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

9 citations as recorded by crossref.

1. Comparison of Machine Learning Models to Predict Lake Area in an Arid Area D. Wang et al. 10.3390/rs15174153
2. A Method for Chlorophyll-a and Suspended Solids Prediction through Remote Sensing and Machine Learning L. Silveira Kupssinskü et al. 10.3390/s20072125
3. Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery J. Amieva et al. 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 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. 10.3390/w16162276
6. Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data D. Grendaitė & E. Stonevičius 10.3390/w14111732
7. Deep Learning with WASI Simulation Data for Estimating Chlorophyll a Concentration of Inland Water Bodies P. Maier et al. 10.3390/rs13040718
8. Self-attention and generative adversarial networks for algae monitoring N. Huynh et al. 10.1080/22797254.2021.2010605
9. Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water G. Kim et al. 10.3390/rs12132070