50 citations as recorded by crossref.

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6. Climate change fingerprints in recent European plant phenology A. Menzel et al. 10.1111/gcb.15000
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8. Mapping rapeseed planting areas using an automatic phenology- and pixel-based algorithm (APPA) in Google Earth Engine J. Han et al. 10.1016/j.cj.2022.04.013
9. The use of remote sensing indices to understand flood‐recharged soil moisture impacts on trees in semi‐arid floodplains: A review E. Pedzisai et al. 10.1002/eco.2460
10. Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk M. Möller et al. 10.1016/j.catena.2016.11.016
11. Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series A. Htitiou et al. 10.3390/rs16173183
12. Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6 S. Rolinski et al. 10.5194/gmd-11-429-2018
13. Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield E. Rezaei et al. 10.1038/s41598-024-74309-4
14. Data rescue of national and international meteorological observations at Deutscher Wetterdienst F. Kaspar et al. 10.5194/asr-12-57-2015
15. Phenology Information Contributes to Reduce Temporal Basis Risk in Agricultural Weather Index Insurance T. Dalhaus et al. 10.1038/s41598-017-18656-5
16. Reduced Reproductive Success of Western Baltic Herring (Clupea harengus) as a Response to Warming Winters P. Polte et al. 10.3389/fmars.2021.589242
17. The Flowering of Black Locust (Robinia pseudoacacia L.) in Italy: A Phenology Modeling Approach R. Alilla et al. 10.3390/agronomy12071623
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19. Climate data and flowering times for 450 species from 1844 deepen the record of phenological change in southern Germany S. Renner et al. 10.1002/ajb2.1643
20. The International Phenological Garden network (1959 to 2021): its 131 gardens, cloned study species, data archiving, and future S. Renner & F. Chmielewski 10.1007/s00484-021-02185-y
21. Phenoclimatology: development and applications in North America M. Schwartz & T. Crimmins 10.1080/02723646.2024.2313783
22. Development of a Spectral Index for the Detection of Yellow-Flowering Vegetation C. Shao et al. 10.3390/rs15071725
23. Can Gridded Precipitation Data and Phenological Observations Reduce Basis Risk of Weather Index–Based Insurance? T. Dalhaus & R. Finger 10.1175/WCAS-D-16-0020.1
24. The optimal drought index for designing weather index insurance J. Bucheli et al. 10.1093/erae/jbaa014
25. Summable C factors for contemporary soil use K. Auerswald et al. 10.1016/j.still.2021.105155
26. Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity K. Petersen et al. 10.1016/j.eja.2021.126306
27. Change in erosion potential of crops due to climate change K. Auerswald & A. Menzel 10.1016/j.agrformet.2021.108338
28. Temperature effects on crop yields in heat index insurance J. Bucheli et al. 10.1016/j.foodpol.2021.102214
29. Historical Data for Natural Hazard Risk Mitigation and Land Use Planning F. Luino et al. 10.3390/land12091777
30. Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2 M. Meroni et al. 10.1016/j.rse.2020.112232
31. Developing a New Method to Identify Flowering Dynamics of Rapeseed Using Landsat 8 and Sentinel-1/2 J. Han et al. 10.3390/rs13010105
32. Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran M. Akbarian et al. 10.1016/j.jhydrol.2023.129480
33. Shifted Global Vegetation Phenology in Response to Climate Changes and Its Feedback on Vegetation Carbon Uptake H. Fang et al. 10.3390/rs15092288
34. Climate and management interaction cause diverse crop phenology trends E. Eyshi Rezaei et al. 10.1016/j.agrformet.2016.11.003
35. Timing and intensity of heat and drought stress determine wheat yield losses in Germany L. Riedesel et al. 10.1371/journal.pone.0288202
36. How Phenology Shapes Crop-Specific Sentinel-1 PolSAR Features and InSAR Coherence across Multiple Years and Orbits J. Löw et al. 10.3390/rs16152791
37. A Crop Group-Specific Pure Pixel Time Series for Europe C. Weissteiner et al. 10.3390/rs11222668
38. Detection of Phenology-Defined Data Acquisition Time Frames For Crop Type Mapping H. Gerstmann et al. 10.1007/s41064-018-0043-6
39. The influence of calibration data diversity on the performance of temperature-based spring phenology models for forest tree species in Central Europe A. Picornell et al. 10.1016/j.agrformet.2024.110302
40. Citizen science and expert opinion working together to understand the impacts of climate change M. Garcia-Rojas et al. 10.1371/journal.pone.0273822
41. The RapeseedMap10 database: annual maps of rapeseed at a spatial resolution of 10 m based on multi-source data J. Han et al. 10.5194/essd-13-2857-2021
42. Correlations of Soil Fungi, Soil Structure and Tree Vigour on an Apple Orchard with Replant Soil U. Cavael et al. 10.3390/soilsystems4040070
43. Soybean phenological changes in response to climate warming in three northeastern provinces of China M. Xin et al. 10.1016/j.fcr.2023.109082
44. Optimized bare soil compositing for soil organic carbon prediction of topsoil croplands in Bavaria using Landsat S. Zepp et al. 10.1016/j.isprsjprs.2023.06.003
45. Remote sensing phenology of two Chinese northern Sphagnum bogs under climate drivers during 2001 and 2018 Y. Pang et al. 10.1016/j.ecolind.2021.107968
46. Monitoring Forest Phenology in a Changing World R. Gray & R. Ewers 10.3390/f12030297
47. PhenoWin – An R Shiny application for visualization and extraction of phenological windows in Germany M. Möller et al. 10.1016/j.compag.2020.105534
48. Impact of invasive alien tree species on symbiotic soil fungal communities in pine-dominated forest ecosystems in central Europe R. Wilgan et al. 10.1016/j.geoderma.2024.117111
49. Climate change effect on wheat phenology depends on cultivar change E. Rezaei et al. 10.1038/s41598-018-23101-2
50. A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level F. Lobert et al. 10.1016/j.rse.2023.113800