Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3311-2015
© Author(s) 2015. 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-8-3311-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
20 Oct 2015
Development and technical paper |
| 20 Oct 2015
Mapping of satellite Earth observations using moving window block kriging
J. M. Tadić
CORRESPONDING AUTHOR
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
X. Qiu
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
V. Yadav
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
A. M. Michalak
Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
Viewed
Total article views: 6,382 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Aug 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,392 | 2,707 | 283 | 6,382 | 365 | 404 |
- HTML: 3,392
- PDF: 2,707
- XML: 283
- Total: 6,382
- BibTeX: 365
- EndNote: 404
Total article views: 5,127 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 20 Oct 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,839 | 2,027 | 261 | 5,127 | 337 | 374 |
- HTML: 2,839
- PDF: 2,027
- XML: 261
- Total: 5,127
- BibTeX: 337
- EndNote: 374
Total article views: 1,255 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Aug 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 553 | 680 | 22 | 1,255 | 28 | 30 |
- HTML: 553
- PDF: 680
- XML: 22
- Total: 1,255
- BibTeX: 28
- EndNote: 30
Cited
24 citations as recorded by crossref.
- Identification of Bias in Satellite Measurements Using its Geospatial Properties J. Tadic & S. Biraud https://doi.org/10.1109/LGRS.2020.3015174
- Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations L. Li et al. https://doi.org/10.3390/rs14030654
- A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks Y. Zhang et al. https://doi.org/10.5194/bg-15-5779-2018
- Estimating monthly China XCO 2 based on DQ-1 data and spatiotemporal heterogeneous filtering model D. Zhu et al. https://doi.org/10.1080/17538947.2026.2668778
- Spatio-temporal approach to moving window block kriging of satellite data v1.0 J. Tadić et al. https://doi.org/10.5194/gmd-10-709-2017
- Drought impacts on photosynthesis, isoprene emission and atmospheric formaldehyde in a mid-latitude forest Y. Zheng et al. https://doi.org/10.1016/j.atmosenv.2017.08.017
- Improving Bayesian Local Spatial Models in Large Datasets A. Lenzi et al. https://doi.org/10.1080/10618600.2020.1814789
- A physics-based approach to oversample multi-satellite, multispecies observations to a common grid K. Sun et al. https://doi.org/10.5194/amt-11-6679-2018
- Locally stationary spatio-temporal interpolation of Argo profiling float data M. Kuusela & M. Stein https://doi.org/10.1098/rspa.2018.0400
- High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems L. da Costa et al. https://doi.org/10.1016/j.envres.2022.114991
- Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and LULC Information S. Bhattacharjee & J. Chen https://doi.org/10.1109/TGRS.2020.2985047
- Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments P. Ma et al. https://doi.org/10.1080/00401706.2018.1524791
- Data reduction for inverse modeling: an adaptive approach v1.0 X. Liu et al. https://doi.org/10.5194/gmd-14-4683-2021
- Temporal and Spatial Patterns of XCO2 and SIF as Observed by OCO-2: A Case Study in the Midwest Region of Brazil G. de Araújo Santos et al. https://doi.org/10.1007/s12524-024-01995-y
- A Review of Satellite-Based CO2 Data Reconstruction Studies: Methodologies, Challenges, and Advances K. Hu et al. https://doi.org/10.3390/rs16203818
- Great expectations and even greater exceedances from spatially referenced data N. Cressie & T. Suesse https://doi.org/10.1016/j.spasta.2020.100420
- Geostatistical Analysis of CH4 Columns over Monsoon Asia Using Five Years of GOSAT Observations M. Liu et al. https://doi.org/10.3390/rs8050361
- Validation of Column-Averaged Dry-Air Mole Fraction of CO2 Retrieved from OCO-2 Using Ground-Based FTS Measurements Y. Bi et al. https://doi.org/10.1007/s13351-018-7118-6
- Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data J. Jacobson et al. https://doi.org/10.3390/rs15164038
- On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals A. Zammit-Mangion et al. https://doi.org/10.3390/rs10010155
- Characterizing the Spatial Scales of Extreme Daily Precipitation in the United States D. Touma et al. https://doi.org/10.1175/JCLI-D-18-0019.1
- QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach M. Gravey & G. Mariethoz https://doi.org/10.5194/gmd-13-2611-2020
- Extending geostationary satellite AOD coverage with a lightweight spatiotemporal sequence model Y. Wang et al. https://doi.org/10.1016/j.jqsrt.2026.109899
- Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence Y. Shiga et al. https://doi.org/10.1002/2017GL076630
24 citations as recorded by crossref.
- Identification of Bias in Satellite Measurements Using its Geospatial Properties J. Tadic & S. Biraud https://doi.org/10.1109/LGRS.2020.3015174
- Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations L. Li et al. https://doi.org/10.3390/rs14030654
- A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks Y. Zhang et al. https://doi.org/10.5194/bg-15-5779-2018
- Estimating monthly China XCO 2 based on DQ-1 data and spatiotemporal heterogeneous filtering model D. Zhu et al. https://doi.org/10.1080/17538947.2026.2668778
- Spatio-temporal approach to moving window block kriging of satellite data v1.0 J. Tadić et al. https://doi.org/10.5194/gmd-10-709-2017
- Drought impacts on photosynthesis, isoprene emission and atmospheric formaldehyde in a mid-latitude forest Y. Zheng et al. https://doi.org/10.1016/j.atmosenv.2017.08.017
- Improving Bayesian Local Spatial Models in Large Datasets A. Lenzi et al. https://doi.org/10.1080/10618600.2020.1814789
- A physics-based approach to oversample multi-satellite, multispecies observations to a common grid K. Sun et al. https://doi.org/10.5194/amt-11-6679-2018
- Locally stationary spatio-temporal interpolation of Argo profiling float data M. Kuusela & M. Stein https://doi.org/10.1098/rspa.2018.0400
- High spatial resolution solar-induced chlorophyll fluorescence and its relation to rainfall precipitation across Brazilian ecosystems L. da Costa et al. https://doi.org/10.1016/j.envres.2022.114991
- Prediction of Satellite-Based Column CO2 Concentration by Combining Emission Inventory and LULC Information S. Bhattacharjee & J. Chen https://doi.org/10.1109/TGRS.2020.2985047
- Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments P. Ma et al. https://doi.org/10.1080/00401706.2018.1524791
- Data reduction for inverse modeling: an adaptive approach v1.0 X. Liu et al. https://doi.org/10.5194/gmd-14-4683-2021
- Temporal and Spatial Patterns of XCO2 and SIF as Observed by OCO-2: A Case Study in the Midwest Region of Brazil G. de Araújo Santos et al. https://doi.org/10.1007/s12524-024-01995-y
- A Review of Satellite-Based CO2 Data Reconstruction Studies: Methodologies, Challenges, and Advances K. Hu et al. https://doi.org/10.3390/rs16203818
- Great expectations and even greater exceedances from spatially referenced data N. Cressie & T. Suesse https://doi.org/10.1016/j.spasta.2020.100420
- Geostatistical Analysis of CH4 Columns over Monsoon Asia Using Five Years of GOSAT Observations M. Liu et al. https://doi.org/10.3390/rs8050361
- Validation of Column-Averaged Dry-Air Mole Fraction of CO2 Retrieved from OCO-2 Using Ground-Based FTS Measurements Y. Bi et al. https://doi.org/10.1007/s13351-018-7118-6
- Spatial Statistical Prediction of Solar-Induced Chlorophyll Fluorescence (SIF) from Multivariate OCO-2 Data J. Jacobson et al. https://doi.org/10.3390/rs15164038
- On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals A. Zammit-Mangion et al. https://doi.org/10.3390/rs10010155
- Characterizing the Spatial Scales of Extreme Daily Precipitation in the United States D. Touma et al. https://doi.org/10.1175/JCLI-D-18-0019.1
- QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach M. Gravey & G. Mariethoz https://doi.org/10.5194/gmd-13-2611-2020
- Extending geostationary satellite AOD coverage with a lightweight spatiotemporal sequence model Y. Wang et al. https://doi.org/10.1016/j.jqsrt.2026.109899
- Atmospheric CO2 Observations Reveal Strong Correlation Between Regional Net Biospheric Carbon Uptake and Solar‐Induced Chlorophyll Fluorescence Y. Shiga et al. https://doi.org/10.1002/2017GL076630
Saved (final revised paper)
Latest update: 27 Sep 2026
