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