A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning
Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Earth observation data ubiquity, attempts have been made to accurately detect crop...
Saved in:
| Main Authors: | Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000331 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine Learning-Based Alfalfa Height Estimation Using Sentinel-2 Multispectral Imagery
by: Hazhir Bahrami, et al.
Published: (2025-05-01) -
Integration of Sentinel-1 and Sentinel-2 for temporal identification of aquacultural ponds
by: Vaishnavi Joshi, et al.
Published: (2025-12-01) -
The potential of Sentinel-1 time series for large-scale assessment of maize and wheat phenology across Germany
by: Laura Flores, et al.
Published: (2025-12-01) -
Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan
by: Arslan Berdyyev, et al.
Published: (2024-12-01) -
DACIA5: a Sentinel-1 and Sentinel-2 dataset for agricultural crop identification applications
by: A. Băicoianu, et al.
Published: (2025-06-01)