Monitoring chlorophyll a concentration in the Mar Menor coastal lagoon using ocean color sensors

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-03-25 DOI:10.1016/j.rsase.2025.101531

Francisco Gómez-Jakobsen , José G. Giménez , José M. Cecilia , Isabel Ferrera , Lidia Yebra , Eugenio Fraile-Nuez , Marijn Oosterbaan , Pedro Martínez-Martínez , Víctor Orenes-Salazar , Virginia Sandoval-Cánovas , Antonio Ortolano-Muñoz , Rocío García-Muñoz , Patricia Pérez-Tórtola , Juan M. Ruíz , Jesús M. Mercado

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引用次数: 0

Abstract

The Mar Menor coastal lagoon has experienced a severe eutrophication process since 2015, with its most visible effect being a series of phytoplankton blooms featured by unprecedentedly high and persistent concentrations of chlorophyll a in the water column. To better quantify and monitor these changes, two suitable chlorophyll concentration algorithms (referred to as BELA, short for BELich Algorithm) for the Mar Menor lagoon are proposed. These algorithms are derived from the analysis of thousands of reflectance spectra obtained from six satellite ocean color sensors, combined with in situ observations performed between 2016 and 2023. Our results demonstrate that the BELA algorithms can accurately estimate chlorophyll a concentration in the shallow waters of the Mar Menor lagoon for values above 2 mg m⁻³, a range consistent with various of the episodes of high productivity observed since 2015. These algorithms perform well for a variety of currently operational ocean color sensors and could be used in future planned missions. This versatility makes them a valuable tool for monitoring and assessing the environmental state of the lagoon.

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利用海洋颜色传感器监测Mar Menor沿海泻湖的叶绿素a浓度

自2015年以来，Mar Menor沿海泻湖经历了严重的富营养化过程，其最明显的影响是一系列浮游植物大量繁殖，其特征是水体中叶绿素a的浓度前所未有地高且持续。为了更好地量化和监测这些变化，提出了两种适合Mar Menor泻湖的叶绿素浓度算法（称为BELA，简称BELich算法）。这些算法是基于对六颗卫星海洋颜色传感器获得的数千个反射光谱的分析，并结合2016年至2023年期间进行的现场观测得出的。我们的研究结果表明，BELA算法可以准确地估计Mar Menor泻湖浅水叶绿素a浓度，其值高于2 mg m - 3，这一范围与自2015年以来观测到的各种高生产力事件一致。这些算法在目前使用的各种海洋颜色传感器中表现良好，可以在未来计划的任务中使用。这种多功能性使它们成为监测和评估泻湖环境状况的宝贵工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems