the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Subsets of geostationary satellite data over international observing network sites for studying the diurnal dynamics of energy, carbon, and water cycles
Abstract. The latest generation of geostationary satellites provide Earth observations similar to widely used polar orbiting sensors but at intervals as frequently as every 5–10 minutes, making them ideal for studying diurnal dynamics of land-atmosphere interactions. The NASA Earth Exchange (NEX) group created the GeoNEX datasets by collating data from several geostationary platforms, including GOES-16/17/18, Himawari-8/9, and GK-2A, and placing them on a common grid to facilitate use by the Earth science community. Here, we document the GeoNEX Coincident Ground Observations dataset (GeCGO) for terrestrial ecosystems studies, and provide examples for its use. Currently, GeCGO provides GOES-16 Advanced Baseline Imager (ABI) data over a 10 x 10 km area surrounding 1586 network sites across Americas. GeCGO make it easy to compare the time series of geostationary data with the diurnal ground observations including carbon/water fluxes and aerosol optical depth, and is extensible to other regions. We also developed GeoNEXTools to facilitate analyses that require both GeoNEX data and other NASA satellite data. The objectives of this paper are to introduce GeCGO and GeoNEXTools, and demonstrate their applications. First, we describe the details of GeCGO and GeoNEXTools. Second, we explain how GeCGO can be integrated with other satellite data. Finally, we showcase comparisons between GeCGO and observations from three ground-based networks.
- Preprint
(3034 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 26 Apr 2025)
-
RC1: 'Comment on essd-2025-33', Anonymous Referee #1, 02 Apr 2025
reply
Much hope has been placed on the hyper-temporal observations from the new generation geostationary satellites to complete the polar-orbiting satellites for environmental remote sensing. Yet a major barrier for the users is the standard, high-level science products from the GEO sensors that can be readily used in the specific applications, hence saving significant amount of time in going through the sophisticated satellite data preprocessing, which is nearly impossible for scientists working in ecology or environmental sciences. In this context, this study by Hashimoto and colleagues made a good effort in eliminating the GEO data barrier and provide a well-organized dataset containing processed high-level variables such as surface reflectance, LST, and solar radiation for more than one thousand of ecological and environmental network sites. In my opinion, this dataset and the R toolbox provided, which is functionally similar to the widely used TESViS MODIS data subset tool maintained by ORNL, is much needed and will be well welcomed by the ecologists and the carbon and water cycle science community. I have no major concerns with regard to workflow employed in creating the dataset. I do have some minor comments that the authors can consider in working on the revised version. The manuscript is well written and was a joy to read. I believe it can be accepted for publication in ESSD with a minor revision.
Specific comments:
L100: I suggest to add figures to illustrate the pixel numbering scheme of the image stamps with different sizes, something similar to TESViS did for various MODIS products.
L210: just to be a bit more accurate, MODIS onboard Terra/Aqua can in together provide four instantaneous measurements per day;
L230: when presenting correlation coefficients, suggest to provide p-value for knowing the significance of the correlation;
Figure 5: the data points on L1G plot is much less than that of L2G, perhaps due to cloud screening during the MAAIAC. Just to be fair with L1g, I wonder if the L2G is still superior than L1g when same subset of data (i.e., sites) is used.
L245: I am not sure what is the relevance of “MVC” here, as it is not recommended for generating multi-day VI composite due to its tendency of chose off-nadir observations (see, van Leeuwen et al. 1999);
* van Leeuwen, W.J.D., Huete, A.R., Laing, T.W. (1999) MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data. Remote Sensing of Environment, 69, 264-280.L260: “The aerosol has large diurnal variability in Aerosol Optical Depth (AOD).” suggest change to —> “The Aerosol Optical Depth (AOD) has large diurnal variability.”
L280: Just to be specific, PhenoCam is one kind of phenology ground observation network focusing on using time-lapse cameras, with another one is the USA National Phenology Network.
L290: “harvardfarmnorth”, this sounds like a site-code not site name;
Fig. 7: please add legend on the plot. Besides, I suggest to have y-axis title for each sub plot to facilitate the interpretation;
L310: ‘You can download the We will add…’, please revise.
The title for Sub-section 3.2.1 “Which NDVI or NIRv can represents annual GPP is more representative of annual GPP?” is grammarly incorrect and can be revised;
Citation: https://doi.org/10.5194/essd-2025-33-RC1
Data sets
GeoNEX Coincident Ground Observations (GeCGO) Hirofumi Hashimoto https://doi.org/10.25966/y5pe-xp41
Model code and software
GeoNEXTools Hirofumi Hashimoto https://github.com/nasa/GeoNEXTools
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
163 | 27 | 4 | 194 | 5 | 4 |
- HTML: 163
- PDF: 27
- XML: 4
- Total: 194
- BibTeX: 5
- EndNote: 4
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1