دورية أكاديمية
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
| العنوان: | Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake |
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| المؤلفون: | Rodríguez-López, Lien, Alvarez, Denisse, Bustos Usta, David, Duran-Llacer, Iongel, Bravo Alvarez, Lisandra, Fagel, Nathalie, Bourrel, Luc, Frappart, Frederic, Urrutia, Roberto |
| المساهمون: | Geology - ULiège, BE |
| المصدر: | Remote Sensing, 16 (4), 647 (2024-02) |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute (MDPI), 2024. |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | chlorophyll-a at depth, lake, machine learning, remote sensing, Chlorophyll a, Chlorophyll-a at depth, Chlorophyll-a concentration, Detection algorithm, Field data, Freshwater ecosystem, Machine-learning, Remote sensing data, Remote-sensing, Statistic modeling, Earth and Planetary Sciences (all), Physical, chemical, mathematical & earth Sciences, Earth sciences & physical geography, Physique, chimie, mathématiques & sciences de la terre, Sciences de la terre & géographie physique |
| الوصف: | 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. Proyecto Interuniversitario de Iniciación en Investigación Asociativa (Chile) |
| نوع الوثيقة: | journal article http://purl.org/coar/resource_type/c_6501 article peer reviewed |
| اللغة: | English |
| Relation: | https://www.mdpi.com/2072-4292/16/4/647/pdf; urn:issn:2072-4292 |
| DOI: | 10.3390/rs16040647 |
| URL الوصول: | https://orbi.uliege.be/handle/2268/319975 |
| حقوق: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| رقم الأكسشن: | edsorb.319975 |
| قاعدة البيانات: | ORBi |
| DOI: | 10.3390/rs16040647 |
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