2017.1370169","DOI":"10.1080\/15481603.2017.1370169"},{"key":"10_CR14","doi-asserted-by":"publisher","unstructured":"Z. Zhu, S. Qiu, S. Ye, Remote sensing of land change: a multifaceted perspective (2022). https:\/\/doi.org\/10.1016\/j.rse.2022.113266","DOI":"10.1016\/j.rse.2022.113266"},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"S. Jombo, S. Adelabu, Evaluating Landsat-8, Landsat-9 and Sentinel-2 imageries in land use and land cover (LULC) classification in a heterogeneous urban area. GeoJournal 88 (2023). https:\/\/doi.org\/10.1007\/s10708-023-10982-8","DOI":"10.1007\/s10708-023-10982-8"},{"key":"10_CR16","doi-asserted-by":"publisher","unstructured":"K. Darwish, S. Smith, A comparison of Landsat-8 OLI, Sentinel-2 MSI and planetscope satellite imagery for assessing coastline change in El-Alamein, Egypt \u2020. Eng. Proc. 10(1) (2021). https:\/\/doi.org\/10.3390\/ecsa-8-11258","DOI":"10.3390\/ecsa-8-11258"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"J.E. Pardo-Pascual et al., Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Eng. 188 (2024). https:\/\/doi.org\/10.1016\/j.coastaleng.2023.104426","DOI":"10.1016\/j.coastaleng.2023.104426"},{"key":"10_CR18","doi-asserted-by":"publisher","unstructured":"K. Vos et al., Benchmarking satellite-derived shoreline mapping algorithms. Commun. Earth Environ. 4(1) (2023). https:\/\/doi.org\/10.1038\/s43247-023-01001-2","DOI":"10.1038\/s43247-023-01001-2"},{"key":"10_CR19","doi-asserted-by":"publisher","unstructured":"G. Misra, F. Cawkwell, A. Wingler, Status of phenological research using sentinel-2 data: A review. Remote Sens. 12(17) (2020). https:\/\/doi.org\/10.3390\/RS12172760","DOI":"10.3390\/RS12172760"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"J.A. Caparros-Santiago, L.C. Quesada-Ruiz, V. Rodriguez-Galiano, Can land surface phenology from Sentinel-2 time-series be used as an indicator of Macaronesian ecosystem dynamics? Ecol. Inf. 77 (2023). https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102239","DOI":"10.1016\/j.ecoinf.2023.102239"},{"key":"10_CR21","doi-asserted-by":"publisher","unstructured":"K. Kowalski, C. Senf, P. Hostert, D. Pflugmacher, Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinformation 92 (2020). https:\/\/doi.org\/10.1016\/j.jag.2020.102172","DOI":"10.1016\/j.jag.2020.102172"},{"key":"10_CR22","doi-asserted-by":"publisher","unstructured":"W.A. Hovis et al., Nimbus-7 coastal zone color scanner: System description and initial imagery. Science 210(4465) (1980). https:\/\/doi.org\/10.1126\/science.210.4465.60","DOI":"10.1126\/science.210.4465.60"},{"key":"10_CR23","doi-asserted-by":"publisher","DOI":"10.1002\/9780471743989.vse10144","author":"GC Feldman","year":"2007","unstructured":"G.C. Feldman, Sea-viewing wide field-of-view sensor (Sea Wi FS). Van Nostrand\u2019s Scientific Encyclopedia (2007). https:\/\/doi.org\/10.1002\/9780471743989.vse10144","journal-title":"Van Nostrand\u2019s Scientific Encyclopedia"},{"key":"10_CR24","doi-asserted-by":"publisher","unstructured":"V.V. Salomonson, W.L. Barnes, P.W. Maymon, H.E. Montgomery, H. Ostrow, MODIS: advanced facility instrument for studies of the earth as a system. IEEE Trans. Geosci. Remote. Sens. 27(2) (1989). https:\/\/doi.org\/10.1109\/36.20292","DOI":"10.1109\/36.20292"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"C. Cao, F.J. De Luccia, X. Xiong, R. Wolfe, F. Weng, Early on-orbit performance of the visible infrared imaging radiometer suite onboard the suomi national polar-orbiting partnership (S-NPP) satellite. IEEE Trans. Geosci. Remote. Sens. 52(2) (2014). https:\/\/doi.org\/10.1109\/TGRS.2013.2247768","DOI":"10.1109\/TGRS.2013.2247768"},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"C. Donlon et al., The global monitoring for environment and security (GMES) Sentinel-3 mission. Remote. Sens. Environ. 120 (2012). https:\/\/doi.org\/10.1016\/j.rse.2011.07.024","DOI":"10.1016\/j.rse.2011.07.024"},{"key":"10_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3383812","volume":"62","author":"G Meister","year":"2024","unstructured":"G. Meister et al., The Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: system design and prelaunch radiometric performance. IEEE Trans. Geosci. Remote Sens. 62, 1\u201318 (2024). https:\/\/doi.org\/10.1109\/TGRS.2024.3383812","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"S. Djavidnia, F. M\u00e9lin, N. Hoepffner, Comparison of global ocean colour data records. Ocean Sci. 6(1) (2010). https:\/\/doi.org\/10.5194\/os-6-61-2010","DOI":"10.5194\/os-6-61-2010"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"J.E. O\u2019Reilly et al., Ocean color chlorophyll algorithms for SeaWiFS. J. Geophys. Res. Ocean. 103(C11) (1998). https:\/\/doi.org\/10.1029\/98JC02160","DOI":"10.1029\/98JC02160"},{"key":"10_CR30","doi-asserted-by":"publisher","unstructured":"S.B. Groom et al., Satellite ocean colour: current status and future perspective (2019). https:\/\/doi.org\/10.3389\/fmars.2019.00485","DOI":"10.3389\/fmars.2019.00485"},{"key":"10_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/9781118945179.ch10","author":"J Nieke","year":"2015","unstructured":"J. Nieke et al., Ocean and land color imager on sentinel-3. Opt. Payloads Space Mission. (2015). https:\/\/doi.org\/10.1002\/9781118945179.ch10","journal-title":"Opt. Payloads Space Mission."},{"key":"10_CR32","doi-asserted-by":"publisher","unstructured":"I. Cetini\u0107 et al., Phytoplankton composition from sPACE: requirements, opportunities, and challenges (2024). https:\/\/doi.org\/10.1016\/j.rse.2023.113964","DOI":"10.1016\/j.rse.2023.113964"},{"key":"10_CR33","unstructured":"IOCCG, IOCCG Report Number 03: Remote sensing of ocean colour in coastal, and other optically-complex. Waters 3 (2000)"},{"key":"10_CR34","unstructured":"IOCCG, Atmospheric correction for remotely-sensed ocean-colour products. IOCCG Report Number 10(10) (2010)"},{"key":"10_CR35","unstructured":"A. Sutcliffe, A.C. Brito, C. S\u00e1, F. Sousa, D. Boutov, V. Brotas, Observa\u00e7\u00e3o da Terra: uso de imagens de temperatura da superf\u00edcie do mar e cor do oceano para a monitoriza\u00e7\u00e3o de \u00e1guas costeiras e oce\u00e2nicas. Lisboa (2016)"},{"key":"10_CR36","doi-asserted-by":"publisher","unstructured":"S. Kalluri, C. Cao, A. Heidinger, A. Ignatov, J. Key, T. Smith, The advanced very high resolution radiometer contributing to earth observations for over 40 years (2021). https:\/\/doi.org\/10.1175\/BAMS-D-20-0088.1","DOI":"10.1175\/BAMS-D-20-0088.1"},{"key":"10_CR37","doi-asserted-by":"publisher","unstructured":"P.J. Minnett et al., Half a century of satellite remote sensing of sea-surface temperature. Remote. Sens. Environ. 233 (2019). https:\/\/doi.org\/10.1016\/j.rse.2019.111366","DOI":"10.1016\/j.rse.2019.111366"},{"key":"10_CR38","doi-asserted-by":"publisher","unstructured":"C.J. Merchant et al., Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Sci. Data 6(1) (2019). https:\/\/doi.org\/10.1038\/s41597-019-0236-x","DOI":"10.1038\/s41597-019-0236-x"},{"key":"10_CR39","doi-asserted-by":"publisher","unstructured":"E. Alerskans, J.L. H\u00f8yer, C.L. Gentemann, L.T. Pedersen, P. Nielsen-Englyst, C. Donlon, Construction of a climate data record of sea surface temperature from passive microwave measurements. Remote Sens. Environ. 236 (2020). https:\/\/doi.org\/10.1016\/j.rse.2019.111485","DOI":"10.1016\/j.rse.2019.111485"},{"key":"10_CR40","doi-asserted-by":"publisher","unstructured":"M. National Academies of Sciences, Engineering, A strategy for active remote sensing amid increased demand for radio spectrum (2015). https:\/\/doi.org\/10.17226\/21729","DOI":"10.17226\/21729"},{"key":"10_CR41","doi-asserted-by":"publisher","unstructured":"P. Schreiner, R. K\u00f6nig, K.H. Neumayer, A. Reinhold, On precise orbit determination based on DORIS, GPS and SLR using Sentinel-3A\/B and -6A and subsequent reference frame determination based on DORIS-only. Adv. Space Res. 72(1) (2023). https:\/\/doi.org\/10.1016\/j.asr.2023.04.002","DOI":"10.1016\/j.asr.2023.04.002"},{"key":"10_CR42","doi-asserted-by":"publisher","unstructured":"J.F. Legeais et al., Copernicus sea level space observations: a basis for assessing mitigation and developing adaptation strategies to sea level rise. Front. Marine Sci. 8 (2021). https:\/\/doi.org\/10.3389\/fmars.2021.704721","DOI":"10.3389\/fmars.2021.704721"},{"key":"10_CR43","doi-asserted-by":"publisher","unstructured":"S. Singh, R.K. Tiwari, V. Sood, R. Kaur, S. Prashar, The legacy of scatterometers: review of applications and perspective (2022). https:\/\/doi.org\/10.1109\/MGRS.2022.3145500","DOI":"10.1109\/MGRS.2022.3145500"},{"key":"10_CR44","doi-asserted-by":"publisher","unstructured":"R.M. Asiyabi, A. Ghorbanian, S.N. Tameh, M. Amani, S. Jin, A. Mohammadzadeh, Synthetic Aperture Radar (SAR) for ocean: a review. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 16 (2023). https:\/\/doi.org\/10.1109\/JSTARS.2023.3310363","DOI":"10.1109\/JSTARS.2023.3310363"},{"issue":"4","key":"10_CR45","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1023\/A:1009998812838","volume":"3","author":"LA Boorman","year":"1999","unstructured":"L.A. Boorman, Salt marshes\u2014present functioning and future change. Mangrove Salt Marshes 3(4), 227\u2013241 (1999). https:\/\/doi.org\/10.1023\/A:1009998812838","journal-title":"Mangrove Salt Marshes"},{"issue":"4","key":"10_CR46","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1002\/2013WR014676","volume":"50","author":"G Mariotti","year":"2014","unstructured":"G. Mariotti, J. Carr, Dual role of salt marsh retreat: long-term loss and short-term resilience. Water Resour. Res. 50(4), 2963\u20132974 (2014). https:\/\/doi.org\/10.1002\/2013WR014676","journal-title":"Water Resour. Res."},{"key":"10_CR47","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.scitotenv.2018.10.381","volume":"653","author":"CL Lopes","year":"2019","unstructured":"C.L. Lopes, R. Mendes, I. Ca\u00e7ador, J.M. Dias, Evaluation of long-term estuarine vegetation changes through Landsat imagery. Sci. Total. Environ. 653, 512\u2013522 (2019). https:\/\/doi.org\/10.1016\/j.scitotenv.2018.10.381","journal-title":"Sci. Total. Environ."},{"issue":"16","key":"10_CR48","doi-asserted-by":"publisher","first-page":"4534","DOI":"10.1002\/ldr.4050","volume":"32","author":"CL Lopes","year":"2021","unstructured":"C.L. Lopes, R. Mendes, I. Ca\u00e7ador, J.M. Dias, Assessing salt marsh loss and degradation by combining long-term LANDSAT imagery and numerical modelling. Land Degrad. Dev. 32(16), 4534\u20134545 (2021). https:\/\/doi.org\/10.1002\/ldr.4050","journal-title":"Land Degrad. Dev."},{"key":"10_CR49","doi-asserted-by":"publisher","unstructured":"C.L. Lopes, R. Mendes, I. Ca\u00e7ador, J.M. Dias, Assessing salt marsh extent and condition changes with 35 years of Landsat imagery: Tagus Estuary case study. Remote Sens. Environ. 247 (2020). https:\/\/doi.org\/10.1016\/j.rse.2020.111939","DOI":"10.1016\/j.rse.2020.111939"},{"issue":"5","key":"10_CR50","doi-asserted-by":"publisher","first-page":"677","DOI":"10.3934\/environsci.2017.5.677","volume":"4","author":"GJ Miller","year":"2017","unstructured":"G.J. Miller, J.T. Morris, C. Wang, Mapping salt marsh dieback and condition in South Carolina\u2019s North Inlet-Winyah Bay National Estuarine Research Reserve using remote sensing. AIMS Environ. Sci. 4(5), 677\u2013689 (2017). https:\/\/doi.org\/10.3934\/environsci.2017.5.677","journal-title":"AIMS Environ. Sci."},{"key":"10_CR51","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/J.JAG.2017.12.003","volume":"68","author":"Y Mo","year":"2018","unstructured":"Y. Mo, M.S. Kearney, J.C.A. Riter, F. Zhao, D.R. Tilley, Assessing biomass of diverse coastal marsh ecosystems using statistical and machine learning models. Int. J. Appl. Earth Obs. Geoinf. 68, 189\u2013201 (2018). https:\/\/doi.org\/10.1016\/J.JAG.2017.12.003","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"10_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2024.104140","volume":"133","author":"G Yang","year":"2024","unstructured":"G. Yang et al., MFI: a mudflat index based on hyperspectral satellite images for mapping coastal mudflats. Int. J. Appl. Earth Obs. Geoinformation 133, 104140 (2024)","journal-title":"Int. J. Appl. Earth Obs. Geoinformation"},{"issue":"10","key":"10_CR53","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1016\/j.oceaneng.2011.05.006","volume":"38","author":"T Kuleli","year":"2011","unstructured":"T. Kuleli, A. Guneroglu, F. Karsli, M. Dihkan, Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey. Ocean Eng. 38(10), 1141\u20131149 (2011). https:\/\/doi.org\/10.1016\/j.oceaneng.2011.05.006","journal-title":"Ocean Eng."},{"issue":"14","key":"10_CR54","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.3390\/rs11141653","volume":"11","author":"ML Laengner","year":"2019","unstructured":"M.L. Laengner, K. Siteur, D. van der Wal, Trends in the seaward extent of saltmarshes across Europe from long-term satellite data. Remote Sensing 11(14), 1653 (2019). https:\/\/doi.org\/10.3390\/rs11141653","journal-title":"Remote Sensing"},{"issue":"11","key":"10_CR55","doi-asserted-by":"publisher","first-page":"3417","DOI":"10.3390\/rs4113417","volume":"4","author":"N Murray","year":"2012","unstructured":"N. Murray, S. Phinn, R. Clemens, C. Roelfsema, R. Fuller, Continental scale mapping of tidal flats across east asia using the landsat archive. Remote Sens. 4(11), 3417\u20133426 (2012). https:\/\/doi.org\/10.3390\/rs4113417","journal-title":"Remote Sens."},{"key":"10_CR56","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.ecss.2018.08.007","volume":"213","author":"C Sun","year":"2018","unstructured":"C. Sun, S. Fagherazzi, Y. Liu, Classification mapping of salt marsh vegetation by flexible monthly NDVI time-series using Landsat imagery. Estuar. Coast. Shelf Sci. 213, 61\u201380 (2018). https:\/\/doi.org\/10.1016\/j.ecss.2018.08.007","journal-title":"Estuar. Coast. Shelf Sci."},{"issue":"14","key":"10_CR57","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1080\/01431160600589179","volume":"27","author":"H Xu","year":"2006","unstructured":"H. Xu, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27(14), 3025\u20133033 (2006). https:\/\/doi.org\/10.1080\/01431160600589179","journal-title":"Int. J. Remote Sens."},{"key":"10_CR58","unstructured":"J.W. Rouse, R.H. Haas, J.A. Schell, D.W. Deering, Monitoring vegetation systems in the Great Okains with ERTS, in Third Earth Resources Technology Satellite-1 Symposium, vol. 1. (1973), pp. 325\u2013333, 10\/citeulike-article-id:12009708"},{"issue":"1","key":"10_CR59","doi-asserted-by":"publisher","first-page":"28","DOI":"10.2307\/1942049","volume":"5","author":"JA Gamon","year":"1995","unstructured":"J.A. Gamon et al., Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 5(1), 28\u201341 (1995). https:\/\/doi.org\/10.2307\/1942049","journal-title":"Ecol. Appl."},{"issue":"2","key":"10_CR60","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","volume":"48","author":"J Qi","year":"1994","unstructured":"J. Qi, A. Chehbouni, A.R. Huete, Y.H. Kerr, S. Sorooshian, A modified soil adjusted vegetation index. Remote Sens. Environ. 48(2), 119\u2013126 (1994). https:\/\/doi.org\/10.1016\/0034-4257(94)90134-1","journal-title":"Remote Sens. Environ."},{"key":"10_CR61","unstructured":"J. Qi, Y. Kerr, A. Chehbouni, External factor consideration in vegetation index development, in Proceedings of 6th International Symposium on Physical Measurements and Signatures in Remote Sensing. (1994), pp. 723\u2013730"},{"issue":"1","key":"10_CR62","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/BF00031911","volume":"101","author":"B Pinty","year":"1992","unstructured":"B. Pinty, M.M. Verstraete, GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio 101(1), 15\u201320 (1992). https:\/\/doi.org\/10.1007\/BF00031911","journal-title":"Vegetatio"},{"key":"10_CR63","doi-asserted-by":"publisher","unstructured":"I.J. Castro, J.M. Dias, C.L. Lopes, Assessing shoreline changes in fringing salt marshes from satellite remote sensing data. Remote Sens. 15(18) (2023). https:\/\/doi.org\/10.3390\/rs15184475","DOI":"10.3390\/rs15184475"},{"key":"10_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2024.101226","volume":"35","author":"A Freitas","year":"2024","unstructured":"A. Freitas, J.M. Dias, C.L. Lopes, Application of remote sensing methods for monitoring extent, condition and blue carbon storage in salt marshes. Remote Sens. Appl. Soc. Environ. 35, 101226 (2024). https:\/\/doi.org\/10.1016\/j.rsase.2024.101226","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"10_CR65","doi-asserted-by":"publisher","unstructured":"A.I. Sousa, A.I. Lilleb\u00f8, M.A. Pardal, I. Ca\u00e7ador, Productivity and nutrient cycling in salt marshes: Contribution to ecosystem health. Estuarine, Coastal Shelf Sci. 87(4) (2010). https:\/\/doi.org\/10.1016\/j.ecss.2010.03.007","DOI":"10.1016\/j.ecss.2010.03.007"},{"key":"10_CR66","doi-asserted-by":"publisher","unstructured":"M.M. Duarte, L. Azevedo, Automatic detection and identification of floating marine debris using multispectral satellite imagery. IEEE Trans. Geosci. Remote. Sens. 61 (2023). https:\/\/doi.org\/10.1109\/TGRS.2023.3283607","DOI":"10.1109\/TGRS.2023.3283607"},{"key":"10_CR67","doi-asserted-by":"publisher","unstructured":"L.J.J. Meijer, T. van Emmerik, R. van der Ent, C. Schmidt, L. Lebreton, More than 1000 rivers account for 80% of global riverine plastic emissions into the ocean. Sci. Adv. 7(18) (2021). https:\/\/doi.org\/10.1126\/sciadv.aaz5803","DOI":"10.1126\/sciadv.aaz5803"},{"key":"10_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpolbul.2021.112227","volume":"166","author":"MC Sousa","year":"2021","unstructured":"M.C. Sousa et al., Modelling the distribution of microplastics released by wastewater treatment plants in Ria de Vigo (NW Iberian Peninsula). Mar. Pollut. Bull. 166, 112227 (2021). https:\/\/doi.org\/10.1016\/j.marpolbul.2021.112227","journal-title":"Mar. Pollut. Bull."},{"key":"10_CR69","doi-asserted-by":"publisher","unstructured":"K.R. Dyer, Estuaries: A Physical Introduction, Estuarine and Coastal Marine Science. Wiley (1973). https:\/\/doi.org\/10.1016\/0302-3524(73)90042-X","DOI":"10.1016\/0302-3524(73)90042-X"},{"issue":"16","key":"10_CR70","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1016\/j.csr.2005.06.011","volume":"25","author":"B Dzwonkowski","year":"2005","unstructured":"B. Dzwonkowski, X.H. Yan, Tracking of a Chesapeake Bay estuarine outflow plume with satellite-based ocean color data. Cont. Shelf Res. 25(16), 1942\u20131958 (2005)","journal-title":"Cont. Shelf Res."},{"key":"10_CR71","doi-asserted-by":"publisher","unstructured":"S.L. Palacios, T.D. Peterson, R.M. Kudela, Development of synthetic salinity from remote sensing for the Columbia River plume. J. Geophys. Res. Ocean. 114(4) (2009). https:\/\/doi.org\/10.1029\/2008JC004895","DOI":"10.1029\/2008JC004895"},{"key":"10_CR72","doi-asserted-by":"publisher","unstructured":"G.S. Sald\u00edas, M. Sobarzo, J. Largier, C. Moffat, R. Letelier, Seasonal variability of turbid river plumes off central Chile based on high-resolution MODIS imagery. Remote Sens. Environ. 123 (2012). https:\/\/doi.org\/10.1016\/j.rse.2012.03.010","DOI":"10.1016\/j.rse.2012.03.010"},{"key":"10_CR73","doi-asserted-by":"publisher","unstructured":"C. Petus, V. Marieu, S. Novoa, G. Chust, N. Bruneau, J. M. Froidefond, Monitoring spatio-temporal variability of the Adour River turbid plume (Bay of Biscay, France) with MODIS 250-m imagery. Continental Shelf Res. 74 (2014). https:\/\/doi.org\/10.1016\/j.csr.2013.11.011","DOI":"10.1016\/j.csr.2013.11.011"},{"key":"10_CR74","doi-asserted-by":"publisher","unstructured":"R. Mendes et al., On the generation of internal waves by river plumes in subcritical initial conditions. Sci. Rep. 11(1) (2021). https:\/\/doi.org\/10.1038\/s41598-021-81464-5","DOI":"10.1038\/s41598-021-81464-5"},{"key":"10_CR75","doi-asserted-by":"publisher","unstructured":"R. Mendes, G.S. Sald\u00edas, M. deCastro, M. G\u00f3mez-Gesteira, N. Vaz, J.M. Dias, Seasonal and interannual variability of the Douro turbid river plume, northwestern Iberian Peninsula. Remote Sens. Environ. 194 (2017). https:\/\/doi.org\/10.1016\/j.rse.2017.04.001","DOI":"10.1016\/j.rse.2017.04.001"},{"key":"10_CR76","doi-asserted-by":"publisher","unstructured":"R. Mendes et al., Observation of a turbid plume using MODIS imagery: the case of Douro estuary (Portugal). Remote Sens. Environ. 154(1) (2014). https:\/\/doi.org\/10.1016\/j.rse.2014.08.003","DOI":"10.1016\/j.rse.2014.08.003"},{"key":"10_CR77","doi-asserted-by":"publisher","unstructured":"F. Toublanc, N.K. Ayoub, P. Marsaleix, On the role of wind and tides in shaping the Gironde River plume (Bay of Biscay). Continental Shelf Res. 253 (2023). https:\/\/doi.org\/10.1016\/j.csr.2022.104891","DOI":"10.1016\/j.csr.2022.104891"},{"key":"10_CR78","doi-asserted-by":"publisher","unstructured":"J. Tavora, G.A. Gon\u00e7alves, E.H. Fernandes, M.S. Salama, D. van der Wal, Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm. Front. Marine Sci. 10 (2023). https:\/\/doi.org\/10.3389\/fmars.2023.1215327","DOI":"10.3389\/fmars.2023.1215327"},{"key":"10_CR79","doi-asserted-by":"publisher","unstructured":"A.C. Teodoro, H. Goncalves, F. Veloso-Gomes, J.A. Gon\u00e7alves, Modeling of the douro river plume size, obtained through image segmentation of MERIS data. IEEE Geosci. Remote Sens. Lett. 6(1) (2009). https:\/\/doi.org\/10.1109\/LGRS.2008.2008446","DOI":"10.1109\/LGRS.2008.2008446"},{"key":"10_CR80","doi-asserted-by":"publisher","unstructured":"R. Mendes, M.C. Sousa, M. deCastro, M. G\u00f3mez-Gesteira, J.M. Dias, New insights into the Western Iberian Buoyant Plume: iInteraction between the Douro and Minho River plumes under winter conditions. Prog. Ocean. (2016). https:\/\/doi.org\/10.1016\/j.pocean.2015.11.006","DOI":"10.1016\/j.pocean.2015.11.006"},{"key":"10_CR81","doi-asserted-by":"publisher","unstructured":"J. Font et al., SMOS first data analysis for sea surface salinity determination. Int. J. Remote. Sens. 34(9\u201310) (2013). https:\/\/doi.org\/10.1080\/01431161.2012.716541","DOI":"10.1080\/01431161.2012.716541"},{"key":"10_CR82","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.898092","author":"DM Le Vine","year":"2007","unstructured":"D.M. Le Vine, G.S.E. Lagerloef, F.R. Colomb, S.H. Yueh, F.A. Pellerano, Aquarius: an instrument to monitor sea surface salinity from space. IEEE Trans. Geosci. Remote Sens. (2007). https:\/\/doi.org\/10.1109\/TGRS.2007.898092","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR83","doi-asserted-by":"publisher","unstructured":"A. Korosov, F. Counillon, J.A. Johannessen, Monitoring the spreading of the Amazon freshwater plume by MODIS, SMOS, Aquarius, and TOPAZ. J. Geophys. Res. Ocean. 120(1) (2015). https:\/\/doi.org\/10.1002\/2014JC010155","DOI":"10.1002\/2014JC010155"},{"key":"10_CR84","doi-asserted-by":"publisher","unstructured":"J. Hopkins et al., Detection and variability of the Congo River plume from satellite derived sea surface temperature, salinity, ocean colour and sea level. Remote Sens. Environ. 139 (2013). https:\/\/doi.org\/10.1016\/j.rse.2013.08.015","DOI":"10.1016\/j.rse.2013.08.015"},{"issue":"1","key":"10_CR85","doi-asserted-by":"publisher","first-page":"37","DOI":"10.21715\/GB.V16I1.497","volume":"16","author":"RV Martins","year":"2002","unstructured":"R.V. Martins, L.D. Lacerda, S. Mounier, H.H.M. Paraquetti, W.S. Marques, Caracteriza\u00e7\u00e3o hidroqu\u00edmica, distribui\u00e7\u00e3o e especia\u00e7\u00e3o de merc\u00fario nos estu\u00e1rios dos rios Cear\u00e1 e Pacoti, regi\u00e3o metropolitana de Fortaleza, Cear\u00e1, Brasil. Geochimica Brasiliensis 16(1), 37 (2002). https:\/\/doi.org\/10.21715\/GB.V16I1.497","journal-title":"Geochimica Brasiliensis"},{"key":"10_CR86","doi-asserted-by":"publisher","unstructured":"R.J. Davies-Colley, D.G. Smith, Turbidity, suspended sediment, and water clarity: a review (2001). https:\/\/doi.org\/10.1111\/j.1752-1688.2001.tb03624.x","DOI":"10.1111\/j.1752-1688.2001.tb03624.x"},{"key":"10_CR87","doi-asserted-by":"publisher","unstructured":"C. Petus, G. Chust, F. Gohin, D. Doxaran, J.M. Froidefond, Y. Sagarminaga, Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery. Continental Shelf Res. 30(5) (2010). https:\/\/doi.org\/10.1016\/j.csr.2009.12.007","DOI":"10.1016\/j.csr.2009.12.007"},{"key":"10_CR88","doi-asserted-by":"publisher","unstructured":"H. Loisel, V. Vantrepotte, C. Jamet, D. Ngoc Dat, Challenges and new advances in ocean color remote sensing of coastal waters. Top. Ocean. (2013). https:\/\/doi.org\/10.5772\/56414","DOI":"10.5772\/56414"},{"key":"10_CR89","doi-asserted-by":"publisher","unstructured":"J.D. Nash, J.N. Moum, River plumes as a source of large-amplitude internal waves in the coastal ocean. Nature 437(7057) (2005). https:\/\/doi.org\/10.1038\/nature03936","DOI":"10.1038\/nature03936"},{"key":"10_CR90","doi-asserted-by":"publisher","unstructured":"H.M. Dierssen et al., Synergies between NASA\u2019s hyperspectral aquatic missions PACE, GLIMR, and SBG: opportunities for new science and applications. J. Geophys. Res. Biogeosciences 128(10) (2023). https:\/\/doi.org\/10.1029\/2023JG007574","DOI":"10.1029\/2023JG007574"},{"key":"10_CR91","doi-asserted-by":"publisher","unstructured":"L.D. Talley, G.L. Pickard, W.J. Emery, J.H. Swift, Descriptive Physical Oceanography: An Introduction, 6th edn. (2011). https:\/\/doi.org\/10.1016\/C2009-0-24322-4","DOI":"10.1016\/C2009-0-24322-4"},{"key":"10_CR92","unstructured":"EPA, Climate Change Indicators: Sea Surface Temperature. Environmental Protection Agency."},{"key":"10_CR93","doi-asserted-by":"publisher","unstructured":"R.A. Hastings, L.A. Rutterford, J.J. Freer, R.A. Collins, S.D. Simpson, M.J. Genner, Climate change drives poleward increases and equatorward declines in marine species. Curr. Biol. 30(8) (2020). https:\/\/doi.org\/10.1016\/j.cub.2020.02.043","DOI":"10.1016\/j.cub.2020.02.043"},{"key":"10_CR94","unstructured":"NOAA, Why do scientists measure sea surface temperature? Natl. Ocean. Serv."},{"issue":"2","key":"10_CR95","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s12210-024-01243-y","volume":"35","author":"D Zardi","year":"2024","unstructured":"D. Zardi, Atmosphere and ocean interactions. Rendiconti Lincei. Scienze Fisiche e Naturali 35(2), 311\u2013325 (2024). https:\/\/doi.org\/10.1007\/s12210-024-01243-y","journal-title":"Rendiconti Lincei. Scienze Fisiche e Naturali"},{"key":"10_CR96","unstructured":"EPA, Factsheet on water quality parameters\u2014temperature, Environmental Protection Agency, no. C, 2021"},{"key":"10_CR97","unstructured":"IPCC, \u201cIPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate,\u201d The Ocean and Cryosphere in a Changing Climate, no. October, 2019"},{"key":"10_CR98","doi-asserted-by":"publisher","unstructured":"Z. Wu, C. Jiang, M. Conde, J. Chen, B. Deng, The long-term spatiotemporal variability of sea surface temperature in the northwest Pacific and China offshore. Ocean Sci. 16(1) (2020). https:\/\/doi.org\/10.5194\/os-16-83-2020","DOI":"10.5194\/os-16-83-2020"},{"key":"10_CR99","doi-asserted-by":"publisher","unstructured":"B. Biguino et al., 40 years of changes in sea surface temperature along the Western Iberian Coast. Sci. Total. Environ. 888 (2023). https:\/\/doi.org\/10.1016\/j.scitotenv.2023.164193","DOI":"10.1016\/j.scitotenv.2023.164193"},{"key":"10_CR100","doi-asserted-by":"publisher","unstructured":"S. Garc\u00eda-Monteiro, J.A. Sobrino, Y. Julien, G. S\u00f2ria, D. Skokovic, Surface temperature trends in the Mediterranean Sea from MODIS data during years 2003\u20132019. Reg.Nal Stud. Mar. Sci. 49 (2022). https:\/\/doi.org\/10.1016\/j.rsma.2021.102086","DOI":"10.1016\/j.rsma.2021.102086"},{"key":"10_CR101","doi-asserted-by":"publisher","unstructured":"A.J. Hobday et al., A hierarchical approach to defining marine heatwaves. Prog. Ocean. 141 (2016). https:\/\/doi.org\/10.1016\/j.pocean.2015.12.014","DOI":"10.1016\/j.pocean.2015.12.014"},{"key":"10_CR102","doi-asserted-by":"publisher","unstructured":"T.L. Fr\u00f6licher, E.M. Fischer, N. Gruber, Marine heatwaves under global warming. Nature 560(7718) (2018). https:\/\/doi.org\/10.1038\/s41586-018-0383-9","DOI":"10.1038\/s41586-018-0383-9"},{"key":"10_CR103","doi-asserted-by":"publisher","unstructured":"E.C.J. Oliver et al., Marine heatwaves. Annu. Rev. Mar. Sci. 13, 313\u2013342 (2021). https:\/\/doi.org\/10.1146\/annurev-marine-032720-095144","DOI":"10.1146\/annurev-marine-032720-095144"},{"key":"10_CR104","doi-asserted-by":"publisher","unstructured":"R.W. Schlegel, S. Darmaraki, J.A. Benthuysen, K. Filbee-Dexter, E.C.J. Oliver, Marine cold-spells. Prog. Ocean. 198 (2021). https:\/\/doi.org\/10.1016\/j.pocean.2021.102684","DOI":"10.1016\/j.pocean.2021.102684"},{"key":"10_CR105","doi-asserted-by":"publisher","unstructured":"A.J. Hobday et al., Categorizing and naming marine heatwaves. Oceanography 31(2 Special Issue) (2018). https:\/\/doi.org\/10.5670\/oceanog.2018.205","DOI":"10.5670\/oceanog.2018.205"},{"key":"10_CR106","unstructured":"IPCC, Summary for policymakers in climate change 2022: mitigation of climate change, in Contribution of Working Group III to the Sixth Assessment Report of the IPCC (2023)"},{"key":"10_CR107","doi-asserted-by":"publisher","unstructured":"A. Simon, C. Poppeschi, S. Plecha, G. Charria, A. Russo, Coastal and regional marine heatwaves and cold spells in the northeastern Atlantic. Ocean Sci. 19(5) (2023). https:\/\/doi.org\/10.5194\/os-19-1339-2023","DOI":"10.5194\/os-19-1339-2023"},{"key":"10_CR108","unstructured":"E.C.J. Oliver, Marine heatwaves detection code (2018)"},{"key":"10_CR109","doi-asserted-by":"publisher","unstructured":"J.E. Cloern, T.S. Schraga, E. Nejad, T. Eddy, Phytoplankton as indicators of global warming? Limnol. Ocean. Lett. 9(3) (2024). https:\/\/doi.org\/10.1002\/lol2.10354","DOI":"10.1002\/lol2.10354"},{"key":"10_CR110","doi-asserted-by":"publisher","unstructured":"P.G. Falkowski, The role of phytoplankton photosynthesis in global biogeochemical cycles (1994). https:\/\/doi.org\/10.1007\/BF00014586","DOI":"10.1007\/BF00014586"},{"key":"10_CR111","doi-asserted-by":"publisher","unstructured":"M.J. Behrenfeld et al., Biospheric primary production during an ENSO transition. Science 291(5513) (2001). https:\/\/doi.org\/10.1126\/science.1055071","DOI":"10.1126\/science.1055071"},{"key":"10_CR112","unstructured":"IOCCG, Remote Sensing of inherent optical properties\u202f: fundamentals, tests of algorithms, and applications. Rep. Int. Ocean. Colour Coord. Group 5(5) (2006)"},{"key":"10_CR113","doi-asserted-by":"publisher","unstructured":"Z. Lee, K.L. Carder, Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance. Remote Sens. Environ. 89(3) (2004). https:\/\/doi.org\/10.1016\/j.rse.2003.10.013","DOI":"10.1016\/j.rse.2003.10.013"},{"key":"10_CR114","doi-asserted-by":"publisher","unstructured":"Z. Lee, K.L. Carder, R.A. Arnone, Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Appl. Opt. 41(27) (2002). https:\/\/doi.org\/10.1364\/ao.41.005755","DOI":"10.1364\/ao.41.005755"},{"key":"10_CR115","doi-asserted-by":"publisher","unstructured":"A.A. Gitelson et al., A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation. Remote Sens. Environ. 112(9) (2008). https:\/\/doi.org\/10.1016\/j.rse.2008.04.015","DOI":"10.1016\/j.rse.2008.04.015"},{"key":"10_CR116","doi-asserted-by":"publisher","unstructured":"T. Hirata, J. Aiken, N. Hardman-Mountford, T.J. Smyth, R.G. Barlow, An absorption model to determine phytoplankton size classes from satellite ocean colour. Remote Sens. Environ. 112(6) (2008) https:\/\/doi.org\/10.1016\/j.rse.2008.03.011","DOI":"10.1016\/j.rse.2008.03.011"},{"key":"10_CR117","doi-asserted-by":"publisher","unstructured":"A. Ferreira, V. Brotas, C. Palma, C. Borges, A.C. Brito, Assessing phytoplankton bloom phenology in upwelling-influenced regions using ocean color remote sensing. Remote Sens. 13(4) (2021). https:\/\/doi.org\/10.3390\/rs13040675","DOI":"10.3390\/rs13040675"},{"key":"10_CR118","doi-asserted-by":"publisher","unstructured":"M.F. Racault, C. Le Qu\u00e9r\u00e9, E. Buitenhuis, S. Sathyendranath, T. Platt, Phytoplankton phenology in the global ocean. Ecol. Indicators 14(1) (2012). https:\/\/doi.org\/10.1016\/j.ecolind.2011.07.010","DOI":"10.1016\/j.ecolind.2011.07.010"},{"key":"10_CR119","doi-asserted-by":"publisher","unstructured":"S R. Brody, M.S. Lozier, J.P. Dunne, A comparison of methods to determine phytoplankton bloom initiation. J. Geophys. Res. Ocean. 118(5) (2013). https:\/\/doi.org\/10.1002\/jgrc.20167","DOI":"10.1002\/jgrc.20167"},{"key":"10_CR120","doi-asserted-by":"publisher","unstructured":"S.M. Chiswell, P.H.R. Calil, P.W. Boyd, Spring blooms and annual cycles of phytoplankton: a unified perspective. J. Plankton Res. 37(3) (2014). https:\/\/doi.org\/10.1093\/plankt\/fbv021","DOI":"10.1093\/plankt\/fbv021"},{"key":"10_CR121","doi-asserted-by":"publisher","unstructured":"R. Sanders et al., The biological carbon pump in the north Atlantic. Prog. Ocean. 129(PB) (2014). https:\/\/doi.org\/10.1016\/j.pocean.2014.05.005","DOI":"10.1016\/j.pocean.2014.05.005"},{"key":"10_CR122","doi-asserted-by":"publisher","unstructured":"A. Corredor-Acosta, C.E. Morales, S. Hormazabal, I. Andrade, M.A. Correa-Ramirez, Phytoplankton phenology in the coastal upwelling region off central-southern Chile (35\u00b0S-38\u00b0S): Time-space variability, coupling to environmental factors, and sources of uncertainty in the estimates. J. Geophys. Res.: Ocean. 120(2) (2015). https:\/\/doi.org\/10.1002\/2014JC010330","DOI":"10.1002\/2014JC010330"},{"key":"10_CR123","doi-asserted-by":"crossref","unstructured":"T. Platt, C. Fuentes-Yaco, K.T. Frank, Spring algal bloom and larval fish survival. Nature 423 (2003)","DOI":"10.1038\/423398b"},{"key":"10_CR124","doi-asserted-by":"publisher","unstructured":"W.A. Hovis, The Nimbus-7 Coastal Zone Color Scanner (CZCS) Program, in Oceanography from Space, ed by J.F.R. Gower (Springer US, Boston, MA, 1981), pp. 213\u2013225. https:\/\/doi.org\/10.1007\/978-1-4613-3315-9_27","DOI":"10.1007\/978-1-4613-3315-9_27"},{"key":"10_CR125","doi-asserted-by":"publisher","unstructured":"K.H.P. Str\u00f6mberg, T.J. Smyth, J.I. Allen, S. Pitois, T.D.O\u2019Brien, Estimation of global zooplankton biomass from satellite ocean colour. J. Mar. Syst. 78(1) (2009). https:\/\/doi.org\/10.1016\/j.jmarsys.2009.02.004","DOI":"10.1016\/j.jmarsys.2009.02.004"},{"key":"10_CR126","doi-asserted-by":"publisher","unstructured":"M.K. Trzcinski, E. Devred, T. Platt, S. Sathyendranath, Variation in ocean colour may help predict cod and haddock recruitment. Mar. Ecol. Prog. Ser. 491 (2013). https:\/\/doi.org\/10.3354\/meps10451","DOI":"10.3354\/meps10451"},{"key":"10_CR127","doi-asserted-by":"publisher","unstructured":"J.C. Burtenshaw et al., Acoustic and satellite remote sensing of blue whale seasonality and habitat in the Northeast Pacific. Deep.-Sea Res. Part II: Top. Stud. Ocean. 51(10\u201311) SPEC. ISS. (2004). https:\/\/doi.org\/10.1016\/j.dsr2.2004.06.020","DOI":"10.1016\/j.dsr2.2004.06.020"},{"key":"10_CR128","doi-asserted-by":"publisher","unstructured":"R.M. Suryan, J.A. Santora, W.J. Sydeman, New approach for using remotely sensed chlorophyll a to identify seabird hotspots. Mar. Ecol. Prog. Ser. 451 (2012). https:\/\/doi.org\/10.3354\/meps09597","DOI":"10.3354\/meps09597"},{"key":"10_CR129","doi-asserted-by":"publisher","unstructured":"R.G. Asch, C.A. Stock, J.L. Sarmiento, Climate change impacts on mismatches between phytoplankton blooms and fish spawning phenology. Glob. Chang. Biol. 25(8) (2019). https:\/\/doi.org\/10.1111\/gcb.14650","DOI":"10.1111\/gcb.14650"},{"key":"10_CR130","doi-asserted-by":"publisher","unstructured":"D.H. Cushing, Plankton production and year-class strength in fish populations: an update of the match\/mismatch hypothesis. Adv. Mar. Biol. 26(C) (1990). https:\/\/doi.org\/10.1016\/S0065-2881(08)60202-3","DOI":"10.1016\/S0065-2881(08)60202-3"},{"key":"10_CR131","doi-asserted-by":"publisher","unstructured":"J.F. Schweigert, M. Thompson, C. Fort, D.E. Hay, T.W. Therriault, L.N. Brown, Factors linking pacific herring (clupea pallasi) productivity and the spring plankton bloom in the strait of Georgia, British Columbia, Canada. Prog. Ocean. 115 (2013). https:\/\/doi.org\/10.1016\/j.pocean.2013.05.017","DOI":"10.1016\/j.pocean.2013.05.017"},{"key":"10_CR132","doi-asserted-by":"publisher","unstructured":"M.J. Malick, S.P. Cox, F.J. Mueter, R.M. Peterman, Linking phytoplankton phenology to salmon productivity along a north-south gradient in the Northeast Pacific Ocean. Can. J. Fish. Aquat. Sci. 72(5) (2015). https:\/\/doi.org\/10.1139\/cjfas-2014-0298","DOI":"10.1139\/cjfas-2014-0298"},{"issue":"1\u20134","key":"10_CR133","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.pocean.2009.07.031","volume":"83","author":"J Ar\u00edstegui","year":"2009","unstructured":"J. Ar\u00edstegui et al., Sub-regional ecosystem variability in the Canary Current upwelling. Prog. Oceanogr. 83(1\u20134), 33\u201348 (2009). https:\/\/doi.org\/10.1016\/j.pocean.2009.07.031","journal-title":"Prog. Oceanogr."},{"key":"10_CR134","doi-asserted-by":"publisher","unstructured":"D. Szalaj, A. Silva, P. R\u00e9, H. Cabral, Detecting regime shifts in the Portuguese continental shelf ecosystem within the last three decades. Front. Marine Sci. 8 (2021). https:\/\/doi.org\/10.3389\/fmars.2021.629130","DOI":"10.3389\/fmars.2021.629130"},{"key":"10_CR135","doi-asserted-by":"publisher","unstructured":"T. Veiga-Malta et al., First representation of the trophic structure and functioning of the Portuguese continental shelf ecosystem: Insights into the role of sardine. Mar. Ecol. Prog. Ser. 617\u2013618 (2019). https:\/\/doi.org\/10.3354\/meps12724","DOI":"10.3354\/meps12724"},{"key":"10_CR136","doi-asserted-by":"publisher","unstructured":"S. Garrido, C.D. van der Lingen, Feeding Biology and Ecology. (2014). https:\/\/doi.org\/10.1201\/b16682","DOI":"10.1201\/b16682"},{"key":"10_CR137","doi-asserted-by":"publisher","unstructured":"S. Garrido, R. Ben-Hamadou, P.B. Oliveira, M.E. Cunha, M.A. Ch\u00edcharo, C.D. Van Der Lingen, Diet and feeding intensity of sardine Sardina pilchardus: correlation with satellite-derived chlorophyll data. Mar. Ecol. Prog. Ser. 354 (2008). https:\/\/doi.org\/10.3354\/meps07201","DOI":"10.3354\/meps07201"},{"key":"10_CR138","doi-asserted-by":"publisher","unstructured":"A. Ferreira, S. Garrido, J.L. Costa, A. Teles-Machado, V. Brotas, A.C. Brito, What drives the recruitment of European sardine in Atlanto-Iberian waters (SW Europe)? Insights from a 22-year analysis. Sci. Total. Environ. 881 (2023). https:\/\/doi.org\/10.1016\/j.scitotenv.2023.163421","DOI":"10.1016\/j.scitotenv.2023.163421"},{"key":"10_CR139","doi-asserted-by":"publisher","unstructured":"S. Garrido et al., Temperature and food-mediated variability of European Atlantic sardine recruitment. Prog. Ocean. 159 (2017). https:\/\/doi.org\/10.1016\/j.pocean.2017.10.006","DOI":"10.1016\/j.pocean.2017.10.006"}],"container-title":["Progress in Optical Science and Photonics","Advanced Optical Sensors for Aerospace Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-1626-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T12:36:58Z","timestamp":1763210218000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-1626-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819516254","9789819516261"],"references-count":139,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-1626-1_10","relation":{},"ISSN":["2363-5096","2363-510X"],"issn-type":[{"value":"2363-5096","type":"print"},{"value":"2363-510X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"16 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}