DEVELOPING A MACHINE LEARNING FRAMEWORK FOR ESTIMATING SOIL MOISTURE WITH VNIR HYPERSPECTRAL DATA

7 citations as recorded by crossref.

1. 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
2. Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models S. Wang et al. https://doi.org/10.1002/ldr.4505
3. Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data S. Keller et al. https://doi.org/10.3390/ijgi9110638
4. Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data F. Riese et al. https://doi.org/10.3390/rs12010007
5. Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll a, Diatoms, Green Algae and Turbidity S. Keller et al. https://doi.org/10.3390/ijerph15091881
6. Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data H. Adab et al. https://doi.org/10.3390/w12113223
7. Dynamic monitoring of surface soil moisture fluctuations using synthetic aperture radar and data-driven algorithms H. Rajeev et al. https://doi.org/10.1007/s12518-024-00606-2