The effects of small water bodies on the atmospheric heat and water budgets over the MacKenzie River Basin

2004 ◽  
Vol 18 (5) ◽  
pp. 913-938 ◽  
Author(s):  
Badrinath Nagarajan ◽  
M. K. Yau ◽  
P. H. Schuepp
Keyword(s):  
River Basin ◽  
Water Bodies ◽  
Water Budgets ◽  
Small Water ◽  
Mackenzie River Basin ◽  
Atmospheric Heat ◽  
Small Water Bodies ◽  
Mackenzie River

The impact of biometric parameters of a hydromacrophyte habitat on the structure of zooplankton communities in various types of small water bodies

2009 ◽  
Vol 38 (2) ◽  
Author(s):  
Natalia Kuczyńska-Kippen ◽  
Barbara Nagengast ◽  
Tomasz Joniak
Keyword(s):  
Water Bodies ◽  
Small Water ◽  
Biometric Parameters ◽  
Zooplankton Communities ◽  
The Impact ◽  
Small Water Bodies

The impact of biometric parameters of a hydromacrophyte habitat on the structure of zooplankton communities in various types of small water bodies

Exploring the potential of small water bodies as an integrative management tool for fisheries production

2021 ◽  
Author(s):  
Christopher Mulanda Aura ◽  
Ruth Lewo Mwarabu ◽  
Chrisphine S. Nyamweya ◽  
Horace Owiti ◽  
Collins Onyango Ongore ◽  
...  
Keyword(s):  
Water Bodies ◽  
Management Tool ◽  
Small Water ◽  
Small Water Bodies ◽  
Integrative Management

Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin

2013 ◽  
Vol 12 (3) ◽  
pp. vzj2012.0134 ◽  
Author(s):  
Naira Chaouch ◽  
Robert Leconte ◽  
Ramata Magagi ◽  
Marouane Temimi ◽  
Reza Khanbilvardi
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Passive Microwave ◽  
Inversion Method ◽  
Microwave Measurements ◽  
Multi Stage ◽  
Mackenzie River Basin ◽  
Mackenzie River

scholarly journals Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

2022 ◽  
Vol 14 (1) ◽  
pp. 229
Author(s):  
Jiarui Shi ◽  
Qian Shen ◽  
Yue Yao ◽  
Junsheng Li ◽  
Fu Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Water Bodies ◽  
Gradient Boosting ◽  
Learning Models ◽  
Small Water ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models ◽  
Sentinel 2 ◽  
Small Water Bodies

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

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