Articles | Volume 10, issue 2
https://doi.org/10.5194/gmd-10-709-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-709-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
15 Feb 2017
Development and technical paper |
| 15 Feb 2017
Spatio-temporal approach to moving window block kriging of satellite data v1.0
Department of Global Ecology, Carnegie Institution for Science,
Stanford, CA 94305, USA
Xuemei Qiu
Department of Global Ecology, Carnegie Institution for Science,
Stanford, CA 94305, USA
Scot Miller
Department of Global Ecology, Carnegie Institution for Science,
Stanford, CA 94305, USA
Anna M. Michalak
Department of Global Ecology, Carnegie Institution for Science,
Stanford, CA 94305, USA
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28 citations as recorded by crossref.
- Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2 J. Susiluoto et al. 10.5194/gmd-13-3439-2020
- Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes W. Sun et al. 10.1029/2020AV000310
- Satellite-Based Reconstruction of Atmospheric CO2 Concentration over China Using a Hybrid CNN and Spatiotemporal Kriging Model Y. Hua et al. 10.3390/rs16132433
- Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation E. Varouchakis et al. 10.3390/s21093132
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- Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data J. Jacobson et al. 10.3390/rs15164038
- A Regional Spatiotemporal Downscaling Method for CO2Columns X. Ma et al. 10.1109/TGRS.2021.3052215
- Spatial enhancement of Landsat-9 land surface temperature imagery by Fourier transformation-based panchromatic fusion K. Sharma et al. 10.1080/19479832.2023.2293077
- Data reduction for inverse modeling: an adaptive approach v1.0 X. Liu et al. 10.5194/gmd-14-4683-2021
- QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach M. Gravey & G. Mariethoz 10.5194/gmd-13-2611-2020
- A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks Y. Zhang et al. 10.5194/bg-15-5779-2018
- Drought impacts on photosynthesis, isoprene emission and atmospheric formaldehyde in a mid-latitude forest Y. Zheng et al. 10.1016/j.atmosenv.2017.08.017
- Estimation of daily XCO2 at 1 km resolution in China using a spatiotemporal ResNet model C. Wu et al. 10.1016/j.scitotenv.2024.176171
- A physics-based approach to oversample multi-satellite, multispecies observations to a common grid K. Sun et al. 10.5194/amt-11-6679-2018
- Assessment of Groundwater Depletion and Implications for Management in the Denver Basin Aquifer System C. Ruybal et al. 10.1111/1752-1688.12755
- Greenhouse gas fluxes from Alaska's North Slope inferred from the Airborne Carbon Measurements campaign (ACME-V) J. Tadić et al. 10.1016/j.atmosenv.2021.118239
- Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence Y. Shiga et al. 10.1002/2017GL076630
- Locally stationary spatio-temporal interpolation of Argo profiling float data M. Kuusela & M. Stein 10.1098/rspa.2018.0400
- Can satellite data on air pollution predict industrial production? J. Bricongne et al. 10.2139/ssrn.3967146
- High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems L. da Costa et al. 10.1016/j.envres.2022.114991
- Effects of Climate Change on Precipitation and the Maximum Daily Temperature (Tmax) at Two US Military Bases with Different Present-Day Climates J. Tadić & S. Biraud 10.3390/cli8020018
- Identification of Bias in Satellite Measurements Using its Geospatial Properties J. Tadic & S. Biraud 10.1109/LGRS.2020.3015174
- Spatiotemporal geostatistical modeling of groundwater levels under a Bayesian framework using means of physical background E. Varouchakis et al. 10.1016/j.jhydrol.2019.05.055
- Towards Hyper-Dimensional Variography Using the Product-Sum Covariance Model J. Tadić et al. 10.3390/atmos10030148
- Validation of Column-Averaged Dry-Air Mole Fraction of CO2 Retrieved from OCO-2 Using Ground-Based FTS Measurements Y. Bi et al. 10.1007/s13351-018-7118-6
- Evaluation of Groundwater Levels in the Arapahoe Aquifer Using Spatiotemporal Regression Kriging C. Ruybal et al. 10.1029/2018WR023437
- Hybrid Machine Learning and Geostatistical Methods for Gap Filling and Predicting Solar-Induced Fluorescence Values J. Tadić et al. 10.3390/rs16101707
- Estimating high spatio-temporal resolution XCO2 using spatial features deep fusion model L. Cui et al. 10.1016/j.atmosres.2024.107542
Latest update: 23 Nov 2024
Short summary
We developed a new method to create contiguous maps from sparse and/or noisy satellite observations. This approach could be used to produce retroactive or real-time estimates of environmental data observed by satellites which exhibit spatio-temporal autocorrelations. The method could be applied in a standalone mode or as part of a broader satellite data processing package. Maps produced in this way could then be incorporated into physical and biogeochemical models of the Earth system.
We developed a new method to create contiguous maps from sparse and/or noisy satellite...
