34 citations as recorded by crossref.

1. Wall-to-wall mapping of Fagus orientalis Lipsky distribution across Türkiye’s forests using satellite remote sensing H. Okurlar et al. https://doi.org/10.1080/01431161.2026.2707265
2. Mountain pine time series mapping with Sentinel-2 and assessing their ecological role in Alpine vegetation dynamics B. Tufail et al. https://doi.org/10.1016/j.rsase.2026.102238
3. Advancing optical earth observation for EU policies: needs, opportunities, recommendations K. Berger et al. https://doi.org/10.1186/s12302-026-01346-3
4. Mapping Nine Dominant Tree Species in the Korean Peninsula Using U-Net and Harmonic Analysis of Sentinel-2 Imagery J. Lee et al. https://doi.org/10.7780/kjrs.2025.41.2.1.1
5. What can Sentinel-2 time series tell us about the phenology of dominant deciduous tree species in Poland? E. Grabska et al. https://doi.org/10.1093/forestry/cpaf064
6. Forest practitioners’ requirements for remote sensing-based canopy height, wood-volume, tree species, and disturbance products F. Fassnacht et al. https://doi.org/10.1093/forestry/cpae021
7. Sawdust-Assisted Phytoremediation: Boosting White Mustard (Sinapis alba) Growth and Cadmium Uptake from Contaminated Soil D. Kramski et al. https://doi.org/10.1021/acsomega.5c04779
8. Mapping and characterizing riparian forests in headwater streams in the Carpathians Mountains of Europe E. Grabska & W. Keeton https://doi.org/10.1016/j.ecolind.2026.114941
9. Mapping forest tree species and their uncertainty using Earth observation and National Forest Inventory data: towards operational monitoring in Sweden A. Abdi & F. Wang https://doi.org/10.1080/01431161.2026.2625513
10. Tree species from space: a new product for Germany based on Sentinel-1 and -2 time series M. Wegler et al. https://doi.org/10.1080/01431161.2025.2530236
11. Mapping Coastal Redwoods ( Sequoia sempervirens ) across their natural range: an updateable and field-validated distribution map using Sentinel satellite data and cloud computing S. Banerjee et al. https://doi.org/10.1080/15481603.2026.2613558
12. Hybrid Machine Learning Models for Predicting Gross CO2e Balance in Polish Forest Stands: A Tool for Sustainable Forest Carbon Assessment in the Circular Economy K. Przybył et al. https://doi.org/10.3390/su18126366
13. Unveiling spatiotemporal forest cover patterns breaking the cloud barrier: Annual 30  m mapping in cloud-prone southern China from 2000 to 2020 P. Qin et al. https://doi.org/10.1016/j.isprsjprs.2026.01.015
14. Map of forest tree species for Poland based on Sentinel-2 data E. Grabska-Szwagrzyk et al. https://doi.org/10.5194/essd-16-2877-2024
15. Geometry-Constrained Reference Sample Construction from Forest Inventory Compartments for Dominant Tree Species Mapping P. Zheng et al. https://doi.org/10.3390/rs18172915
16. Selected properties of Norway spruce ( Picea abies (L.) H. Karst) wood after 5 years of storage in a pile: a case study J. Chrostek et al. https://doi.org/10.1080/17480272.2026.2727542
17. Plant species mapping across the Miankaleh biosphere reserve and successional patterns in its drying wetland: A remote sensing-based assessment M. Karimi et al. https://doi.org/10.1016/j.ecoinf.2026.103822
18. Spatial scaling of drought impacts on forest mortality: when climate signals emerge from local noise L. Tymińska-Czabańska et al. https://doi.org/10.1016/j.agrformet.2026.111213
19. A Sentinel-2 machine learning dataset for tree species classification in Germany M. Freudenberg et al. https://doi.org/10.5194/essd-17-351-2025
20. Unsupervised Approach to Forest Tree Species Diversity Assessment With Satellite Observations J. Talaga et al. https://doi.org/10.1109/JSTARS.2026.3667647
21. TS2GNet: A temporal–spatial–spectral multidomain guided network for classifying hyperspectral tree species using multiseason satellite imagery K. Xu et al. https://doi.org/10.1016/j.jag.2025.104715
22. Comparison of Sentinel-2 Multitemporal Approaches for Tree Species Mapping Within Natura 2000 Riparian Forest Y. Rueva et al. https://doi.org/10.3390/rs17183194
23. Temporal generalization in evergreen leaf type classification using tailored Sentinel-2 composites P. Hofinger et al. https://doi.org/10.1016/j.ecoinf.2025.103167
24. Mapping tree species fractions in temperate mixed forests using Sentinel-2 time series and synthetically mixed training data D. Klehr et al. https://doi.org/10.1016/j.rse.2025.114740
25. A Novel Large Vision Foundation Model-Based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management S. Tan et al. https://doi.org/10.34133/remotesensing.0880
26. Comprehensive mapping of individual living and dead tree species using leaf-on and leaf-off ALS and CIR data in a complex temperate forest M. Lisiewicz et al. https://doi.org/10.1093/forestry/cpaf007
27. Extensive forest canopy discoloration during the 2024 heat-drought in southeastern Central Europe: The response of sessile oak (Quercus petraea) in Croatia was consistent with premature leaf senescence A. Kern & H. Marjanović https://doi.org/10.1016/j.agrformet.2026.111450
28. Preliminary forest tree species classification in northern provinces of Mongolia using Sentinel-2 and machine learning approach E. Boldbaatar et al. https://doi.org/10.14746/quageo-2026-0009
29. Stable in vitro propagation of Fagus sylvatica using micro-cuttings established from seedlings F. Thiesen et al. https://doi.org/10.1007/s11240-025-03304-y
30. Sentinel-2 vs. PlanetScope: Comparison and combination for tree species classification in two central European forest ecosystems A. Daryaei et al. https://doi.org/10.1016/j.rsase.2025.101617
31. Classification of Tree Species in Poland Using CNNs Tabular-to-Pseudo Image Approach Based on Sentinel-2 Annual Seasonality Data Ł. Mikołajczyk et al. https://doi.org/10.3390/f16071039
32. Floristic vs. Dominant Classification Approaches Applied to Geospatial Modeling of Mixed and Broadleaf Forest Types in the Northwestern Caucasus (Russia) E. Gavrilyuk et al. https://doi.org/10.3390/f16121761
33. Multi-indicator deterministic model based on time series of Sentinel-2, to assess the degree of natural succession on the abandoned arable areas M. Kozak et al. https://doi.org/10.2478/cag-2024-0017
34. Real-time pollen dynamics and automated detection: novel insights from Wrocław (Poland) 2024–2025 S. Tomczyk et al. https://doi.org/10.5194/amt-19-4779-2026