{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T22:10:37Z","timestamp":1773180637120,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Proyecto Interuniversitario de Iniciaci\u00f3n en Investigaci\u00f3n Asociativa","award":["3IA-22\/23"],"award-info":[{"award-number":["3IA-22\/23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.<\/jats:p>","DOI":"10.3390\/rs16040647","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T08:12:03Z","timestamp":1707466323000},"page":"647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0550-0253","authenticated-orcid":false,"given":"Lien","family":"Rodr\u00edguez-L\u00f3pez","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Arquitectura y Dise\u00f1o, Universidad San Sebasti\u00e1n, Lientur 1457, Concepcion 4030000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8471-7380","authenticated-orcid":false,"given":"Denisse","family":"Alvarez","sequence":"additional","affiliation":[{"name":"Centro Bah\u00eda Lomas, Facultad de Ciencias, Universidad Santo Tom\u00e1s, Concepcion 4030000, Chile"}]},{"given":"David","family":"Bustos Usta","sequence":"additional","affiliation":[{"name":"Facultad de Oceanograf\u00eda, Universidad de Concepci\u00f3n, Concepcion 4030000, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3290-4947","authenticated-orcid":false,"given":"Iongel","family":"Duran-Llacer","sequence":"additional","affiliation":[{"name":"H\u00e9mera Centro de Observaci\u00f3n de la Tierra, Facultad de Ciencias, Ingenier\u00eda y Tecnolog\u00eda, Universidad Mayor, Camino La Pir\u00e1mide 5750, Santiago 8580745, Chile"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3588-6115","authenticated-orcid":false,"given":"Lisandra","family":"Bravo Alvarez","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Universidad de Concepci\u00f3n, Edmundo Larenas 219, Concepcion 4030000, Chile"}]},{"given":"Nathalie","family":"Fagel","sequence":"additional","affiliation":[{"name":"UR Argile, Geochimie et Environment Sedimentary (AGEs), Geology Department, University of Liege, 4000 Li\u00e8ge, Belgium"}]},{"given":"Luc","family":"Bourrel","sequence":"additional","affiliation":[{"name":"G\u00e9osciences Environnement Toulouse, UMR 5563, Universit\u00e9 de Toulouse, CNRS-IRD-OMP-CNES, 31000 Toulouse, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4661-8274","authenticated-orcid":false,"given":"Frederic","family":"Frappart","sequence":"additional","affiliation":[{"name":"INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, 33140 Villenave-d\u2019Ornon, France"}]},{"given":"Roberto","family":"Urrutia","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias Ambientales, Universidad de Concepci\u00f3n, Concepcion 4030000, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.gloplacha.2019.04.001","article-title":"Mountain Lakes: Eyes on Global Environmental Change","volume":"178","author":"Moser","year":"2019","journal-title":"Glob. Planet. Change"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1007\/s11629-020-6283-0","article-title":"Using Ecosystem Service Supply and Ecosystem Sensitivity to Identify Landscape Ecology Security Patterns in the Lanzhou-Xining Urban Agglomeration, China","volume":"17","author":"Tong","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Grebby, S., Sowter, A., Gee, D., Athab, A., De la Barreda-Bautista, B., Girindran, R., and Marsh, S. (2021). Remote Monitoring of Ground Motion Hazards in High Mountain Terrain Using Insar: A Case Study of the Lake Sarez Area, Tajikistan. Appl. Sci., 11.","DOI":"10.3390\/app11188738"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Regmi, G.R., and Huettmann, F. (2020). Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives, Springer International Publishing.","DOI":"10.1007\/978-3-030-36275-1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wolf, I.D., Croft, D.B., and Green, R.J. (2019). Nature Conservation and Nature-Based Tourism: A Paradox?. Environments, 6.","DOI":"10.3390\/environments6090104"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1134\/S2079970522700472","article-title":"Russia in the Global Natural and Ecological Space","volume":"13","author":"Klyuev","year":"2023","journal-title":"Reg. Res. Russ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10021-021-00684-y","article-title":"Are Northern Lakes in Relatively Intact Temperate Forests Showing Signs of Increasing Phytoplankton Biomass?","volume":"25","author":"Paltsev","year":"2021","journal-title":"Ecosystems"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110532","DOI":"10.1016\/j.ecolind.2023.110532","article-title":"Plankton Community Composition in Mountain Lakes and Consequences for Ecosystem Services","volume":"154","author":"Pritsch","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"126079","DOI":"10.1016\/j.limno.2023.126079","article-title":"A Review of Zooplankton Research in Chile","volume":"100","author":"Woelfl","year":"2023","journal-title":"Limnologica"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21717","DOI":"10.1038\/s41598-021-01228-z","article-title":"Applications of Unmanned Aerial Vehicles in Antarctic Environmental Research","volume":"11","author":"Navarro","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47726","DOI":"10.1007\/s11356-022-19001-8","article-title":"Anthropogenically Impacted Lake Catchments in Denmark Reveal Low Microplastic Pollution","volume":"29","author":"Kallenbach","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cantonati, M., Poikane, S., Pringle, C.M., Stevens, L.E., Turak, E., Heino, J., Richardson, J.S., Bolpagni, R., Borrini, A., and Cid, N. (2020). Characteristics, Main Impacts, and Stewardship of Natural and Artificial Freshwater Environments: Consequences for Biodiversity Conservation. Water, 12.","DOI":"10.3390\/w12010260"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101183","DOI":"10.1016\/j.ecoinf.2020.101183","article-title":"Spectral Analysis Using LANDSAT Images to Monitor the Chlorophyll-a Concentration in Lake Laja in Chile","volume":"60","author":"Parra","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-L\u00f3pez, L., Usta, D.B., Duran-Llacer, I., Alvarez, L.B., Y\u00e9pez, S., Bourrel, L., Frappart, F., and Urrutia, R. (2023). Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile. Remote Sens., 15.","DOI":"10.3390\/rs15174157"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-L\u00f3pez, L., Duran-Llacer, I., Bravo Alvarez, L., Lami, A., and Urrutia, R. (2023). Recovery of Water Quality and Detection of Algal Blooms in Lake Villarrica through Landsat Satellite Images and Monitoring Data. Remote Sens., 15.","DOI":"10.3390\/rs15071929"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-L\u00f3pez, L., Bustos Usta, D., Bravo Alvarez, L., Duran-Llacer, I., Lami, A., Mart\u00ednez-Retureta, R., and Urrutia, R. (2023). Machine Learning Algorithms for the Estimation of Water Quality Parameters in Lake Llanquihue in Southern Chile. Water, 15.","DOI":"10.3390\/w15111994"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Park, J., Kim, K.T., and Lee, W.H. (2020). Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality. Water, 12.","DOI":"10.3390\/w12020510"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101431","DOI":"10.1016\/j.ecoinf.2021.101431","article-title":"Spatio-Temporal Analysis of Chlorophyll in Six Araucanian Lakes of Central-South Chile from Landsat Imagery","volume":"65","author":"Cardenas","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Skakun, S., Kalecinski, N.I., Brown, M.G.L., Johnson, D.M., Vermote, E.F., Roger, J.C., and Franch, B. (2021). Assessing Within-Field Corn and Soybean Yield Variability from Worldview-3, Planet, Sentinel-2, and Landsat 8 Satellite Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13050872"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vrdoljak, L., and Kili\u0107 Pamukovi\u0107, J. (2022). Assessment of Atmospheric Correction Processors and Spectral Bands for Satellite-Derived Bathymetry Using Sentinel-2 Data in the Middle Adriatic. Hydrology, 9.","DOI":"10.3390\/hydrology9120215"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113089","DOI":"10.1016\/j.rse.2022.113089","article-title":"Spectral Mixture Analysis for Surveillance of Harmful Algal Blooms (SMASH): A Field-, Laboratory-, and Satellite-Based Approach to Identifying Cyanobacteria Genera from Remotely Sensed Data","volume":"279","author":"Legleiter","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"de Lima, T.M.A., Giardino, C., Bresciani, M., Barbosa, C.C.F., Fabbretto, A., Pellegrino, A., and Begliomini, F.N. (2023). Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms. Remote Sens., 15.","DOI":"10.3390\/rs15051299"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xue, B., Wang, G., Zhang, X., and Zhang, Q. (2022). Deep Learning-Based Water Quality Retrieval in an Impounded Lake Using Landsat 8 Imagery: An Application in Dongping Lake. Remote Sens., 14.","DOI":"10.3390\/rs14184505"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"117489","DOI":"10.1016\/j.envpol.2021.117489","article-title":"A New Approach to Monitor Water Quality in the Menor Sea (Spain) Using Satellite Data and Machine Learning Methods","volume":"286","author":"Salvador","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s42834-023-00170-1","article-title":"Machine-Learning-Estimation of High-Spatiotemporal-Resolution Chlorophyll-a Concentration Using Multi-Satellite Imagery","volume":"33","author":"Chusnah","year":"2023","journal-title":"Sustain. Environ. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Medina-L\u00f3pez, E., Navarro, G., Santos-Echeand\u00eda, J., Bern\u00e1rdez, P., and Caballero, I. (2023). Machine Learning for Detection of Macroalgal Blooms in the Mar Menor Coastal Lagoon Using Sentinel-2. Remote Sens., 15.","DOI":"10.3390\/rs15051208"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Berger, K., Rivera Caicedo, J.P., Martino, L., Wocher, M., Hank, T., and Verrelst, J. (2021). A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data. Remote Sens., 13.","DOI":"10.3390\/rs13020287"},{"key":"ref_28","first-page":"200222","article-title":"Deep Learning Detection of Types of Water-Bodies Using Optical Variables and Ensembling","volume":"18","author":"Nasir","year":"2023","journal-title":"Intell. Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15831","DOI":"10.1021\/acsomega.2c06441","article-title":"Applications of Machine Learning in Chemical and Biological Oceanography","volume":"8","author":"Sadaiappan","year":"2023","journal-title":"ACS Omega"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"269","DOI":"10.14358\/PERS.85.4.269","article-title":"Machine Learning-Based Ensemble Prediction of Water-Quality Variables Using Feature-Level and Decision-Level Fusion with Proximal Remote Sensing","volume":"85","author":"Peterson","year":"2019","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.scitotenv.2019.04.211","article-title":"Science of the Total Environment An Overview of Biomass Thermochemical Conversion Technologies in Malaysia","volume":"680","author":"Herng","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"111604","DOI":"10.1016\/j.rse.2019.111604","article-title":"Seamless Retrievals of Chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in Inland and Coastal Waters: A Machine-Learning Approach","volume":"240","author":"Pahlevan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Su, H., Lu, X., Chen, Z., Zhang, H., Lu, W., and Wu, W. (2021). Estimating Coastal Chlorophyll-a Concentration from Time-Series Olci Data Based on Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13040576"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"124045","DOI":"10.1088\/1748-9326\/ac302d","article-title":"Improved Predictive Performance of Cyanobacterial Blooms Using a Hybrid Statistical and Deep-Learning Method","volume":"16","author":"Li","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1002\/wer.1643","article-title":"Comparing the Performance of Machine Learning Algorithms for Remote and in Situ Estimations of Chlorophyll-a Content: A Case Study in the Tri an Reservoir, Vietnam","volume":"93","author":"Nguyen","year":"2021","journal-title":"Water Environ. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"157191","DOI":"10.1016\/j.scitotenv.2022.157191","article-title":"Modeling Ocean Surface Chlorophyll-a Concentration from Ocean Color Remote Sensing Reflectance in Global Waters Using Machine Learning","volume":"844","author":"Kolluru","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bartold, M., and Kluczek, M. (2023). A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands. Remote Sens., 15.","DOI":"10.3390\/rs15092392"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8743","DOI":"10.1038\/s41598-020-65600-1","article-title":"New Capabilities of Sentinel-2A\/B Satellites Combined with in Situ Data for Monitoring Small Harmful Algal Blooms in Complex Coastal Waters","volume":"10","author":"Caballero","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"113060","DOI":"10.1016\/j.jenvman.2021.113060","article-title":"Prediction of Harmful Algal Blooms in Large Water Bodies Using the Combined EFDC and LSTM Models","volume":"295","author":"Zheng","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1163\/15685403-00003634","article-title":"Use of Null Models to Explain Crustacean Zooplankton Assemblages in North Patagonian Lakes with Presence or Absence of Mixotrophic Ciliates (38\u00b0S, Chile)","volume":"90","author":"Woelfl","year":"2017","journal-title":"Crustaceana"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1111\/sed.12193","article-title":"A Comparison of the Sedimentary Records of the 1960 and 2010 Great Chilean Earthquakes in 17 Lakes: Implications for Quantitative Lacustrine Palaeoseismology","volume":"62","author":"Moernaut","year":"2015","journal-title":"Sedimentology"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.limno.2006.08.004","article-title":"The Distribution of Large Mixotrophic Ciliates (Stentor) in Deep North Patagonian Lakes (Chile): First Results","volume":"37","author":"Woelfl","year":"2007","journal-title":"Limnologica"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.erss.2019.04.014","article-title":"Megawatts Mask Impacts: Small Hydropower and Knowledge Politics in the Puelwillimapu, Southern Chile","volume":"54","author":"Kelly","year":"2019","journal-title":"Energy Res. Soc. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-L\u00f3pez, L., Gonz\u00e1lez-Rodr\u00edguez, L., Duran-Llacer, I., Garc\u00eda, W., Cardenas, R., and Urrutia, R. (2022). Assessment of the Diffuse Attenuation Coefficient of Photosynthetically Active Radiation in a Chilean Lake. Remote Sens., 14.","DOI":"10.3390\/rs14184568"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1038\/s41597-021-01076-6","article-title":"The Swiss Data Cube, Analysis Ready Data Archive Using Earth Observations of Switzerland","volume":"8","author":"Chatenoux","year":"2021","journal-title":"Sci. Data"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.rse.2018.07.015","article-title":"Atmospheric Correction of Metre-Scale Optical Satellite Data for Inland and Coastal Water Applications","volume":"216","author":"Vanhellemont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2019.03.010","article-title":"Adaptation of the Dark Spectrum Fitting Atmospheric Correction for Aquatic Applications of the Landsat and Sentinel-2 Archives","volume":"225","author":"Vanhellemont","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"29948","DOI":"10.1364\/OE.397456","article-title":"Sensitivity Analysis of the Dark Spectrum Fitting Atmospheric Correction for Metre- and Decametre-Scale Satellite Imagery Using Autonomous Hyperspectral Radiometry","volume":"28","author":"Vanhellemont","year":"2020","journal-title":"Opt Express"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2014.01.009","article-title":"Turbid Wakes Associated with Offshore Wind Turbines Observed with Landsat 8","volume":"145","author":"Vanhellemont","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2015.02.007","article-title":"Advantages of High Quality SWIR Bands for Ocean Colour Processing: Examples from Landsat-8","volume":"161","author":"Vanhellemont","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"Vanhellemont, Q., and Ruddick, K. (2016, January 9\u201313). Acolite for Sentinel-2: Aquatic Applications of Msi Imagery. Proceedings of the 2016 ESA Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-L\u00f3pez, L., Duran-Llacer, I., Gonz\u00e1lez-Rodr\u00edguez, L., Cardenas, R., and Urrutia, R. (2021). Retrieving Water Turbidity in Araucanian Lakes (South-Central Chile) Based on Multispectral Landsat Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13163133"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"113295","DOI":"10.1016\/j.rse.2022.113295","article-title":"A Bayesian Approach for Remote Sensing of Chlorophyll-a and Associated Retrieval Uncertainty in Oligotrophic and Mesotrophic Lakes","volume":"283","author":"Werther","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8497","DOI":"10.1109\/JSTARS.2021.3105746","article-title":"Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Setiawan, F., Matsushita, B., Hamzah, R., Jiang, D., and Fukushima, T. (2019). Long-Term Change of the Secchi Disk Depth in Lake Maninjau, Indonesia Shown by Landsat TM and ETM+ Data. Remote Sens., 11.","DOI":"10.3390\/rs11232875"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"110103","DOI":"10.1016\/j.ecolind.2023.110103","article-title":"Detection of Changes in the Hydrobiological Parameters of the Oder River during the Ecological Disaster in July 2022 Based on Multi-Parameter Probe Tests and Remote Sensing Methods","volume":"148","author":"Absalon","year":"2023","journal-title":"Ecol. Indic."},{"key":"ref_58","first-page":"e01877","article-title":"Spatial-Temporal Variability Analysis of Water Quality Using Remote Sensing Data: A Case Study of Lake Manyame","volume":"21","author":"Kowe","year":"2023","journal-title":"Sci. Afr."},{"key":"ref_59","first-page":"309","article-title":"Monitoring Vegetation Systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"162168","DOI":"10.1016\/j.scitotenv.2023.162168","article-title":"Increase in Chlorophyll-a Concentration in Lake Taihu from 1984 to 2021 Based on Landsat Observations","volume":"873","author":"Yin","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_61","first-page":"782506","article-title":"Detection of Surface Algal Blooms Using the Newly Developed Algorithm Surface Algal Bloom Index (SABI)","volume":"7825","author":"Alawadi","year":"2010","journal-title":"Remote Sens. Ocean. Sea Ice Large Water Reg."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1016\/j.rse.2009.05.012","article-title":"A Novel Ocean Color Index to Detect Floating Algae in the Global Oceans","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ma, J., Jin, S., Li, J., He, Y., and Shang, W. (2021). Spatio-Temporal Variations and Driving Forces of Harmful Algal Blooms in Chaohu Lake: A Multi-Source Remote Sensing Approach. Remote Sens., 13.","DOI":"10.3390\/rs13030427"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Markogianni, V., Kalivas, D., Petropoulos, G.P., and Dimitriou, E. (2020). Estimating Chlorophyll-a of Inland Water Bodies in Greece Based on Landsat Data. Remote Sens., 12.","DOI":"10.3390\/rs12132087"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"L08403","DOI":"10.1029\/2005GL022688","article-title":"Remote Estimation of Canopy Chlorophyll Content in Crops","volume":"32","author":"Gitelson","year":"2005","journal-title":"Geophys Res. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hassan, M.A., Yang, M., Rasheed, A., Jin, X., Xia, X., Xiao, Y., and He, Z. (2018). Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat. Remote Sens., 10.","DOI":"10.3390\/rs10060809"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Korstanje, J. (2021). Advanced Forecasting with Python, Springer.","DOI":"10.1007\/978-1-4842-7150-6"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"28469","DOI":"10.1007\/s11356-021-18205-8","article-title":"Forecasts of Cardiac and Respiratory Mortality in Tehran, Iran, Using ARIMAX and CNN-LSTM Models","volume":"29","author":"Mahmudimanesh","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/01621459.1985.10477131","article-title":"Dynamic Generalized Linear Models and Bayesian Forecasting","volume":"80","author":"West","year":"1985","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A Review of Recurrent Neural Networks: Lstm Cells and Network Architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1214\/009053604000000201","article-title":"Mean Squared Error of Empirical Predictor","volume":"32","author":"Das","year":"2004","journal-title":"Ann. Statist."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1038\/s42256-019-0077-5","article-title":"Learning with Known Operators Reduces Maximum Error Bounds","volume":"1","author":"Maier","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Luetkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer.","DOI":"10.1007\/978-3-540-27752-1"},{"key":"ref_75","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, Springer."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"114312","DOI":"10.1016\/j.eswa.2020.114312","article-title":"Evaluation of Feature Selection Methods Based on Artificial Neural Network Weights","volume":"168","author":"Barbosa","year":"2021","journal-title":"Expert Syst. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/647\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:57:48Z","timestamp":1760104668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,9]]},"references-count":76,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16040647"],"URL":"https:\/\/doi.org\/10.3390\/rs16040647","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,9]]}}}