Articles | Volume 15, issue 6
https://doi.org/10.5194/gmd-15-2505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
25 Mar 2022
Model description paper |
| 25 Mar 2022
| 25 Mar 2022
SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python
Institute for Water and River Basin Management, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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- Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature A. Hassani et al. 10.1007/s10666-023-09943-9
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18 citations as recorded by crossref.
- Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry E. Mannerfelt et al. 10.5194/tc-16-3249-2022
- GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation E. MacKie et al. 10.5194/gmd-16-3765-2023
- Exploring spatiotemporal dynamics, seasonality, and time-of-day trends of PM2.5 pollution with a low-cost sensor network: Insights from classic and spatially explicit Markov chains M. Biancardi et al. 10.1016/j.apgeog.2024.103414
- RoGeR v3.0.5 – a process-based hydrological toolbox model in Python R. Schwemmle et al. 10.5194/gmd-17-5249-2024
- Utilizing the vegetation health index to assess agricultural drought in the Constantine Region of Algeria B. Maya et al. 10.47818/DRArch.2024.v5i2132
- A Python Multiprocessing Approach for Fast Geostatistical Simulations of Subglacial Topography N. Schoedl et al. 10.1109/MCSE.2023.3317773
- Global, spatially explicit modelling of zenith wet delay with XGBoost L. Crocetti et al. 10.1007/s00190-024-01829-2
- pySimFrac: A Python library for synthetic fracture generation and analysis E. Guiltinan et al. 10.1016/j.cageo.2024.105665
- Large-Pore Network Simulations Coupled with Innovative Wettability Anchoring Experiment to Predict Relative Permeability of a Mixed-Wet Rock M. Regaieg et al. 10.1007/s11242-023-01921-9
- GSTools v1.3: a toolbox for geostatistical modelling in Python S. Müller et al. 10.5194/gmd-15-3161-2022
- Bond Portfolio Optimization at Life Insurance Companies: Duration Spread Ratio Optimization vs. Mean-Variance Optimization O. Gurin 10.2139/ssrn.4825814
- Co-Kriging-Guided Interpolation for Mapping Forest Aboveground Biomass by Integrating Global Ecosystem Dynamics Investigation and Sentinel-2 Data Y. Wang et al. 10.3390/rs16162913
- Fuzzy membership function for weighting pairs in variographical analysis P. Masoudi 10.1016/j.spasta.2022.100717
- Spatial structure of in situ reflectance in coastal and inland waters: implications for satellite validation T. Jordan et al. 10.3389/frsen.2023.1249521
- SciKit-GStat Uncertainty: A software extension to cope with uncertain geostatistical estimates M. Mälicke et al. 10.1016/j.spasta.2023.100737
- PRODEM: an annual series of summer DEMs (2019 through 2022) of the marginal areas of the Greenland Ice Sheet M. Winstrup et al. 10.5194/essd-16-5405-2024
- Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain R. Hugonnet et al. 10.1109/JSTARS.2022.3188922
- A Gaussian Process Based Approach for Validation of Multi-Variable Measurement Systems: Application to SAR Measurement Systems C. Bujard et al. 10.1109/ACCESS.2024.3393778
2 citations as recorded by crossref.
- Interpolation, Satellite-Based Machine Learning, or Meteorological Simulation? A Comparison Analysis for Spatio-temporal Mapping of Mesoscale Urban Air Temperature A. Hassani et al. 10.1007/s10666-023-09943-9
- SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python M. Mälicke 10.5194/gmd-15-2505-2022
Latest update: 13 Dec 2024
Short summary
I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The variogram is the core means of geostatistics, which almost all other methods rely on. Geostatistical interpolation and field generation are widely spread in geoscience, i.e., for data assimilation or modeling.
While SciKit-GStat focuses on effective and intuitive variogram estimation, it can interface with other prominent packages and make its variograms available for a multitude of methods.
I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The...
