NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management.

The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.

Known Issues * Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. * High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs. * The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

Historic Land Cover Change, Area Vegetation, HH time_bounds Dimensioned By time, bounds_dim. _CoordSysBuilder=ucar.nc2.dataset.conv.CF1Convention acknowledgement=This work was supported by National Aeronautics and Space Administration (NASA) Land Cover and Land Use Change Program (No. NNX08AK75G) cdm_data_type=Grid Comment=Cropland and Pastureland Data Source used in this netcdf file: Houghton (2008) - Based on deforestation rates from FAO (2005) contributor_role=Principle Investigator and originator Conventions=CF-1.4 data_set_progress=complete history=FMRC Best Dataset id=land-cover_hh_landcover_yr2004.nc infoUrl=https://www.ncei.noaa.gov/thredds/catalog/ncFC/sat/landcover-HHAREAVEG-fc/catalog.html?dataset=ncFC/sat/landcover-HHAREAVEG-fc/Historical_Land-Cover_Change_and_Land-Use_Conversions_Global_Dataset:_HH_AREAVEG_Feature_Collection_best.ncd institution=University of Illinois at Urbana-Champaign Keywords=EARTH SCIENCE > AGRICULTURE > FOREST SCIENCE > AFFORESTATION/REFORESTATION, EARTH SCIENCE > AGRICULTURE > FOREST SCIENCE > REFORESTATION, EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > AGRICULTURAL LANDS, EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > DESERTS, EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > FORESTS, EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > GRASSLANDS, EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > SHRUBLAND/SCRUB, EARTH SCIENCE > BIOSPHERE > TERRESTRIAL ECOSYSTEMS > URBAN LANDS, EARTH SCIENCE > BIOSPHERE > VEGETATION > AFFORESTATION/REFORESTATION, EARTH SCIENCE > BIOSPHERE > VEGETATION > DECIDUOUS VEGETATION, EARTH SCIENCE > BIOSPHERE > VEGETATION > DOMINANT SPECIES, EARTH SCIENCE > BIOSPHERE > VEGETATION > EVERGREEN VEGETATION, EARTH SCIENCE > BIOSPHERE > VEGETATION > VEGETATION COVER, EARTH SCIENCE > BIOSPHERE > VEGETATION > REFORESTATION, EARTH SCIENCE > HUMAN DIMENSIONS > HUMAN SETTLEMENTS > URBAN AREAS, EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND COVER, EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND USE CLASSES, EARTH SCIENCE > HUMAN DIMENSIONS > ENVIRONMENTAL GOVERNANCE/MANAGEMENT > LAND MANAGEMENT > LAND USE CLASSES, EARTH SCIENCE > HUMAN DIMENSIONS > ENVIRONMENTAL GOVERNANCE/MANAGEMENT > LAND MANAGEMENT > LAND USE/LAND COVER CLASSIFICATION, EARTH SCIENCE > LAND SURFACE > LANDSCAPE > REFORESTATION keywords_vocabulary=NASA Global Change Master Directory (GCMD) Earth Science Keywords, Version 8.0 location=Proto fmrc:Historical_Land-Cover_Change_and_Land-Use_Conversions_Global_Dataset:_HH_AREAVEG_Feature_Collection metadata_link=gov.noaa.ncdc:C00814 naming_authority=gov.noaa.ncdc references=https://dx.doi.org/10.1007/s11707-012-0314-2, https://dx.doi.org/10.1111/gcb.12207 source=model sourceUrl=https://www.ncei.noaa.gov/thredds/dodsC/ncFC/sat/landcover-HHAREAVEG-fc/Historical_Land-Cover_Change_and_Land-Use_Conversions_Global_Dataset:_HH_AREAVEG_Feature_Collection_best.ncd spatial_coverage=Global spatial_domain=land only; all ocean grid cells have been filled with the specified missing values spatial_resolution=0.5x0.5 degrees lat/lon standard_name_vocabulary=CF Standard name Table (v25, 05 July 2013) time_coverage_duration=one year time_coverage_end=2006-01-01T00:00:00Z time_coverage_resolution=yearly time_coverage_start=1771-01-01T00:00:00Z vertical_levels=surface observations years_of_record=1770/01 -2005/12

NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Land Cover Mapping and Estimation (GLanCE) annual 30 meter (m) Version 1 data product provides global land cover and land cover change data derived from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI). These maps provide the user community with land cover type, land cover change, metrics characterizing the magnitude and seasonality of greenness of each pixel, and the magnitude of change. GLanCE data products will be provided using a set of seven continental grids that use Lambert Azimuthal Equal Area projections parameterized to minimize distortion for each continent. Currently, North America, South America, Europe, and Oceania are available. This dataset is useful for a wide range of applications, including ecosystem, climate, and hydrologic modeling; monitoring the response of terrestrial ecosystems to climate change; carbon accounting; and land management. The GLanCE data product provides seven layers: the land cover class, the estimated day of year of change, integer identifier for class in previous year, median and amplitude of the Enhanced Vegetation Index (EVI2) in the year, rate of change in EVI2, and the change in EVI2 median from previous year to current year. A low-resolution browse image representing EVI2 amplitude is also available for each granule.Known Issues Version 1.0 of the data set does not include Quality Assurance, Leaf Type or Leaf Phenology. These layers are populated with fill values. These layers will be included in future releases of the data product. * Science Data Set (SDS) values may be missing, or of lower quality, at years when land cover change occurs. This issue is a by-product of the fact that Continuous Change Detection and Classification (CCDC) does not fit models or provide synthetic reflectance values during short periods of time between time segments. * The accuracy of mapping results varies by land cover class and geography. Specifically, distinguishing between shrubs and herbaceous cover is challenging at high latitudes and in arid and semi-arid regions. Hence, the accuracy of shrub cover, herbaceous cover, and to some degree bare cover, is lower than for other classes. * Due to the combined effects of large solar zenith angles, short growing seasons, lower availability of high-resolution imagery to support training data, the representation of land cover at land high latitudes in the GLanCE product is lower than in mid latitudes. * Shadows and large variation in local zenith angles decrease the accuracy of the GLanCE product in regions with complex topography, especially at high latitudes. * Mapping results may include artifacts from variation in data density in overlap zones between Landsat scenes relative to mapping results in non-overlap zones. * Regions with low observation density due to cloud cover, especially in the tropics, and/or poor data density (e.g. Alaska, Siberia, West Africa) have lower map quality. * Artifacts from the Landsat 7 Scan Line Corrector failure are occasionally evident in the GLanCE map product. High proportions of missing data in regions with snow and ice at high elevations result in missing data in the GLanCE SDSs.* The GlanCE data product tends to modestly overpredict developed land cover in arid regions.

Dataset links to the Digital Collections of Colorado, DSpace Repository. From the homepage, you can search the 1240 datasets hosted there, or browse using a list of filters on the right. DSpace is a digital service that collects, preserves, and distributes digital material. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/ShortgrassSteppe_eaa_2015_March_19_1220

The Millennium Ecosystem Assessment: MA Rapid Land Cover Change provides data and information on global and regional land cover change in raster format for agriculture (cropland increase and disease), deforestation (forest mask and hotspots), desertification (hotspot areas of degraded land and degradation types), and fires (most frequent and exceptional fires). Urbanization data are in vector format for cities with the highest rate of change (1995-2015), largest cities (2000), and the overlay of these two data layers for the year 2000. This assessment identified the need to synthesize what is known about areas of rapid land cover change around the world in order to evaluate how the provision of ecosystem goods and services has changed.

Culminating more than four years of processing data, NASA and the National Geospatial-Intelligence Agency (NGA) have completed Earth's most extensive global topographic map. The mission is a collaboration among NASA, NGA, and the German and Italian space agencies. For 11 days in February 2000, the space shuttle Endeavour conducted the Shuttle Radar Topography Mission (SRTM) using C-Band and X-Band interferometric synthetic aperture radars to acquire topographic data over 80% of the Earth's land mass, creating the first-ever near-global data set of land elevations. This data was used to produce topographic maps (digital elevation maps) 30 times as precise as the best global maps used today. The SRTM system gathered data at the rate of 40,000 per minute over land. They reveal for the first time large, detailed swaths of Earth's topography previously obscured by persistent cloudiness. The data will benefit scientists, engineers, government agencies and the public with an ever-growing array of uses. The SRTM radar system mapped Earth from 56 degrees south to 60 degrees north of the equator. The resolution of the publicly available data is three arc-seconds (1/1,200th of a degree of latitude and longitude, about 295 feet, at Earth's equator). The final data release covers Australia and New Zealand in unprecedented uniform detail. It also covers more than 1,000 islands comprising much of Polynesia and Melanesia in the South Pacific, as well as islands in the South Indian and Atlantic oceans. SRTM data are being used for applications ranging from land use planning to "virtual" Earth exploration. Currently, the mission's homepage "http://www.jpl.nasa.gov/srtm" provides direct access to recently obtained earth images. The Shuttle Radar Topography Mission C-band data for North America and South America are available to the public. A list of complete public data set is available at "http://www2.jpl.nasa.gov/srtm/dataprod.htm" The data specifications are within the following parameters: 30-meter X 30-meter spatial sampling with 16 meter absolute vertical height accuracy, 10-meter relative vertical height accuracy, and 20-meter absolute horizontal circular accuracy. From the JPL Mission Products Summary, "http://www.jpl.nasa.gov/srtm/dataprelimdescriptions.html". The primary products of the SRTM mission are the digital elevation maps of most of the Earth's surface. Visualized images of these maps are available for viewing online. Below you will find descriptions of the types of images that are being generated:

• Radar Image
• Radar Image with Color as Height
• Radar Image with Color Wrapped Fringes
  -Shaded Relief
• Perspective View with B/W Radar Image Overlaid
• Perspective View with Radar Image Overlaid, Color as Height
• Perspective View of Shaded Relief
• Perspective View with Landsat or other Image Overlaid
• Contour Map - B/W with Contour Lines
• Stereo Pair
• Anaglypgh

The SRTM radar contained two types of antenna panels, C-band and X-band. The near-global topographic maps of Earth called Digital Elevation Models (DEMs) are made from the C-band radar data. These data were processed at the Jet Propulsion Laboratory and are being distributed through the United States Geological Survey's EROS Data Center. Data from the X-band radar are used to create slightly higher resolution DEMs but without the global coverage of the C-band radar. The SRTM X-band radar data are being processed and distributed by the German Aerospace Center, DLR.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses as input to control the simulated land fields ERA5 atmospheric variables, such as air temperature and air humidity. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'.
The ERA5-Land dataset, as any other simulation, provides estimates which have some degree of uncertainty. Numerical models can only provide a more or less accurate representation of the real physical processes governing different components of the Earth System. In general, the uncertainty of model estimates grows as we go back in time, because the number of observations available to create a good quality atmospheric forcing is lower. ERA5-land parameter fields can currently be used in combination with the uncertainty of the equivalent ERA5 fields. The temporal and spatial resolutions of ERA5-Land makes this dataset very useful for all kind of land surface applications such as flood or drought forecasting. The temporal and spatial resolution of this dataset, the period covered in time, as well as the fixed grid used for the data distribution at any period enables decisions makers, businesses and individuals to access and use more accurate information on land states.

This data evaulates rates of land-use change from natural to non-natural landcover classes (and vice versa) for 1200km2 hexbins in southern Canada.

The purpose of this data is to evaluate the potential additionality of protecting ecosystem carbon in southern Canada (agricultural region of Canada). This approach serves as a coarse filter for assessing additionality by applying the rate of landcover change within a defined area to the amount of carbon currently stored within the area. If the area were to be protected, the additional benefit can be calculated using both this dataset and our National Carbon Stocks dataset.

Rates of landcover change were calculated using data from the Earth Observation Team of the Science and Technology Branch at Agriculture and Agri-Food Canada at a 30m resolution. The timeframe spans 2011-2020 inclusive (10 years).

This data release contains a single vector shapefile and two text documents with code used to generate the data product. This vector shapefile contains the locations of 365 “plugged and abandoned” well sites from across the Colorado Plateau with their respective relative fractional vegetation cover (RFVC) values. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive (collectively termed “plugged and abandoned”). Understanding the rates, controls, and degree of recovery of these reclaimed well sites (well pads) to a state similar to pre-development conditions is critical for energy development and land management decision processes. We used the Soil-Adjusted Total Vegetation Index (SATVI) to measure post-abandonment vegetation cover relative to pre-drilling condition as a metric of recovery: relative fractional vegetation cover (RFVC). The Google Earth Engine cloud computing platform allows for the automated processing of hundreds of images for each of the hundreds of sites, permitting time series analyses that were not easily achieved with earlier image processing methods. The time-series package BFAST in R statistical software enables the efficient detection of breaks in temporal trends, helping to identify when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The code text documents include: 1) Google Earth Engine Script: Well Pad Means, Medians, and DART Percentile Time Series Collection 2) R Script: Generation of BFAST time series models and calculation of RFVC The Google Earth Engine and R code used for data processing, and the final shapefile were used for statistical analysis in the following paper: Waller, E.K., Villarreal, M.L., Poitras, T.B., Nauman, T.W., Duniway, M.C. 2018. Landsat time series analysis of fractional plant cover changes on abandoned energy development sites. International Journal of Applied Earth Observation and Geoinformation 10.1016/j.jag.2018.07.008

GEE-TED: A tsetse ecological distribution model for Google Earth Engine Associated publication forthcoming: Fox, L., Peter, B. G., Frake, A. N., and Messina, J. P. (Forthcoming). A Bayesian maximum entropy model for predicting tsetse ecological distributions. Journal TBD. Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (Forthcoming): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (Forthcoming) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (Forthcoming) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/... Visit https://dataone.org/datasets/sha256%3A3695619598269618f05611b802adc5f0e04bc7317cfecc7fcd6bc2536f881776 for complete metadata about this dataset.

Based on time series of Landsat images, this study uses the Google Earth Engine cloud platform to extract built-up land in the Beijing-Tianjin-Hebei region, and integrates the results with GlobeLand30, GHS-Built, GAIA and GLC_FCS-2020 land cover products to derive the built-up land data set during the period 2000-2020 in the region. An overall accuracy higher than 90% was obtained. Based on this data set, the SDG 11.3.1 indicators-land consumption rate(LCR), population growth rate(PGR) and ratio of land consumption rate to population growth rate(LCRPGR) were calculated for each city.

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at https://github.com/parallelworks/sl-archive-whondrs. A static copy of the GitHub repository is included in this data package as an archived version at the time of publishing this data package (March 2023). However, we recommend accessing these files via GitHub for full functionality. Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

The Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Data Catalog is a collection of data resources, including data sets, aerial photos, satellite imagery, GIS data, and national LTER network-wide data. You are welcome to examine and use the data as you wish for research and educational needs. However, data are copyrighted and use in a publication requires permission as detailed in KBS LTER's terms of use, which can be found at http://lter.kbs.msu.edu/data/terms-of-use/. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/KellogBioStation_eaa_2015_March_19_1317

New global sub-daily meteorological forcing data are provided for use with land surface and hydrological-models. The data are derived from the ERA-40 reanalysis product via sequential interpolation to half-degree resolution, elevation correction and monthly-scale adjustments based on CRU (corrected-temperature, diurnal temperature range, cloud-cover) and GPCC (precipitation) monthly observations combined with new corrections for varying atmospheric aerosol-loading and separate precipitation gauge corrections for rainfall and snowfall. The WATCH Forcing data is a twentieth century meteorological forcing dataset for land surface and hydrological models. It consists of three of 6-hourly states of the weather for global half-degree land grid points. It was generated as part of the EU FP 6 project "WATCH" (WATer and global CHange") which ran from 2007-2011. The data was generated in 2 time periods with slightly different methodology: 1901-1957 and 1958-2001, but generally the dataset can be considered as continuous. More details regarding the generation process can be found in the associated WATCH technical report and paper in J. Hydrometeorology. The data covers land points only and excludes the Antarctica.

Most of us understand the hydrologic cycle in terms of the visible paths that water can take such as rainstorms, rivers, waterfalls and lakes. However, an even larger volume of water flows through the air all around us in two invisible paths: evaporation and transpiration. These two paths together are referred to as evapotranpsiration (ET), and claim 61% of all terrestrial precipitation. Solar radiation, air temperature, wind speed, soil moisture, and land cover all affect the rate of evapotranspiration, which is a major driver of the global water cycle, and key component of most catchments' water budget. This map contains a historical record showing the volume of water lost to evapotranspiration during each month from March 2000 to the present.Dataset SummaryThe GLDAS Evapotranspiration layer is a time-enabled image service that shows total actual evapotranspiration monthly from 2000 to the present, measured in millimeters of water loss. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS for Desktop. It is useful for scientific modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Air temperature lapse rates vary geographically and temporally. Sub-Antarctic Macquarie Island provides an opportunity to compare lapse rates between windward and leeward slopes in a hyper-oceanic climate. Development of orographic cloud is expected to modify lapse rates, given the theoretical shift between dry and saturated adiabatic lapse rates that occurs with condensation of water vapour. This dataset is part of a PhD project examining vegetation patterns and drivers on Macquarie Island. Data loggers were placed along an east-west altitudinal transect across the narrow axis of Macquarie Island to record air temperature from August 2014 to March 2016.A random sample of digital photographs from the AAD webcam at Macquarie Island Station was used to classify cloud base level as observed from the Station. This dataset includes air temperature data from LogTag loggers, analysis of near surface atmospheric lapse rates, observations of cloud cover from webcam images and relevant data supplied by Bureau of Meteorology used in analysis.

Reference: Fitzgerald, N. B., and Kirkpatrick, J. B. (2020). Air temperature lapse rates and cloud cover in a hyper-oceanic climate. Antarctic Science, 14. https://doi.org/10.1017/S0954102020000309

U.S.V.I. relative erosion rate by land cover type (1900)

This dataset links to the Jornada Basin LTER data homepage. The Jornada Basin Long Term Ecological Research Program (JRN LTER) has been investigating desertification processes since 1982. Jornada research topics include: desertification; nonlinear dynamics; threshold behavior; cross-scale interactions; cascading events; ecosystem indicators and vegetation dynamics; geomorphology and wind; ecohydrology; animal interactions; factors affecting primary production; animal-induced soil disturbances; direct and indirect consumer effects; vertebrate and invertebrate population dynamics; and grazing effects on ecosystem structure and function. Resources in this dataset:Resource Title: Jornada Basin LTER Data Catalog. File Name: Web Page, url: https://lter.jornada.nmsu.edu/data-catalog/

The Suomi National Polar-Orbiting Partnership (Suomi NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) Land Cover Dynamics data product provides global land surface phenology (GLSP) metrics at yearly intervals. The VNP22Q2 data product is derived from time series of the two-band Enhanced Vegetation Index-2 (EVI2) calculated from VIIRS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR). Vegetation phenology metrics at 500 meter spatial resolution are identified for up to two detected growing cycles per year.

Provided in each VNP22Q2 product are 19 Science Dataset (SDS) layers. The product contains six phenological transition dates: onset of greenness increase, onset of greenness maximum, onset of greenness decrease, onset of greenness minimum, dates of mid-greenup, and senescence phases. The product also includes the growing season length. The greenness related metrics consist of EVI2 onset of greenness increase, EVI2 onset of greenness maximum, EVI2 growing season, rate of greenness increase and rate of greenness decrease. The confidence of phenology detection is provided as greenness agreement growing season, proportion of good quality (PGQ) growing season, PGQ onset greenness increase, PGQ onset greenness maximum, PGQ onset greenness decrease, and PGQ onset greenness minimum. The final layer is quality control specifying the overall quality of the product. A low-resolution browse image showing greenup is also available when viewing each VNP22Q2 granule.

Here we provide an archive of Modèle Atmosphérique Regionale v. 3.5.2 simulations over Greenland. This archive contains daily data at 20-kilometer resolution with lateral boundary forcings from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis model. Data are provided in netCDF format, where information of all variables and their units are provided within the netCDF files. See method section for more detailed description of the data. Citation: If this dataset is used in any publication, this data repository and Fettweis et al. 2017 should be cited. Contact Xavier Fettweis for obtaining more recent MAR based simulations (see contact information in metadata). List of variables in the dataset (this list is also provided in the data_variables.csv file) Variable, Variable description, Units TIME, time, hours since 1901-01-15 00:00:00 X12_84, X coordinate, km Y21_155,Y coordinate, km LON, Longitude, degrees LAT, Latitude, degrees SH, Surface Height, m SRF, Surface Type, - SOL, Soil Type, - SLO, Surface Slope, - CZ, Cosine of Solar Zenith Angle, - SAL, Soil Albedo, - VEG, Vegetation Type Index, - MSK, Ice Sheet Area, - FRV, Vegetation Class Coverage, % FRA, Surface fraction:sic or land, % SHSN0, Ini. old firn/ice thickness , m SHSN2, Snow Pack Height above Ice, m SHSN3, Snow Pack Height Total, m SMB, Surface Mass Balance (SMB~SF+RF-RU-SU-SW), mmWE/day SU, Sublimation and evaporation, mmWE/day ME, Meltwater production, mmWE/day RZ, Meltwater refreezing and deposition, mmWE/day SW, Surface Water, mmWE/day SF, Snowfall, mmWE/day RF, Rainfall, mmWE/day RU, Run-off of meltwater and rain water, mmWE/day CP, Convective precipitation, mmWE/day UU, x-Wind Speed component, m/s VV, y-Wind Speed component, m/s TT, Temperature, C ZZ, Model Surface Height, m QQ, Specific Humidity, g/kg UV, Wind Speed, m/s RH, Relative Humidity, % UUP, x-Wind Speed component, m/s VVP, y-Wind Speed component, m/s TTP, Temperature, C ZZP, Height, m QQP, Specific Humidity, g/kg UUZ, x-Wind Speed component, m/s VVZ, y-Wind Speed component, m/s TTZ, Temperature, C QQZ, Specific Humidity, g/kg UVZ, Horizontal Wind Speed, m/s TTMIN, Min Temp, C TTMAX, Max Temp, C SP, Surface Pressure, hPa UVMAX, Maximum Wind Speed, m/s TTH, Hourly Temperature, C QQH, Hourly Specific Humidity, g/kg UUH, Hourly x-Wind Speed component, m/s VVH, Hourly y-Wind Speed component, m/s SWDH, Hourly Short Wave Downward, w/m2 LWDH, Hourly Long Wave Downward, W/m2 LWUH, Hourly Long Wave Upward, W/m2 SPH, Hourly Surface Pressure, hPa SHFH, Hourly Sensible Heat Flux, W/m2 LHFH, Hourly Latent Heat Flux, W/m2 ALH, Hourly Albedo, - CCH, Hourly Cloud Cover, - SWD, Short Wave Downward, W/m2 LWD, Long Wave Downward, W/m2 LWU, Long Wave Upward, W/m2 SHF, Sensible Heat Flux, W/m2 LHF, Latent Heat Flux, W/m2 AL1, Albedo (Tot Refl/Tot Inc), - AL2, Albedo, - AL, Albedo, - QW, Cloud Dropplets Concent., kg/kg QI, Cloud Ice Crystals Concent., kg/kg QS, Cloud Snow Flakes Concent., kg/kg QR, Cloud Rain Concentration, kg/kg CC, Cloud Cover, - COD, Cloud Optical Depth, - CU, Cloud Cover (up), - CM, Cloud Cover (Middle), - CD, Cloud Cover (down), - R0, Roughness length for Heat, m Z0, Roughness length for Moment., m PBL, Height of Bound. Layer (2val.), m WVP, Water Vapour Path, kg/m2 IWP, Ice Water Path, kg/m2 CWP, Condensed Water Path, kg/m2 ST, Surface Temperature, C ST2, Surface Temperature, C PDD, Postive Degree Day, C SWSN, Surficial Water Specific Mass, mmWE RO1, Snow Density, kg/m3 TI1, Ice/Snow Temperature, C WA1, Liquid Water Content, kg/kg G11, g1 (Dendri./Spheri.), - G21, g2 (Sphericity/Size), - References: Fettweis X, Box JE, Agosta C, Amory C, Kittel C, Lang C, van As D, Machguth H and Gallée H (2017) Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR (Modèle Atmosphérique Régional) model. The Cryosphere 11(2), 1015–1033 (doi:10.5194/tc-11-1015-2017)