FOREST AREA DERIVATION FROM SENTINEL-1 DATA

14 citations as recorded by crossref.

1. Green Energy Sources Assessment Using Sentinel-1 Satellite Remote Sensing M. Majidi Nezhad et al. https://doi.org/10.3389/fenrg.2021.649305
2. Annual seasonality in Sentinel-1 signal for forest mapping and forest type classification A. Dostálová et al. https://doi.org/10.1080/01431161.2018.1479788
3. A Review of Onboard AI Technologies for Synthetic Aperture Radar: Techniques, challenges, and opportunities P. Sur et al. https://doi.org/10.1109/MGRS.2026.3689429
4. Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests X. Chen & Y. Sun https://doi.org/10.1007/s11676-024-01700-2
5. Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017 E. Makinde & E. Oyelade https://doi.org/10.1007/s11356-019-05589-x
6. Multi-sensor modelling of woody vegetation and canopy cover across natural and modified ecosystems S. Stewart et al. https://doi.org/10.1016/j.jag.2025.104635
7. The Effect of Forest Mask Quality in the Wall-to-Wall Estimation of Growing Stock Volume E. Vangi et al. https://doi.org/10.3390/rs13051038
8. Vegetation scenario of Indian part of Ganga Delta: a change analysis using Sentinel-1 time series data on Google earth engine platform B. Halder & J. Bandyopadhyay https://doi.org/10.1007/s42797-021-00041-1
9. Object-based mapping and classification features for tropical highlands using on Sentinel-1, Sentinel-2, and GEDI canopy height data - A case study of the Muringato catchment, Kenya M. Goebel et al. https://doi.org/10.3112/erdkunde.2023.01.03
10. Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar J. Hansen et al. https://doi.org/10.3390/rs12111899
11. Forest Cover Mapping Based on a Combination of Aerial Images and Sentinel-2 Satellite Data Compared to National Forest Inventory Data S. Ganz et al. https://doi.org/10.3390/f11121322
12. Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data I. Borlaf-Mena et al. https://doi.org/10.1016/j.rse.2023.113728
13. The deadliest tropical cyclone ‘Amphan’: investigate the natural flood inundation over south 24 Parganas using google earth engine B. Halder et al. https://doi.org/10.1007/s42797-021-00035-z
14. Assessment of Deep Learning Based Image Segmentation for Identifying Floating Net Cages from Very High-Resolution Capella Synthetic Aperture Radar (SAR) Data S. Arjasakusuma & S. Kusuma https://doi.org/10.1007/s12524-024-02022-w