129 citations as recorded by crossref.

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48. Neural geometry in the human hippocampus enables generalization across spatial position and gaze A. Chericoni et al. https://doi.org/10.1016/j.neuron.2026.08.004
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51. AIS and VMS Ensemble Can Address Data Gaps on Fisheries for Marine Spatial Planning P. Thoya et al. https://doi.org/10.3390/su13073769
52. CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey L. Zhang et al. https://doi.org/10.1016/j.compag.2024.108672
53. On the value of mobile cosmic-ray neutron measurements for spatio-temporal soil moisture simulations E. Fatima et al. https://doi.org/10.3389/frwa.2025.1630051
54. In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics Y. Yang et al. https://doi.org/10.1029/2018WR024178
55. Hydrograph separation for tackling equifinality in conceptual hydrological models J. Casado-Rodríguez & M. del Jesus https://doi.org/10.1016/j.jhydrol.2022.127816
56. Diagnosis of GCM-RCM-driven rainfall patterns under changing climate through the robust selection of multi-model ensemble and sub-ensembles S. Gaur et al. https://doi.org/10.1007/s10584-022-03475-z
57. What controls the distribution of post-Little Ice Age landslides around the South Patagonian Icefield? G. Seier et al. https://doi.org/10.1007/s10346-025-02681-x
58. Projection of precipitation and temperature in major cities of Pakistan using multi-model ensembles F. Shah & A. Sharifi https://doi.org/10.1016/j.uclim.2025.102430
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60. Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods Z. Iqbal et al. https://doi.org/10.3390/su14116620
61. Validation of retracked Sentinel-3 altimetry observations along the Norwegian coast M. Tomić et al. https://doi.org/10.1016/j.asr.2024.01.055
62. Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression H. Zhang et al. https://doi.org/10.3390/ijgi11020101
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67. Deep‐learning‐based harmonization and super‐resolution of near‐surface air temperature from CMIP6 models (1850–2100) X. Wei et al. https://doi.org/10.1002/joc.7926
68. Scaling Point‐Scale (Pedo)transfer Functions to Seamless Large‐Domain Parameter Estimates for High‐Resolution Distributed Hydrologic Modeling: An Example for the Rhine River R. Imhoff et al. https://doi.org/10.1029/2019WR026807
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72. Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling R. Sun et al. https://doi.org/10.1029/2020WR028740
73. Bias correction for near-real-time estimation of snow water equivalent using machine learning algorithms: A case study in the Tuolumne River basin, California K. Yang et al. https://doi.org/10.1016/j.rse.2025.114693
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77. On the Evaluation of Both Spatial and Temporal Performance of Distributed Hydrological Models Using Remote Sensing Products T. Nguyen et al. https://doi.org/10.3390/rs14091959
78. Projection of temperature and precipitation in the Mediterranean region through multi-model ensemble from CMIP6 M. Seker & V. Gumus https://doi.org/10.1016/j.atmosres.2022.106440
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