ESTIMATING CHLOROPHYLL A CONCENTRATIONS OF SEVERAL INLAND WATERS WITH HYPERSPECTRAL DATA AND MACHINE LEARNING MODELS

10 citations as recorded by crossref.

1. Comparison of Machine Learning Models to Predict Lake Area in an Arid Area D. Wang et al. https://doi.org/10.3390/rs15174153
2. A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning L. Silveira Kupssinskü et al. https://doi.org/10.3390/s20072125
3. Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery J. Amieva et al. https://doi.org/10.3390/rs15225385
4. Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks D. Grendaitė & L. Petkevičius https://doi.org/10.1109/ACCESS.2024.3366491
5. Chromaticity-Based Discrimination of Algal Bloom from Inland and Coastal Waters Using In Situ Hyperspectral Remote Sensing Reflectance D. Zhao et al. https://doi.org/10.3390/w16162276
6. Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data D. Grendaitė & E. Stonevičius https://doi.org/10.3390/w14111732
7. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies P. Maier et al. https://doi.org/10.3390/rs13040718
8. Self-attention and generative adversarial networks for algae monitoring N. Huynh et al. https://doi.org/10.1080/22797254.2021.2010605
9. 3LATNet: Attention based deep learning model for global Chlorophyll-a retrieval from GCOM-C satellite M. Salah et al. https://doi.org/10.1016/j.isprsjprs.2024.12.019
10. Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water G. Kim et al. https://doi.org/10.3390/rs12132070