Acta Limnologica Brasiliensia
https://actalb.org/article/doi/10.1590/S2179-975X6924
Acta Limnologica Brasiliensia
Seção Temática: Métodos

Coordinating in situ lake sampling with satellite acquisition days provides a mechanism for addressing data scarcity: a case study from Lake Yojoa, Honduras

Coordenar a amostragem in situ de lagos com dias de aquisição de satélite é um bom mecanismo para lidar com a escassez de dados: um estudo de caso do Lago Yojoa, Honduras

Jemma Fadum; Bethel Steele; Matthew Ross; Mia Groff; Ed Hall

http://dx.doi.org/10.1590/S2179-975X6924 Acta Limnol. Bras. (Online), vol.37, e2, 2025
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Abstract

Aim: In this study, we present the results of a project which used Landsat Collection 2 Surface Reflectance data and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data to develop a machine learning model to estimate Secchi depth in Lake Yojoa, Honduras.

Methods: Satellite remote sensing data obtained within a 7-day window of an in situ measurement were matched with in situ Secchi depth measurements and were partitioned into train-test-validate data sets for model development.

Results: The machine learning model had good (R2= 0.57) agreement and reasonable uncertainty (MAE = 0.58 m) between remotely estimated and in situ observed Secchi depth. Application of the machine learning model increased the monitoring record of Lake Yojoa from 6 years of measured data to a 23-year record.

Conclusions: This model demonstrates the utility of coordinating in situ sampling schedules of short-term research projects with satellite imagery acquisition schedules in order to increase the temporal coverage of remote sensing derived estimates of water quality in understudied lakes.

Keywords

remote sensing; water clarity; water quality trends

Resumo

Objetivo: Neste estudo, apresentamos os resultados de um projeto que utilizou dados de refletância de superfície da Landsat Collection 2 e dados de reanálise v5 (ERA5) do Centro Europeu de Previsões Meteorológicas de Médio Prazo (ECMWF) para desenvolver um modelo de aprendizado de máquina para estimar a profundidade do disco de Secchi no Lago Yojoa, Honduras.

Métodos: Os dados de sensoriamento remoto por satélite, obtidos dentro de uma janela de 7 dias de uma medição in situ, foram combinados com medições in situ da profundidade de Secchi e particionados em conjuntos de treinamento, teste e validação para o desenvolvimento do modelo.

Resultados: O modelo de aprendizado de máquina apresentou boa concordância (R2 = 0,57) e incerteza razoável (MAE = 0,58 m) entre as estimativas remotas da profundidade de Secchi e as observações in situ. A aplicação do modelo de aprendizado de máquina ampliou o registro de monitoramento do Lago Yojoa, de 6 anos de dados medidos para um total de 23 anos.

Conclusões: Este modelo demonstra a utilidade de coordenar cronogramas de amostragem in situ de projetos de pesquisa de curto prazo com cronogramas de aquisição de imagens de satélite, aumentando assim a cobertura temporal de estimativas derivadas de sensoriamento remoto da qualidade da água em lagos pouco estudados.

Palavras-chave

sensoriamento remoto; transparência da água; tendências de qualidade da água

Referências

Alikas, K., & Kratzer, S., 2017. Improved retrieval of Secchi depth for optically-complex waters using remote sensing data. Ecol. Indic. 77, 218-227. http://doi.org/10.1016/j.ecolind.2017.02.007.

Allen, G.H., Yang, X., Gardner, J., Holliman, J., David, C.H., & Ross, M., 2020. Timing of landsat overpasses effectively captures flow conditions of large rivers. Remote Sens. (Basel) 12(9), 9. http://doi.org/10.3390/rs12091510.

Basterrechea, M., 2008. Informe final de la consultoría “Modelo para la estimación de cargas tolerables de contaminantes en el Lago de Yojoa, para las actividades de producción pesquera, hidroeléctrica, agropecuaria y comercial, y recomendaciones para su regulación y control”. Honduras: SERNA-MARENA.

Bender, M.A., Knutson, T.R., Tuleya, R.E., Sirutis, J.J., Vecchi, G.A., Garner, S.T., & Held, I.M., 2010. Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science 327(5964), 454-458. PMid:20093471. http://doi.org/10.1126/science.1180568.

Cairo, C., Barbosa, C., Lobo, F., Novo, E., Carlos, F., Maciel, D., Flores Júnior, R., Silva, E., & Curtarelli, V., 2020. Hybrid chlorophyll-a algorithm for assessing trophic states of a tropical Brazilian reservoir based on MSI/Sentinel-2 data. Remote Sens. (Basel) 12(1), 1. http://doi.org/10.3390/rs12010040.

Carpenter, S.R., & Brock, W.A., 2006. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9(3), 311-318. PMid:16958897. http://doi.org/10.1111/j.1461-0248.2005.00877.x.

Carpenter, S.R., Cole, J.J., Pace, M.L., Batt, R., Brock, W.A., Cline, T., Coloso, J., Hodgson, J.R., Kitchell, J.F., Seekell, D.A., Smith, L., & Weidel, B., 2011. Early warnings of regime shifts: a whole-ecosystem experiment. Science 332(6033), 1079-1082. PMid:21527677. http://doi.org/10.1126/science.1203672.

Chang, N.B., Bai, K., & Chen, C.F., 2017. Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management. J. Environ. Manage. 201, 227-240. PMid:28667841. http://doi.org/10.1016/j.jenvman.2017.06.045.

Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., Li, Y., & Yuan, J., 2023. xgboost: Extreme Gradient Boosting. R package version 1.7.3.1. Retrieved in 2024, July 22, from https://CRAN.R-project.org/package=xgboost.

Fadum, J.M., & Hall, E.K., 2022. The interaction of physical structure and nutrient loading drives ecosystem change in a large tropical lake over 40 years. Sci. Total Environ. 830, 154454. PMid:35278553. http://doi.org/10.1016/j.scitotenv.2022.154454.

Fadum, J.M., Borton, M.A., Daly, R.A., Wrighton, K.C., & Hall, E.K., 2024. Dominant nitrogen metabolisms of a warm, seasonally anoxic freshwater ecosystem revealed using genome resolved metatranscriptomics. mSystems 9(2), e0105923. PMid:38259093. http://doi.org/10.1128/msystems.01059-23.

Fadum, J.M., Waters, M.N., & Hall, E.K., 2023. Trophic state resilience to hurricane disturbance of Lake Yojoa, Honduras. Sci. Rep. 13(1), 5681. PMid:37029170. http://doi.org/10.1038/s41598-023-32712-3.

Flores-Anderson, A.I., Griffin, R., Dix, M., Romero-Oliva, C.S., Ochaeta, G., Skinner-Alvarado, J., Ramirez Moran, M.V., Hernandez, B., Cherrington, E., Page, B., & Barreno, F., 2020. Hyperspectral satellite remote sensing of water quality in Lake Atitlán, Guatemala. Front. Environ. Sci. 8, 7. http://doi.org/10.3389/fenvs.2020.00007.

Gardner, J.R., Yang, X., Topp, S.N., Ross, M.R.V., Altenau, E.H., & Pavelsky, T.M., 2021. The color of rivers. Geophys. Res. Lett. 48(1), e202088946. http://doi.org/10.1029/2020GL088946.

Gilarranz, L.J., Narwania, A., Odermattb, R., Odermatt, S., & Dakos, V., 2022. Regime shifts, trends, and variability of lake productivity at a global scale. Proc. Natl. Acad. Sci. USA 119(35), e2116413119. PMid:35994657. http://doi.org/10.1073/pnas.2116413119.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R., 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18-27. http://doi.org/10.1016/j.rse.2017.06.031.

Hansen, C.H., Burian, S.J., Dennison, P.E., & Williams, G.P., 2020. Evaluating historical trends and influences of meteorological and seasonal climate conditions on lake chlorophyll a using remote sensing. Lake Reserv. Manage. 36(1), 45-63. http://doi.org/10.1080/10402381.2019.1632397.

Jones, J., 2019. Improved automated detection of subpixel-scale inundation-revised dynamic surface water extent (DSWE) Partial surface water tests. Remote Sens. (Basel) 11(4), 374. http://doi.org/10.3390/rs11040374.

Kloiber, S.M., Brezonik, P.L., Olmanson, L.G., & Bauer, M.E., 2002. A procedure for regional lake water clarity assessment using Landsat multispectral data. Remote Sens. Environ. 82(1), 38-47. http://doi.org/10.1016/S0034-4257(02)00022-6.

Knutson, T.R., McBride, J.L., Chan, J., Emanuel, K., Holland, G., Landsea, C., Held, I., Kossin, J.P., Srivastava, A.K., & Sugi, M., 2010. Tropical cyclones and climate change. Nat. Geosci. 3(3), 157-163. http://doi.org/10.1038/ngeo779.

Komárek, J., Zapomělová, E., Šmarda, J., Kopecký, J., Rejmánková, E., Woodhouse, J., Neilan, B.A., & Komárková, J., 2013. Polyphasic evaluation of Limnoraphis robusta, a water-bloom forming cyanobacterium from Lake Atitlán, Guatemala, with a description of Limnoraphis gen. nov. Fottea 13(1), 39-52. http://doi.org/10.5507/fot.2013.004.

Lucia Lobo, F., Nagel, G.W., Maciel, D.A., et al, 2021. AlgaeMAp: algae bloom monitoring application for inland waters in Latin America. Remote Sens. (Basel) 13(15), 2874. http://doi.org/10.3390/rs13152874.

Majozi, N.P., Salama, M.S., Bernard, S., Harper, D.M., & Habte, M.G., 2014. Remote sensing of euphotic depth in shallow tropical inland waters of Lake Naivasha using MERIS data. Remote Sens. Environ. 148, 178-189. http://doi.org/10.1016/j.rse.2014.03.025.

Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., & Lim, T.K., 2006. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3(1), 68-72. http://doi.org/10.1109/LGRS.2005.857030.

Melack, J.M., Hess, L.L., & Sippel, S., 2009. Remote sensing of lakes and floodplains in the Amazon basin. Remote Sens. Rev. 10(1-3), 127-142. http://doi.org/10.1080/02757259409532240.

Morris, S.S., Neidecker-Gonzales, O., Carletto, C., Munguía, M., Medina, J.M., & Wodon, Q., 2002. Hurricane mitch and the livelihoods of the rural poor in Honduras. World Dev. 30(1), 49-60. http://doi.org/10.1016/S0305-750X(01)00091-2.

Muñoz Sabater, J., 2019. ERA5-Land daily averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Retrieved in 2024, July 22, from https://essd.copernicus.org/articles/13/4349/2021/essd-13-4349-2021-assets.html.

Pahlevan, N., Smith, B., Schalles, J., Binding, C., Cao, Z., Ma, R., Alikas, K., Kangro, K., Gurlin, D., Hà, N., Matsushita, B., Moses, W., Greb, S., Lehmann, M.K., Ondrusek, M., Oppelt, N., & Stumpf, R., 2020. Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sens. Environ. 240, 111604. http://doi.org/10.1016/j.rse.2019.111604.

Pantoja, D.A., Vega-Álvarez, N.A., & Gasca-Ortiz, T., 2021. Trophic state in a tropical lake based on Chlorophyll-a profiler data and Sentinel-2 images: the onset of an algal bloom event. Water Environ. Res. 93(10), 2185. PMid:34018272. http://doi.org/10.1002/wer.1590.

Pompêo, M., Moschini-Carlos, V., Bitencourt, M.D., Sòria-Perpinyà, X., Vicente, E., & Delegido, J., 2021. Water quality assessment using Sentinel-2 imagery with estimates of chlorophyll a, Secchi disk depth, and Cyanobacteria cell number: The Cantareira System reservoirs (São Paulo, Brazil). Environ. Sci. Pollut. Res. Int. 28(26), 34990. PMid:33661492. http://doi.org/10.1007/s11356-021-12975-x.

R Core Team, 2023. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved in 2024, July 22, from https://www.R-project.org/.

Rejmánková, E., Komárek, J., Dix, M., Komárková, J., & Girón, N., 2011. Cyanobacterial blooms in Lake Atitlan, Guatemala. Limnologica 41(4), 296-302. http://doi.org/10.1016/j.limno.2010.12.003.

Sayyed, T.K., Ovienmhada, U., Kashani, M., Vohra, K., Kerr, G.H., O’Donnell, C., Harris, M.H., Gladson, L., Titus, A.R., Adamo, S.B., Fong, K.C., Gargulinski, E.M., Soja, A.J., Anenberg, S., & Kuwayama, Y., 2024. Satellite data for environmental justice: a scoping review of the literature in the United States. Environ. Res. Lett. 19(3), 033001. PMid:39377051. http://doi.org/10.1088/1748-9326/ad1fa4.

Sillen, S.J., Ross, M.R.V., & Collins, S.M., 2024. Long-Term trends in productivity across intermountain west lakes provide no evidence of widespread eutrophication. Water Resour. Res. 60(6), e202334997. http://doi.org/10.1029/2023WR034997.

Stanley, E.H., Collins, S.M., Lottig, N.R., Oliver, S.K., Webster, K.E., Cheruvelil, K.S., & Soranno, P.A., 2019. Biases in lake water quality sampling and implications for macroscale research. Limnol. Oceanogr. 64(4), 1572-1585. http://doi.org/10.1002/lno.11136.

Steele, B., Fadum, J., Hall, E., Willi, K., & Ross, M., 2023. Processing pipeline for estimating Secchi depth at Lake Yojoa, Honduras from remote sensing products. Zenodo. Version v1. https://doi.org/10.5281/zenodo.8139922.

Topp, S.N., Pavelsky, T.M., Dugan, H.A., Yang, X., Gardner, J., & Ross, M.R.V., 2021. Shifting patterns of summer lake color phenology in over 26,000 US lakes. Water Resour. Res. 57(5), eWR029123. PMid:34219822. http://doi.org/10.1029/2020WR029123.

Topp, S.N., Pavelsky, T.M., Jensen, D., Simard, M., & Ross, M.R.V., 2020. Research trends in the use of remote sensing for inland water quality science: moving towards multidisciplinary applications. Water 12(1), 169. http://doi.org/10.3390/w12010169.

U.S. Geological Survey, 2016. Landsat International Cooperators and Global Archive Consolidation. Reston: U.S. Geological Survey Fact Sheet.

Vermote, E., Justice, C., Claverie, M., & Franch, B., 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens. Environ. 185(2), 46-56. PMid:32020955. http://doi.org/10.1016/j.rse.2016.04.008.

Watanabe, F., Alcântara, E., Bernardo, N., de Andrade, C., Gomes, A.C., do Carmo, A., Rodrigues, T., & Rotta, L.H., 2019. Mapping the chlorophyll-a horizontal gradient in a cascading reservoirs system using MSI Sentinel-2A images. Adv. Space Res. 64(3), 3. http://doi.org/10.1016/j.asr.2019.04.035.

Watanabe, F., Alcantara, E., Rodrigues, T., Rotta, L., Bernardo, N., & Imai, N., 2018. Remote sensing of the chlorophyll-a based on OLI/Landsat-8 and MSI/Sentinel-2A (Barra Bonita reservoir, Brazil). An. Acad. Bras. Cienc. 90(2, Suppl.1), 1987-2000. PMid:28876398. http://doi.org/10.1590/0001-3765201720170125.

Watanabe, F.S.Y., Alcântara, E., Rodrigues, T.W.P., Imai, N.N., Barbosa, C.C.F., & Silva Rotta, L.H., 2015. Estimation of chlorophyll-a concentration and the trophic State of the Barra Bonita Hydroelectric reservoir using OLI/Landsat-8 images. Int. J. Environ. Res. Public Health 12(9), 10391. PMid:26322489. http://doi.org/10.3390/ijerph120910391.

Weigand, M., Wurm, M., Dech, S., & Taubenböck, H., 2019. Remote sensing in environmental justice research: a review. ISPRS Int. J. Geoinf. 8(1), 1. http://doi.org/10.3390/ijgi8010020.

Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., & Roy, D.P., 2016. The global Landsat archive: status, consolidation, and direction. Remote Sens. Environ. 185, 271-283. http://doi.org/10.1016/j.rse.2015.11.032.

Yang, X., O’Reilly, C.M., Gardner, J.R., Ross, M.R.V., Topp, S.N., Wang, J., & Pavelsky, T.M., 2022. The color of earth’s lakes. Geophys. Res. Lett. 49(18), e20298925. http://doi.org/10.1029/2022GL098925.
 

Submetido em:
22/07/2024

Aceito em:
02/12/2024

Publicado em:
03/02/2025

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