Articles | Volume 10, issue 3
https://doi.org/10.5194/essd-10-1527-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-10-1527-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes
Odermatt & Brockmann GmbH, Zurich, 8005, Switzerland
Brockmann Consult GmbH, Geesthacht, 20502, Germany
Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 8600, Switzerland
Olaf Danne
Brockmann Consult GmbH, Geesthacht, 20502, Germany
Petra Philipson
Brockmann Geomatics Sweden AB, Kista, 164 40, Sweden
Carsten Brockmann
Brockmann Consult GmbH, Geesthacht, 20502, Germany
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- Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms M. Warren et al. 10.1016/j.rse.2021.112651
- Dive Into the Unknown: Embracing Uncertainty to Advance Aquatic Remote Sensing M. Werther & O. Burggraaff 10.34133/remotesensing.0070
- The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate Y. Huot et al. 10.1016/j.scitotenv.2019.133668
- Analyzing short term spatial and temporal dynamics of water presence at a basin-scale in Mexico using SAR data A. López-Caloca et al. 10.1080/15481603.2020.1840106
- Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach N. Pahlevan et al. 10.1016/j.rse.2019.111604
- The impact of water quality on GDP growth: Evidence from around the world J. Russ et al. 10.1016/j.wasec.2022.100130
- Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications J. Wang et al. 10.3390/agronomy14091975
- From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
- Development of an algal bloom satellite and in situ metadata hub with case studies in Canada D. Beaulne & G. Fotopoulos 10.1016/j.ecoinf.2023.102447
- A database of chlorophyll and water chemistry in freshwater lakes A. Filazzola et al. 10.1038/s41597-020-00648-2
- A Bayesian approach for remote sensing of chlorophyll-a and associated retrieval uncertainty in oligotrophic and mesotrophic lakes M. Werther et al. 10.1016/j.rse.2022.113295
- Spatiotemporal Variability of the Lake Tana Water Quality Derived from the MODIS-Based Forel–Ule Index: The Roles of Hydrometeorological and Surface Processes N. Abegaz et al. 10.3390/atmos14020289
- Research of chlorophyll-a concentration inversion at different depths in Hong Kong offshore waters based on gaussian process regression J. Zhang et al. 10.1088/1755-1315/1087/1/012034
- A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes D. Jiang et al. 10.1016/j.jag.2023.103188
- Using a Remote-Sensing-Based Piecewise Retrieval Algorithm to Map Chlorophyll-a Concentration in a Highland River System Y. Ma et al. 10.3390/rs14236119
- Water Optical Property of High-Altitude Lakes in the Tibetan Plateau W. Shi & M. Wang 10.1109/TGRS.2021.3065637
- AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters M. Ross et al. 10.1029/2019WR024883
- Evaluating Satellite-Based Water Quality Sensing of Inland Waters on Basis of 100+ German Water Bodies Using 2 Different Processing Chains S. Schmidt et al. 10.3390/rs16183416
- Regime shifts, trends, and variability of lake productivity at a global scale L. Gilarranz et al. 10.1073/pnas.2116413119
- An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data A. Vundo et al. 10.3390/rs11030279
- Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data T. Schröder et al. 10.3390/rs16152798
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- Remote sensing of chlorophyll-a concentrations in coastal oceans of the Greater Bay Area in China: Algorithm development and long-term changes Y. Tong et al. 10.1016/j.jag.2022.102922
- Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes B. Schaeffer et al. 10.1007/s10661-021-09684-w
- Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects J. Wang et al. 10.3390/agronomy14091920
Latest update: 14 Dec 2024
Short summary
The Diversity II inland water database consists of remotely sensed water quality information for more than 300 lakes in the whole world. It was derived from optical and thermal imagery acquired by the ESA ENVISAT satellite between 2002 and 2012. The database consists of spatially resolved monthly, yearly and 9-year averages for 10 geophysical parameters. Its practical usage is demonstrated by means of several case studies on lake-specific processes and regime shifts.
The Diversity II inland water database consists of remotely sensed water quality information for...
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