FUSION OF SENTINEL-2 AND PLANETSCOPE IMAGERY FOR VEGETATION DETECTION AND MONITORING

38 citations as recorded by crossref.

1. A web-based high-resolution global water and flood mapping platform F. Policelli et al. https://doi.org/10.1144/gh2025-7
2. Mapping the Cerrado–Amazon Transition Using PlanetScope–Sentinel Data Fusion and a U-Net Deep Learning Framework C. Li et al. https://doi.org/10.3390/rs17132138
3. Yield forecasting in maize: Performance and limits of unmanned aerial vehicle and PlanetScope remote sensing across multiple growth cycles P. Vigneault et al. https://doi.org/10.1016/j.compag.2026.111445
4. Enhanced spatial–spectral fusion for mapping invasive Spartina alterniflora in mangrove forests T. Li & L. Wang https://doi.org/10.1080/2150704X.2026.2715740
5. Soybean seed composition prediction from standing crops using PlanetScope satellite imagery and machine learning S. Sarkar et al. https://doi.org/10.1016/j.isprsjprs.2023.09.010
6. STAIR-Tr: Transformer-Based Gap Filling for Multisource Satellite Data Fusion K. Zhao et al. https://doi.org/10.1109/JSTARS.2026.3702922
7. Assessing the effectiveness of PlanetScope synthesized panchromatic bands for spatial enhancement of Sentinel-2 data G. Kaplan https://doi.org/10.1117/1.JRS.14.036504
8. Chalk grassland mapping in Surrey County using remote sensing and citizen science data: a random forest approach A. Andries et al. https://doi.org/10.1080/22797254.2026.2691799
9. Spatially predicting ecosystem service patterns in boreal drained peatlands forests using multisource satellite data K. Keränen et al. https://doi.org/10.1016/j.jag.2025.104545
10. A hybrid retrieval framework for vegetation biophysical and biochemical traits using PRISMA-like data S. Hasti & M. Maralan https://doi.org/10.1080/01431161.2026.2720315
11. Fusion of PlanetScope and Sentinel 2 in assessing foliar C:N ratio in a rangeland A. Arogoundade et al. https://doi.org/10.1080/14498596.2023.2229274
12. Early season irrigation detection and evapotranspiration modeling of winter vegetables based on Planet satellite using water and energy balance algorithm in lower Colorado basin R. Dhungel et al. https://doi.org/10.1007/s00271-023-00874-7
13. Trajectories of farming systems reshape land use/land cover dynamics in the agricultural zones of the Galapagos Islands F. Benitez et al. https://doi.org/10.1016/j.agsy.2025.104506
14. Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges T. Matongera et al. https://doi.org/10.3390/rs13112060
15. Integrating Radar and Multispectral Data from Sentinel Satellites for Accurate Urban Vegetation Classification using Deep Learning Methods J. Ngoua Ndong Avele https://doi.org/10.32603/1993-8985-2026-29-3-112-120
16. A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions S. Ahmad et al. https://doi.org/10.1109/TGRS.2019.2950705
17. Significance of Planet SuperDove and refined Sentinel-2 imagery fusion for enhanced soil organic carbon prediction in croplands J. Mensah Biney et al. https://doi.org/10.1016/j.catena.2025.108902
18. Data-driven interpretability of urban tree species classification from deep learning models and Satellite Image Time Series C. Bressant et al. https://doi.org/10.1016/j.ufug.2026.129360
19. Effects of Satellite Revisit Rate and Time-Series Smoothing Method on Throughout-Season Maize Yield Correlation Accuracy E. Myers et al. https://doi.org/10.1109/JSTARS.2021.3129148
20. Agricultural growth and land use land cover change in peri-urban India S. Patel et al. https://doi.org/10.1007/s10661-019-7736-1
21. Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward D. Tuia et al. https://doi.org/10.1109/MGRS.2024.3425961
22. Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM P. Liu & X. Chen https://doi.org/10.3390/ijgi8020086
23. Spatial and Temporal Pasture Biomass Estimation Integrating Electronic Plate Meter, Planet CubeSats and Sentinel-2 Satellite Data J. Gargiulo et al. https://doi.org/10.3390/rs12193222
24. Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery J. Guo et al. https://doi.org/10.3390/agronomy12010014
25. New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification I. Aneece et al. https://doi.org/10.1109/JSTARS.2022.3204223
26. Cost-effectiveness of remote sensing technology for spruce budworm monitoring in Maine, USA A. Foster et al. https://doi.org/10.62320/fm.v1.i1.14
27. A robust and adaptive spatial-spectral fusion model for PlanetScope and Sentinel-2 imagery Y. Zhao & D. Liu https://doi.org/10.1080/15481603.2022.2036054
28. Generation of High-Resolution Time-Series NDVI Images for Monitoring Heterogeneous Crop Fields S. Kim et al. https://doi.org/10.3390/s25165183
29. Application of nanosatellites PlanetScope data to monitor crop growth S. Kokhan et al. https://doi.org/10.1051/e3sconf/202017102014
30. Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation C. Baldin & V. Casella https://doi.org/10.3390/geosciences15050184
31. Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data D. Kpienbaareh et al. https://doi.org/10.3390/rs13040700
32. Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring Y. Sadeh et al. https://doi.org/10.1016/j.jag.2020.102260
33. Planetscope Nanosatellites Image Classification Using Machine Learning M. Anul Haq https://doi.org/10.32604/csse.2022.023221
34. Fine-Scale Phenology of Urban Trees From Satellite Image Time Series: Toward a Comprehensive Analysis of Influencing Factors C. Bressant et al. https://doi.org/10.1109/JSTARS.2024.3411304
35. From Forest to Future Capital: Tracking Land Cover Change in Ibu Kota Nusantara (IKN) from 2021 to 2026 with PlanetScope Imagery C. Ong et al. https://doi.org/10.1016/j.infgeo.2026.100063
36. Optimizing urban critical green space development using machine learning M. Ganjirad et al. https://doi.org/10.1016/j.scs.2025.106158
37. Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems T. Surasinghe et al. https://doi.org/10.3390/rs17101778
38. Phenology-based early detection of bracken fern using PlanetScope and fused Sentinel-2–Sentinel-1 time series R. Mukwevho et al. https://doi.org/10.1080/01431161.2026.2680713