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- Added [Global 30m Impervious-Surface Dynamic Dataset (GISD30)](https://samapriya.github.io/awesome-gee-community-datasets/projects/gisd30) - Added [USGS VIIRS Evapotranspiration](https://samapriya.github.io/awesome-gee-community-datasets/projects/usgs_viirs) - Added [USGS MODIS Evapotranspiration](https://samapriya.github.io/awesome-gee-community-datasets/projects/usgs_modis_et) - Added [Snow Data Assimilation System (SNODAS) Daily](https://samapriya.github.io/awesome-gee-community-datasets/projects/snodas) - Added [North American Drought Monitor (NADM) Monthly](https://samapriya.github.io/awesome-gee-community-datasets/projects/nadm)
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| # Global 30m Impervious-Surface Dynamic Dataset (GISD30) | ||
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| The Global 30 m Impervious-Surface Dynamic Dataset (GISD30) offers an invaluable resource for understanding the ever-changing landscape of impervious surfaces across the globe from 1985 to 2020. This dataset holds profound scientific significance and practical applications in the realms of urban sustainable development, anthropogenic carbon emissions assessment, and global ecological-environment modeling. The GISD30 was meticulously created through an innovative and automated methodology that capitalizes on the strengths of spectral-generalization and automatic-sample-extraction strategies. Leveraging time-series Landsat imagery on the Google Earth Engine cloud computing platform, the dataset provides comprehensive insights into impervious-surface dynamics. | ||
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| In the dataset creation process, global training samples and corresponding reflectance spectra were automatically derived, enhancing accuracy and reliability. Spatiotemporal adaptive classification models were employed, taking into account the dynamic nature of impervious surfaces across different epochs and geographical tiles. Furthermore, a spatiotemporal-consistency correction method was introduced to enhance the reliability of impervious-surface dynamics. The GISD30 dynamic model exhibits remarkable accuracy, with an overall accuracy of 90.1% and a kappa coefficient of 0.865, validated using a substantial dataset of 23,322 global time-series samples. This dataset provides vital insights into the doubling of global impervious surface area over the past 35 years, from 1985 to 2020, with Asia experiencing the most substantial increase. The GISD30 dataset is freely accessible and serves as a crucial tool for monitoring urbanization at regional and global scales, offering invaluable support for diverse applications. Access the [dataset here](https://doi.org/10.5281/zenodo.5220816) (Liu et al., 2021b). | ||
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| The global dynamic dataset was used to label the expansion information in a single band; specifically, the pervious surface and the impervious surface before 1985 were, respectively, labeled 0 and 1, and the expanded impervious surfaces in the periods 1985–1990, 1990–1995, 1995–2000, 2000–2005, 2005–2010, 2010–2015 and 2015–2020 were labeled 2, 3, 4, 5, 6, 7 and 8. | ||
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| <center> | ||
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| | Years | Impervious Surface Labels | | ||
| |-----------------|---------------------------| | ||
| | Before 1985 | 1 | | ||
| | 1985–1990 | 2 | | ||
| | 1990–1995 | 3 | | ||
| | 1995–2000 | 4 | | ||
| | 2000–2005 | 5 | | ||
| | 2005–2010 | 6 | | ||
| | 2010–2015 | 7 | | ||
| | 2015–2020 | 8 | | ||
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| </center> | ||
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| #### Citation | ||
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| ``` | ||
| Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J.: GISD30: global 30 m impervious-surface dynamic dataset from 1985 to 2020 using | ||
| time-series Landsat imagery on the Google Earth Engine platform, Earth Syst. Sci. Data, 14, 1831–1856, | ||
| https://doi.org/10.5194/essd-14-1831-2022, 2022 | ||
| ``` | ||
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| #### Dataset citation | ||
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| ``` | ||
| Liangyun,Liu; Xiao,Zhang; Tingting,Zhao; Yuan,Gao; Xidong,Chen; Jun,Mi. (2021). GISD30: global 30-m impervious surface dynamic dataset from 1985 to | ||
| 2020 using time-series Landsat imagery on the Google Earth Engine platform [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5220816 | ||
| ``` | ||
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| #### Earth Engine Snippet | ||
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| ```js | ||
| var gisd30 = ee.Image("projects/sat-io/open-datasets/GISD30_1985_2020"); | ||
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| //zoom to an urban center | ||
| Map.setCenter(31.16387, 30.97292,8) | ||
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| var palette = ["#808080", "#006400", "#228B22", "#32CD32", "#ADFF2F", "#FFFF00", "#FFA500", "#FF0000"]; | ||
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| var snazzy = require("users/aazuspan/snazzy:styles"); | ||
| snazzy.addStyle("https://snazzymaps.com/style/132/light-gray", "Grayscale"); | ||
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| Map.addLayer(gisd30,{min:1,max:8,palette:palette},'GISD 30') | ||
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| ``` | ||
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| Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:/global-landuse-landcover/GLOBAL-IMPERVIOUS-30-GISD | ||
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| #### License | ||
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| This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. | ||
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| Created by : Zhang, X., Liu, L., Zhao, T., Gao, Y., Chen, X., and Mi, J. | ||
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| Curated in GEE by: Samapriya Roy | ||
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| Keywords: Landsat, Urban, Google Earth Engine, Impervious area, Urban expansion, global dataset | ||
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| Last updated in GEE: 2023-09-12 |
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| # North American Drought Monitor (NADM) | ||
| The North American Drought Monitor (NADM) raster dataset is produced by the National Centers for Environmental Information (NCEI) and the National Oceanic and Atmospheric Administration's (NOAA) National Integrated Drought Information System (NIDIS). This dataset is a gridded version of the North American Drought Monitor (NADM) produced by Canadian, Mexican and US authors where for each 2.5-km gridcell, the value is given by the current NADM drought classification for that region is: | ||
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| Drought categories are coded as the following values in the images: | ||
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| * NoData Value = -1 = no drought or wet | ||
| * 0 = abnormal dry | ||
| * 1 = moderate drought | ||
| * 2 = severe drought | ||
| * 3 = extreme drought | ||
| * 4 = exceptional drought | ||
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| Additional [details can be found here](https://www.ncdc.noaa.gov/temp-and-precip/drought/nadm/) and information about this dataset is also available at [climate engine org](https://support.climateengine.org/article/73-nadm). | ||
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| #### Dataset details | ||
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| <center> | ||
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| | **Spatial extent** | North America | | ||
| |----------------------|-------------------------------------------| | ||
| | **Spatial resolution**| 2.5-km (0.025 deg) | | ||
| | **Temporal resolution**| Monthly | | ||
| | **Time span** | 2001-11-01 to present | | ||
| | **Update frequency** | Updated Monthly | | ||
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| </center> | ||
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| **Variables** | ||
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| <center> | ||
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| | Variable | Drought category ('nadm') | | ||
| |------------------------|--------------------------------------------| | ||
| | Units | Drought classification | | ||
| | Scale factor | 1.0 | | ||
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| </center> | ||
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| #### Citation | ||
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| ``` | ||
| Heim, Jr., R. R., 2002. A review of Twentieth-Century drought indices used in the United States. Bulletin of the American Meteorological Society, 83, 1149-1165. | ||
| Lawrimore, J., et al., 2002. Beginning a new era of drought monitoring across North America. Bulletin of the American Meteorological Society, 83, 1191-1192. | ||
| Lott, N., and T. Ross, 2000. NCDC Technical Report 2000-02, A Climatology of Recent Extreme Weather and Climate Events. [Asheville, N.C.]: National Climatic Data Center. | ||
| Svoboda, M., et al., 2002. The Drought Monitor. Bulletin of the American Meteorological Society, 83, 1181-1190. | ||
| ``` | ||
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| #### Earth Engine Snippet | ||
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| ```js | ||
| // Read in Image Collection and mosaic to single image | ||
| var nadm_ic = ee.ImageCollection('projects/climate-engine/nadm/monthly') | ||
| var nadm_i = nadm_ic.first() | ||
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| // Print image to see bands | ||
| print(nadm_i) | ||
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| // Visualize a single image | ||
| var nadm_palette = ["#ffffff", "#ffff00", "#fcd37f", "#ffaa00", "#e60000", "#730000"] | ||
| Map.addLayer(nadm_i, {min:-1, max:4, palette: nadm_palette}, 'nadm_i') | ||
| ``` | ||
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| Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/NADM-MONTHLY | ||
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| #### License | ||
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| NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site. | ||
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| Keywords: drought, NADM, North America, United States, Canada, Mexico | ||
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| Created & provided by: NOAA, NIDIS, NCEI | ||
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| Curated by: Climate Engine Org |
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| # Snow Data Assimilation System (SNODAS) | ||
| The Snow Data Assimilation System (SNODAS) represents a comprehensive modeling and data assimilation system meticulously developed by the National Operational Hydrologic Remote Sensing Center (NOHRSC). Its primary objective is to provide highly accurate estimations of snow cover and associated parameters, serving as a crucial resource for hydrologic modeling and analysis. SNODAS achieves this by assimilating data from a diverse array of sources, including satellite observations, ground-based measurements, and numerical weather prediction models. These disparate data streams undergo thorough processing within a snow mass and energy balance model, ultimately yielding estimations of snow water equivalent (SWE), snow depth, snow cover extent, and snow albedo. | ||
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| The SNODAS dataset boasts a spatial resolution of 1 km and a temporal resolution of 24 hours, ensuring precise and timely insights. Updated daily, the dataset encompasses the continental United States, Alaska, and Hawaii, offering comprehensive coverage for users across a spectrum of applications. SNODAS data caters to a wide-ranging audience, including water resource managers, emergency responders, and climate scientists. These invaluable data play a pivotal role in diverse applications, including estimating snowmelt runoff, forecasting snow avalanches, monitoring snowpack conditions for drought and flood management, and conducting studies on the influence of climate change on snow dynamics. SNODAS data is freely accessible through the National Snow and Ice Data Center (NSIDC), further enhancing its accessibility and utility for a broad user base. | ||
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| This dataset description provides a comprehensive overview of SNODAS, emphasizing its significance in supporting hydrologic research and decision-making across various domains. You can find [additional information here](https://nsidc.org/data/g02158) and you can also find link to the dataset in [climate engine org here](https://support.climateengine.org/article/44-snodas) | ||
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| #### Dataset details | ||
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| <center> | ||
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| | **Spatial extent** | Conterminous US | | ||
| |----------------------|--------------------------------------------| | ||
| | **Spatial resolution**| 1000 m (1/120-deg) | | ||
| | **Temporal resolution**| Daily | | ||
| | **Time span** | 2003-10-01 to present | | ||
| | **Update frequency** | Updated daily with 1 day lag | | ||
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| </center> | ||
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| **Variables** | ||
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| <center> | ||
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| | Variable | Units | Scale Factor | | ||
| |------------------------|-----------------|---------------| | ||
| | Snow Water Equivalent | Meters | 1.0 | | ||
| | Snow Depth | Meters | 1.0 | | ||
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| </center> | ||
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| #### Citation | ||
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| ``` | ||
| Barrett, Andrew. 2003. National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special | ||
| Report 11. Boulder, CO USA: National Snow and Ice Data Center. 19 pp. | ||
| Barrett, A. P., R. L. Armstrong, and J. L. Smith. 2001. The Snow Data Assimilation System (SNODAS): An overview. | ||
| Journal of Hydrometeorology 2(3):288-306. | ||
| ``` | ||
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| ### Earth Engine Snippet | ||
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| ```js | ||
| // Read in Image Collection and get image | ||
| var snodas_ic = ee.ImageCollection('projects/earthengine-legacy/assets/projects/climate-engine/snodas/daily') | ||
| var snodas_i = snodas_ic.filterDate('2022-01-01', '2022-01-05').first() | ||
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| // Print first image to see bands | ||
| print(snodas_i) | ||
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| // Visualize select bands from first image | ||
| var prec_palette = ["#ffffcc", "#c7e9b4", "#7fcdbb", "#41b6c4", "#1d91c0", "#225ea8", "#0c2c84"] | ||
| Map.addLayer(snodas_i.select('Snow_Depth'), {min: 0, max: 1, palette: prec_palette}, 'Snow_Depth') | ||
| Map.addLayer(snodas_i.select('SWE'), {min: 0, max: 1, palette: prec_palette}, 'SWE') | ||
| ``` | ||
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| Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:weather-climate/SNODAS-DAILY | ||
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| ### License | ||
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| NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use. The forgoing data is in the public domain and is being provided without restriction on use and distribution. For more information visit the NWS disclaimer site. | ||
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| Keywords: snow, climate, near real-time, CONUS, United States, NOAA, daily | ||
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| Created & provided by: NOAA, NSIDC | ||
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| Curated by: Climate Engine Org |
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