CHARACTERIZATION OF LAND COVER SEASONALITY IN SENTINEL-1 TIME SERIES DATA

14 citations as recorded by crossref.

1. An Unsupervised Clustering Technique for Dual-Pol Sentinel-1 SLC and GRD SAR Data A. Verma et al.
2. Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay G. Alciaturi et al.
3. Accounting for Deciduous Forest Structure and Viewing Geometry Effects Improves Sentinel-1 Time Series Image Consistency M. Zehner et al.
4. Characterizing Spatial Variability of Soil Organic Carbon Through Improved Machine-Learning Modeling With In Situ Data Resampling: A Case Study in Alaska W. Peng et al.
5. Potential of Sentinel-1 SAR to Assess Damage in Drought-Affected Temperate Deciduous Broadleaf Forests K. Schellenberg et al.
6. Characterizing Forest Species Seasonality Using Time Series of C-Band Sentinel-1 Backscattering Coefficient: A Case Study in the Spanish Central Range Z. Liu et al.
7. European Wide Forest Classification Based on Sentinel-1 Data A. Dostálová et al.
8. Mapping spatiotemporal mortality patterns in spruce mountain forests using Sentinel-2 data and environmental factors M. Kluczek & B. Zagajewski
9. Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests M. Mueller et al.
10. Sensitivity of Sentinel-1 Backscatter to Management-Related Disturbances in Temperate Forests S. van der Woude et al.
11. Vast, overlooked peat, and organic soils in Brazil's Cerrado: carbon storage, dynamics, and stability L. Verona et al.
12. Exploring the Relationship Between Time Series of Sentinel-1 Interferometric Coherence Data and Wild Edible Mushroom Yields in Mediterranean Forests R. Martínez-Rodrigo et al.
13. Spectral-temporal traits in Sentinel-1 C-band SAR and Sentinel-2 multispectral remote sensing time series for 61 tree species in Central Europe C. Schulz et al.
14. Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data I. Borlaf-Mena et al.