Acta Limnologica Brasiliensia
https://actalb.org/article/doi/10.1590/S2179-975X6924
Acta Limnologica Brasiliensia
Thematic Section: Methods

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

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Submitted date:
22/07/2024

Accepted date:
02/12/2024

Publication date:
03/02/2025

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