Keyphrases
Soil Moisture Monitoring
100%
Sentinel-2
100%
CycleGAN
100%
Soil Moisture Content
60%
Proposed Methodology
40%
South Australia
20%
Time Monitoring
20%
Water Management
20%
Satellite Images
20%
Time Series Data
20%
Crop Growth
20%
Existing Data
20%
Machine Learning Models
20%
Sentinel-1
20%
Random Forest Algorithm
20%
Freely Available
20%
Sentinel-2 Images
20%
Synthetic Aperture Radar Image
20%
Overfitting
20%
Earth Observation Data
20%
Autoencoder
20%
Earth Observation
20%
State-of-the-art Techniques
20%
Linear Regression Model
20%
Crop Yield
20%
Vineyard
20%
Cycle-consistent Adversarial Network
20%
Machine Learning System
20%
Optical Aperture
20%
Feature Translation
20%
Translation-based
20%
Latent Feature
20%
Growth Yield
20%
Low-dimensional Representation
20%
Eden
20%
Latent Representation
20%
Simple Machine
20%
Moisture Mapping
20%
Growth Quality
20%
Crop Quality
20%
Sentinel-2 Time Series
20%
Simultaneous Prediction
20%
Gap Filling
20%
Efficient Framework
20%
Earth and Planetary Sciences
Time Series
100%
Soil Moisture
100%
Sentinel-2
100%
Machine Learning
50%
Synthetic Aperture Radar
25%
Satellite Image
25%
Crop Growth
25%
Water Resources Development
25%
State of the Art
25%
Real Time
25%
Crop Yield
25%
Vineyard
25%
Computer Science
Machine Learning
100%
Learning System
100%
Random Decision Forest
50%
Synthetic Aperture Radar Images
50%
Time Series Data
50%
Autoencoder
50%
Time Monitoring
50%
Satellite Image
50%
Engineering
Sentinel-2
100%
Soil Moisture Content
75%
Earth Observation
50%
Learning System
50%
Random Forest
25%
Data Series
25%
State-of-the-Art Method
25%
Synthetic Aperture Radar Images
25%
Autoencoder
25%
Water Resource
25%