New algorithm to estimate chlorophyll-a concentration from the spectral reflectance of inland water

1998 ◽  
Author(s):  
Xiaozhou Shu ◽  
Dingbo Kuang
Keyword(s):  
Chlorophyll A ◽  
Spectral Reflectance ◽  
Chlorophyll A Concentration ◽  
Inland Water

Relationship between spectral reflectance and chlorophyll-a concentration in the eutrophic Lake Togo-ike

2014 ◽  
Author(s):  
Y. Sakuno ◽  
K. Hatakeyama ◽  
Y. Miyamoto ◽  
A. Hatsuda ◽  
A. Mori ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Spectral Reflectance ◽  
Eutrophic Lake ◽  
Chlorophyll A Concentration

scholarly journals Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies

2021 ◽  
Vol 13 (4) ◽  
pp. 718
Author(s):  
Philipp M. Maier ◽  
Sina Keller ◽  
Stefan Hinz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Chlorophyll A ◽  
Real World ◽  
Water Bodies ◽  
Simulation Data ◽  
Chlorophyll A Concentration ◽  
Inland Water ◽  
The Real ◽  
Inland Water Bodies

Information about the chlorophyll a concentration of inland water bodies is essential for water monitoring. This study focuses on estimating chlorophyll a with remote sensing data, and machine learning (ML) approaches on the real-world SpecWa dataset. We adapt and apply a one-dimensional convolutional neural network (1D CNN) as a deep learning architecture for the first time to address this estimation. Since such a DL approach requires a large amount of data for its training, we rely on simulation data generated by the Water Color Simulator (WASI). This simulation is prepared accordingly and includes a knowledge-based water composition with two origins of the chlorophyll a concentration. Therefore, the training data is independent of the real-world SpecWa dataset, which is challenging for any ML approach. We define two spectral downsampling approaches as a pre-processing step, representing the hyperspectral EnMAP satellite mission (SR-EnMAP) and the multispectral Sentinel-2 mission (SR-Sentinel). Subsequently, we train a Random Forest, an artificial neural network, a band-ratio approach, and the 1D CNN on the WASI-generated simulation training dataset. Finally, all ML models are evaluated on the real SpecWa dataset. For both downsampled data, the 1D CNN outperforms the other ML models. On the finer resolved SR-EnMAP data it achieves an R2=81.9%, RMSE=12.4 μg L−1, and MAE=6.7 μg L−1. Besides, the 1D CNN’s performance decreases on the SR-Sentinel data to R2=62.4%. When focusing on the individual water bodies of the SpecWa dataset, the most significant differences exist between natural and artificial water bodies. We discover that the applied models estimate the chlorophyll a concentration of most natural water bodies satisfyingly. In sum, the newly DL approach can estimate the chlorophyll a values of unknown inland water bodies successfully, although it is trained on an entire simulation dataset.

scholarly journals The Variation on Sediment Reworking with Influencing Factors by a Sand Bubbler Crab, Scopimera globosa, in Intertidal Sediments of the Anmyeon Island, Korea

2021 ◽  
Vol 13 (10) ◽  
pp. 5703
Author(s):  
Jaehwan Seo ◽  
Bon Joo Koo
Keyword(s):  
Chlorophyll A ◽  
Influencing Factors ◽  
Surface Sediment ◽  
Intertidal Sediments ◽  
Daily Production ◽  
Chlorophyll A Concentration ◽  
Sediment Reworking ◽  
Pellet Production ◽  
Crab Density ◽  
Annual Sediment

Though biological and ecological characteristics of Scopimera globosa have been intensively investigated, little has been understood on bioturbation, especially sediment reworking. This study was designed to evaluate variation on sediment reworking of S. globosa based on feeding pellet production (FP) and burrowing pellet production (BP) with influencing factors and estimating the chlorophyll content reduction within the surface sediment by its feeding. The FP and BP largely fluctuated according to chlorophyll a concentration and crab density, but both were not influenced by temperature. The FP was enhanced by chlorophyll a concentration, whereas both FP and BP were restricted by crab density. The daily individual production was highest in spring, followed by fall and summer, with values of 25.61, 20.70 and 3.90 g ind.−1 d−1, respectively, while the total daily production was highest in fall, followed by summer and spring 2150, 1660 and 660 g m−2 d−1, respectively. The daily sediment reworking based on the FP and BP of Scopimera was highest in fall, followed by summer and spring, with values of 1.91, 1.70 and 0.77 mm d-1 and the annual sediment reworking rate of this species was calculated 40 cm year−1 based on its density in this study area. The chlorophyll a reduction ratio was estimated from 11 to 24% in one day by its feeding. These results imply that the sediment reworking of S. globosa is regulated by food abundance and its density, and Scopimera is an important bioturbator, greatly influencing biogeochemical changes in the intertidal sediments.

scholarly journals Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images

2021 ◽  
Vol 18 (9) ◽  
pp. 4419
Author(s):  
Yuequn Lai ◽  
Jing Zhang ◽  
Yongyu Song ◽  
Zhaoning Gong
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Phosphorus Concentration ◽  
Landsat 8 ◽  
Supply Function ◽  
Estimation Model ◽  
Nitrogen And Phosphorus ◽  
Chlorophyll A Concentration ◽  
Guanting Reservoir

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other.

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