24 citations as recorded by crossref.

1. AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters M. Ross et al. 10.1029/2019WR024883
2. A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges H. Yang et al. 10.3390/rs14081770
3. 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
4. Dive Into the Unknown: Embracing Uncertainty to Advance Aquatic Remote Sensing M. Werther & O. Burggraaff 10.34133/remotesensing.0070
5. 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
6. 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
7. 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
8. Regime shifts, trends, and variability of lake productivity at a global scale L. Gilarranz et al. 10.1073/pnas.2116413119
9. The impact of water quality on GDP growth: Evidence from around the world J. Russ et al. 10.1016/j.wasec.2022.100130
10. An Overall Evaluation of Water Transparency in Lake Malawi from MERIS Data A. Vundo et al. 10.3390/rs11030279
11. From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
12. A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data J. Wang & X. Chen 10.1016/j.scitotenv.2023.167631
13. 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
14. A database of chlorophyll and water chemistry in freshwater lakes A. Filazzola et al. 10.1038/s41597-020-00648-2
15. A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing H. Guo et al. 10.1016/j.envpol.2021.117734
16. 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
17. Supervised Classifications of Optical Water Types in Spanish Inland Waters M. Pereira-Sandoval et al. 10.3390/rs14215568
18. 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
19. 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
20. 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
21. 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
22. 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
23. Water Optical Property of High-Altitude Lakes in the Tibetan Plateau W. Shi & M. Wang 10.1109/TGRS.2021.3065637
24. 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