In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region

Coelho, Christine; Heim, Birgit; Förster, Saskia; Brosinsky, Arlena; Araújo, José Carlos de

doi:10.20944/preprints201711.0075.v1

2017

DOI: 10.20944/preprints201711.0075.v1

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In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region

Christine Coelho¹,

Birgit Heim²,

Saskia Förster³

et al.

Abstract: Abstract:We analyzed Chlorophyll-a and CDOM dynamics from field measurements and 17 assessed the potential of multispectral satellite data for retrieving water-quality parameters in 27CDOM-moderate and highly eutrophic reservoir, whereas the non-perennial ones were assigned 28 as CDOM-rich and oligotrophic-dystrophic reservoirs. The remote sensing initiative was partially 29 successful: the Chl-a was best modelled using RapidEye for the perennial; whereas CDOM 30 performed best with Landsat-8 for non-perennia… Show more

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Cited by 13 publications

References 53 publications

(116 reference statements)

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Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

et al. 2023

Drones

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Water quality monitoring of medium-sized inland water is important for water environment protection given the large number of small-to-medium size water bodies in China. A case study was conducted on Yuandang Lake in the Yangtze Delta region, with a surface area of 13 km2. This study proposed utilising a multispectral uncrewed aerial vehicle (UAV) to collect large-scale data and retrieve multiple water quality parameters using machine learning algorithms. An alternate processing method is proposed to process large and repetitive lake surface images for mapping the water quality data to the image. Machine learning regression methods (Random Forest, Gradient Boosting, Backpropagation Neural Network, and Convolutional Neural Network) were used to construct separate water quality inversion models for ten water parameters. The results showed that several water quality parameters (CODMn, temperature, pH, DO, and NC) can be retrieved with reasonable accuracy (R2 = 0.77, 0.75, 0.73, 0.67, and 0.64, respectively), although others (NH3-N, BGA, TP, Turbidity, and Chl-a) have a determination coefficient (R2) less than 0.6. This work demonstrated the tremendous potential of employing multispectral data in conjunction with machine learning algorithms to retrieve multiple water quality parameters for monitoring medium-sized bodies of water.

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Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

et al. 2023

Drones

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Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

Irie,

Manabe,

Yamashita

2024

Drones

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The observation of the phytoplankton distribution with a high spatiotemporal resolution is necessary to track the nutrient sources that cause algal blooms and to understand their behavior in response to hydraulic phenomena. Photography from UAVs, which has an excellent temporal and spatial resolution, is an effective method to obtain water quality information comprehensively. In this study, we attempted to develop a method for estimating the chlorophyll concentration from aerial images using machine learning that considers brightness correction based on insolation and the spatial distribution of turbidity evaluated by satellite image analysis. The reflectance of harmful algae bloom (HAB) was different from that of phytoplankton seen under normal conditions; so, the images containing HAB were the causes of error in the estimation of the chlorophyll concentration. First, the images when the bloom occurred were extracted by the discrimination with machine learning. Then, the other images were used for the regression of the concentration. Finally, the coefficient of determination between the estimated chlorophyll concentration when no bloom occurred by the image analysis and the observed value reached 0.84. The proposed method enables the detailed depiction of the spatial distribution of the chlorophyll concentration, which contributes to the improvement in water quality management in reservoirs.

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Entropy-Based Temporal Downscaling of Precipitation as Tool for Sediment Delivery Ratio Assessment

Alencar

Paton

Araújo

2021

Entropy

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Many regions around the globe are subjected to precipitation-data scarcity that often hinders the capacity of hydrological modeling. The entropy theory and the principle of maximum entropy can help hydrologists to extract useful information from the scarce data available. In this work, we propose a new method to assess sub-daily precipitation features such as duration and intensity based on daily precipitation using the principle of maximum entropy. Particularly in arid and semiarid regions, such sub-daily features are of central importance for modeling sediment transport and deposition. The obtained features were used as input to the SYPoME model (sediment yield using the principle of maximum entropy). The combined method was implemented in seven catchments in Northeast Brazil with drainage areas ranging from 10−3 to 10+2 km2 in assessing sediment yield and delivery ratio. The results show significant improvement when compared with conventional deterministic modeling, with Nash–Sutcliffe efficiency (NSE) of 0.96 and absolute error of 21% for our method against NSE of −4.49 and absolute error of 105% for the deterministic approach.

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In Situ and Satellite Observation of CDOM and Chlorophyll-a Dynamics in Small Water Surface Reservoirs in the Brazilian Semiarid Region

Cited by 13 publications

References 53 publications

Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

Medium-Sized Lake Water Quality Parameters Retrieval Using Multispectral UAV Image and Machine Learning Algorithms: A Case Study of the Yuandang Lake, China

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

Entropy-Based Temporal Downscaling of Precipitation as Tool for Sediment Delivery Ratio Assessment

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