OFFL/L3_CLOUD This dataset provides offline high-resolution imagery of cloud parameters. The TROPOMI/S5P cloud properties retrieval is based on the OCRA and ROCINN algorithms currently being used in the operational GOME and GOME-2 products. OCRA retrieves the cloud fraction using measurements in the UV/VIS spectral regions and ROCINN retrieves the cloud height (pressure) and optical thickness (albedo) using measurements in and around the oxygen A-band at 760 nm. Version 3.0 of the algorithms are used, which are based on a more realistic treatment of clouds as optically uniform layers of light-scattering particles. Additionally, the cloud parameters are also provided for a cloud model which assumes the cloud to be a Lambertian reflecting boundary. More information. OFFL L3 Product To make our OFFL L3 products, we find which areas within the product's bounding box contain data by using a command like this: harpconvert --format hdf5 --hdf5-compression 9 -a 'cloud_fraction>50;derive(datetime_stop {time})' S5P_OFFL_L2_CLOUD_20180705T095218_20180705T113348_03760_01_010000_20180712T082510.nc grid_info.h5 We then merge all the data into one large mosaic (area-averaging values for pixels that may have different values for different times). From the mosaic, we create a set of tiles containing orthorectified raster data. Example harpconvert invocation for one tile: harpconvert --format hdf5 --hdf5-compression 9 -a 'cloud_fraction>50;derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(cloud_fraction,cloud_top_pressure,cloud_top_height, cloud_base_pressure,cloud_base_height,cloud_optical_depth,surface_albedo, sensor_azimuth_angle,sensor_zenith_angle,solar_azimuth_angle, solar_zenith_angle)' S5P_OFFL_L2_CLOUD_20180705T095218_20180705T113348_03760_01_010000_20180712T082510.nc output.h5 Sentinel-5 Precursor Sentinel-5 Precursor is a satellite launched on 13 October 2017 by the European Space Agency to monitor air pollution. The onboard sensor is frequently referred to as Tropomi (TROPOspheric Monitoring Instrument). All of the S5P datasets, except CH4, have two versions: Near Real-Time (NRTI) and Offline (OFFL). CH4 is available as OFFL only. The NRTI assets cover a smaller area than the OFFL assets, but appear more quickly after acquisition. The OFFL assets contain data from a single orbit (which, due to half the earth being dark, contains data only for a single hemisphere). Because of noise in the data, negative vertical column values are often observed in particular over clean regions or for low SO2 emissions. It is recommended not to filter these values except for outliers, i.e. for vertical columns lower than -0.001 mol/m^2. The original Sentinel 5P Level 2 (L2) data is binned by time, not by latitude/longitude. To make it possible to ingest the data into Earth Engine, each Sentinel 5P L2 product is converted to L3, keeping a single grid per orbit (that is, no aggregation across products is performed). Source products spanning the antimeridian are ingested as two Earth Engine assets, with suffixes _1 and _2. The conversion to L3 is done by the harpconvert tool using the bin_spatial operation. The source data is filtered to remove pixels with QA values less than: 80% for AER_AI 75% for the tropospheric_NO2_column_number_density band of NO2 50% for all other datasets except for O3 and SO2 The O3_TCL product is ingested directly (without running harpconvert).

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

CANUE staff developed annual estimates of maximum mean warm-season land surface temperature (LST) recorded by LandSat 8 at 30m resolution. To reduce the effect of missing data/cloud cover/shadows, the highest mean warm-season value reported over three years was retained - for example, the data for 2021 represent the maximum of the mean land surface temperature at a pixel location between April 1st and September 30th in 2019, 2020 and 2021. Land surface temperature was calculated in Google Earth Engine, using a public algorithm (see supplementary documentation). In general, annual mean LST may not reflect ambient air temperatures experienced by individuals at any given time, but does identify areas that are hotter during the day and therefore more likely to radiate excess heat at night - both factors that contribute to heat islands within urban areas.

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high���resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user���s and producer���s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).

This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone country in Southeast Asia lacking a comprehensive stream gauging network. Each file includes 372 bands. Bands represent surface water for each month from 1988 to 2018, and are stacked from oldest (Band 1 - January 1988) to newest (Band 372 - December 2018). DSWE classifies pixels unobscured by cloud, cloud shadow, or snow into five categories of ground surface inundation; in addition to not-water (class 0) and water (class 1), the DSWE algorithm distinguishes pixels that are less distinctly inundated (class 2: “moderate confidence”), comprise a mixture of vegetation and water (class 3: “potential wetland”), or are of marginal validity (class 4: “water or wetland - low confidence”). Class 9 is applied to classify clouds, shadows and hill shade. Two additional documents accompany the raster image files and XML metadata. The first provides a key representing the general location of each raster file. The second file includes all Google Earth Engine Javascript code, which can be used online (https://code.earthengine.google.com/) to replicate the monthly DSWE map time series for Cambodia, or for any other location on Earth. The code block includes comments to explain how each step works. These data support the following publication: These data support the following publication: Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing: Remote Sensing, v. 12, no. 6, p. 984, https://doi.org/10.3390/rs12060984.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The Sentinel-2 data contain 13 UINT16 spectral bands representing TOA reflectance scaled by 10000. See the Sentinel-2 User Handbook for details. QA60 is a bitmask band that contained rasterized cloud mask polygons until Feb 2022, when these polygons stopped being produced. Starting in February 2024, legacy-consistent QA60 bands are constructed from the MSK_CLASSI cloud classification bands. For more details, see the full explanation of how cloud masks are computed.. Each Sentinel-2 product (zip archive) may contain multiple granules. Each granule becomes a separate Earth Engine asset. EE asset ids for Sentinel-2 assets have the following format: COPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the first numeric part represents the sensing date and time, the second numeric part represents the product generation date and time, and the final 6-character string is a unique granule identifier indicating its UTM grid reference (see MGRS). The Level-2 data produced by ESA can be found in the collection COPERNICUS/S2_SR. For datasets to assist with cloud and/or cloud shadow detection, see COPERNICUS/S2_CLOUD_PROBABILITY and GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED. For more details on Sentinel-2 radiometric resolution, see this page.

This dataset contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 7 ETM+ sensor. These images contain 4 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and one thermal infrared (TIR) band processed to orthorectified surface temperature. They also contain intermediate bands used in calculation of the ST products, as well as QA bands. Landsat 7 SR products are created with the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm (version 3.4.0). All Collection 2 ST products are created with a single-channel algorithm jointly created by the Rochester Institute of Technology (RIT) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Strips of collected data are packaged into overlapping "scenes" covering approximately 170km x 183km using a standardized reference grid. Some assets have only SR data, in which case ST bands are present but empty. For assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'. For assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'. Additional documentation and usage examples. Data provider notes: Data products must contain both optical and thermal data to be successfully processed to surface temperature, as ASTER NDVI is required to temporally adjust the ASTER GED product to the target Landsat scene. Therefore, night time acquisitions cannot be processed to surface temperature. A known error exists in the surface temperature retrievals relative to clouds and possibly cloud shadows. The characterization of these issues has been documented by Cook et al., (2014). ASTER GED contains areas of missing mean emissivity data required for successful ST product generation. If there is missing ASTER GED information, there will be missing ST data in those areas. The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. While this dataset has a global spatial extent, there are areas missing mean emissivity information due to persistent cloud contamination in the ASTER measurements. The USGS further screens unphysical values (emissivity < 0.6) in ASTER GED to remove any emissivity underestimation due to undetected clouds. For any given pixel with no ASTER GED input or unphysical emissivity value, the resulting Landsat ST products have missing pixels. The missing Landsat ST pixels will be consistent through time (1982-present) given the static nature of ASTER GED mean climatology data. For more information refer to landsat-collection-2-surface-temperature-data-gaps-due-missing Note that Landsat 7's orbit has been drifting to an earlier acquisition time since 2017.

This dataset provides high quality Climate Data Record (CDR) of multiple cloud properties along with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres Extended (PATMOS-x) brightness temperatures and reflectances. These data have been fitted to a 0.1 x 0.1 equal angle-grid with both ascending and descending assets generated daily from two to ten NOAA and MetOp satellite passes per day. This dataset includes 48 bands, 11 of which are deemed CDR quality (marked with "CDR variable" in the band list). The cloud products are derived using the ABI (Advanced Baseline Imager) Cloud Height Algorithm (ACHA), and the Daytime Cloud Optical Properties (DCOMP) algorithm. For more detail on the processing see the Climate Algorithm Theoretical Basis Document (C-ATBD).

Top of Atmosphere (TOA) reflectance data in bands from the USGS Landsat 5 and Landsat 8 satellites were accessed via Google Earth Engine. CANUE staff used Google Earth Engine functions to create cloud free annual composites, and mask water features, then export the resulting band data. NDVI indices were calculated as (band 4 - Band 3)/(Band 4 Band 3) for Landsat 5 data, and as (band 5 - band 4)/(band 5 Band 4) for Landsat 8 data. These composites are created from all the scenes in each annual period beginning from the first day of the year and continuing to the last day of the year. No data were available for 2012, due to decommissioning of Landsat 5 in 2011 prior to the start of Landsat 8 in 2013. No cross-calibration between the sensors was performed, please be aware there may be small bias differences between NDVI values calculated using Landsat 5 and Landsat 8. Final NDVI metrics were linked to all 6-digit DMTI Spatial single link postal code locations in Canada, and for surrounding areas within 100m, 250m, 500m, and 1km.

Links:

• Sentinel-2: Cloud Probability in Earth Engine's Public Data Catalog
• Sentinel-2: Cloud Probability in Earth Engine STAC viewed with STAC Browser

The S2 cloud probability is created with the sentinel2-cloud-detector library (using LightGBM). All bands are upsampled using bilinear interpolation to 10m resolution before the gradient boost base algorithm is applied. The resulting 0..1 floating point probability is scaled to 0..100 and stored as a UINT8. Areas missing any or all of the bands are masked out. Higher values are more likely to be clouds or highly reflective surfaces (e.g. roof tops or snow).

Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas.

The Level-2 data can be found in the collection COPERNICUS/S2_SR. The Level-1B data can be found in the collection COPERNICUS/S2. Additional metadata is available on assets in those collections.

See this tutorial explaining how to apply the cloud mask.

Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. The proposed approach combines multi-source satellite imagery for accurate land cover classification in a fragmented municipal territory in Southern Italy over a 5-month vegetative period. Within the GEE platform, a set of Sentinel-1, Sentinel-2, and Landsat 8 data was acquired and processed to generate a land cover map for the 2021 greenness period. A supervised pixel-based classification was performed, using a Random Forest (RF) machine learning algorithm, to classify the imagery and derived spectral indices into eight land cover classes. Classification accuracy was assessed using Overall Accuracy (OA), Producer’s and User’s accuracies (PA, UA), and F-score. McNemar’s test was applied to assess the significance of difference between classification results. The optical integrated datasets in combination with SAR data and derivate indices (NDVI, GNDVI, NDBI, VHVV) produce the most accurate LULC map among those produced (OA: 89.64%), while SAR-only datasets performed the lowest accuracy (OA: 61.30%). The classification process offers several advantages, including widespread spectral information, SAR’s ability to capture almost all-weather, day-and-night imagery, and the computation of vegetation indices in the near infrared spectrum interval, in a short revisit time. The proposed digital techniques for processing multi-temporal satellite data provide useful tools for understanding territorial and environmental dynamics, supporting decision-making in land use planning, agricultural expansion, and environmental management in fragmented landscapes.

Land use/land cover (LULC) mapping in fragmented landscapes, characterized by multiple and small land uses, is still a challenge. This study aims to evaluate the effectiveness of Synthetic Aperture Radar (SAR) and multispectral optical data in land cover mapping using Google Earth Engine (GEE), a cloud computing platform allowing big geospatial data analysis. The proposed approach combines multi-source satellite imagery for accurate land cover classification in a fragmented municipal territory in Southern Italy over a 5-month vegetative period. Within the GEE platform, a set of Sentinel-1, Sentinel-2, and Landsat 8 data was acquired and processed to generate a land cover map for the 2021 greenness period. A supervised pixel-based classification was performed, using a Random Forest (RF) machine learning algorithm, to classify the imagery and derived spectral indices into eight land cover classes. Classification accuracy was assessed using Overall Accuracy (OA), Producer’s and User’s accuracies (PA, UA), and F-score. McNemar’s test was applied to assess the significance of difference between classification results. The optical integrated datasets in combination with SAR data and derivate indices (NDVI, GNDVI, NDBI, VHVV) produce the most accurate LULC map among those produced (OA: 89.64%), while SAR-only datasets performed the lowest accuracy (OA: 61.30%). The classification process offers several advantages, including widespread spectral information, SAR’s ability to capture almost all-weather, day-and-night imagery, and the computation of vegetation indices in the near infrared spectrum interval, in a short revisit time. The proposed digital techniques for processing multi-temporal satellite data provide useful tools for understanding territorial and environmental dynamics, supporting decision-making in land use planning, agricultural expansion, and environmental management in fragmented landscapes.

Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD.

"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".

CLCD in 2022 is now available.

1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the Collection 2 SR data to update the CLCD.

2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check here for more details.

3. Internal overviews and color tables are built into each file to speed up software loading and rendering.

This dataset contains atmospherically corrected surface reflectance and land surface temperature derived from the data produced by the Landsat 9 OLI/TIRS sensors. These images contain 5 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and one thermal infrared (TIR) band processed to orthorectified surface temperature. They also contain intermediate bands used in calculation of the ST products, as well as QA bands. Landsat 9 SR products are created with the Land Surface Reflectance Code (LaSRC). All Collection 2 ST products are created with a single-channel algorithm jointly created by the Rochester Institute of Technology (RIT) and National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Strips of collected data are packaged into overlapping "scenes" covering approximately 170km x 183km using a standardized reference grid. Some assets have only SR data, in which case ST bands are present but empty. For assets with both ST and SR bands, 'PROCESSING_LEVEL' is set to 'L2SP'. For assets with only SR bands, 'PROCESSING_LEVEL' is set to 'L2SR'. Additional documentation and usage examples. Data provider notes: Data products must contain both optical and thermal data to be successfully processed to surface temperature, as ASTER NDVI is required to temporally adjust the ASTER GED product to the target Landsat scene. Therefore, night time acquisitions cannot be processed to surface temperature. A known error exists in the surface temperature retrievals relative to clouds and possibly cloud shadows. The characterization of these issues has been documented by Cook et al., (2014). ASTER GED contains areas of missing mean emissivity data required for successful ST product generation. If there is missing ASTER GED information, there will be missing ST data in those areas. The ASTER GED dataset is created from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. While this dataset has a global spatial extent, there are areas missing mean emissivity information due to persistent cloud contamination in the ASTER measurements. The USGS further screens unphysical values (emissivity < 0.6) in ASTER GED to remove any emissivity underestimation due to undetected clouds. For any given pixel with no ASTER GED input or unphysical emissivity value, the resulting Landsat ST products have missing pixels. The missing Landsat ST pixels will be consistent through time (1982-present) given the static nature of ASTER GED mean climatology data. For more information refer to landsat-collection-2-surface-temperature-data-gaps-due-missing

This resource contain the training materials from a workshop held at the 2nd Annual Developers Conference at the University of Utah. It delves into the integration of ground-based observations with remote sensing datasets. The workshop facilitated hands-on experience in employing cloud-based technologies such as Google Earth Engine, Compute Engine, and Cloud Storage for data dissemination. Participants learned to create automated systems for data upload, processing, and dissemination, featuring the Stevens River Ice Monitoring System. This approach enhances collaboration and efficiency in environmental studies by streamlining data handling workflows.

This dataset contains composite satellite images for the Coral Sea region based on 10 m resolution Sentinel 2 imagery from 2015 – 2021. This image collection is intended to allow mapping of the reef and island features of the Coral Sea. This is a draft version of the dataset prepared from approximately 60% of the available Sentinel 2 image. An improved version of this dataset was released https://doi.org/10.26274/NH77-ZW79.

This collection contains composite imagery for 31 Sentinel 2 tiles in the Coral Sea. For each tile there are 5 different colour and contrast enhancement styles intended to highlight different features. These include: - DeepFalse - Bands: B1 (ultraviolet), B2 (blue), B3 (green): False colour image that shows deep marine features to 50 - 60 m depth. This imagery exploits the clear waters of the Coral Sea to allow the ultraviolet band to provide a much deeper view of coral reefs than is typically achievable with true colour imagery. This technique doesn't work where the water is not as clear as the ultraviolet get scattered easily. - DeepMarine - Bands: B2 (blue), B3 (green), B4 (red): This is a contrast enhanced version of the true colour imagery, focusing on being able to better see the deeper features. Shallow features are over exposed due to the increased contrast. - ReefTop - Bands: B3 (red): This imagery is contrast enhanced to create an mask (black and white) of reef tops, delineating areas that are shallower or deeper than approximately 4 - 5 m. This mask is intended to assist in the creating of a GIS layer equivalent to the 'GBR Dry Reefs' dataset. The depth mapping exploits the limited water penetration of the red channel. In clear water the red channel can only see features to approximately 6 m regardless of the substrate type. - Shallow - Bands: B5 (red edge), B8 (Near Infrared) , B11 (Short Wave infrared): This false colour imagery focuses on identifying very shallow and dry regions in the imagery. It exploits the property that the longer wavelength bands progressively penetrate the water less. B5 penetrates the water approximately 3 - 5 m, B8 approximately 0.5 m and B11 < 0.1 m. Feature less than a couple of metres appear dark blue, dry areas are white. - TrueColour - Bands: B2 (blue), B3 (green), B4 (red): True colour imagery. This is useful to interpreting what shallow features are and in mapping the vegetation on cays and identifying beach rock.

For most Sentinel tiles there are two versions of the DeepFalse and DeepMarine imagery based on different collections (dates). The R1 imagery are composites made up from the best available imagery while the R2 imagery uses the next best set of imagery. This splitting of the imagery is to allow two composites to be created from the pool of available imagery so that mapped features could be checked against two images. Typically the R2 imagery will have more artefacts from clouds.

The satellite imagery was processed in tiles (approximately 100 x 100 km) to keep each final image small enough to manage. The dataset only covers the portion of the Coral Sea where there are shallow coral reefs.

Methods:

The satellite image composites were created by combining multiple Sentinel 2 images using the Google Earth Engine. The core algorithm was: 1. For each Sentinel 2 tile, the set of Sentinel images from 2015 – 2021 were reviewed manually. In some tiles the cloud cover threshold was raised to gather more images, particularly if there were less than 20 images available. The Google Earth Engine image IDs of the best images were recorded. These were the images with the clearest water, lowest waves, lowest cloud, and lowest sun glint. 2. A composite image was created from the best images by taking the statistical median of the stack of images selected in the previous stage, after masking out clouds and their shadows (described in detail later). 3. The contrast of the images was enhanced to create a series of products for different uses. The true colour image retained the full range of tones visible, so that bright sand cays still retained some detail. The marine enhanced version stretched the blue, green and red channels so that they focused on the deeper, darker marine features. This stretching was done to ensure that when converted to 8-bit colour imagery that all the dark detail in the deeper areas were visible. This contrast enhancement resulted in bright areas of the imagery clipping, leading to loss of detail in shallow reef areas and colours of land areas looking off. A reef top estimate was produced from the red channel (B4) where the contrast was stretched so that the imagery contains almost a binary mask. The threshold was chosen to approximate the 5 m depth contour for the clear waters of the Coral Sea. Lastly a false colour image was produced to allow mapping of shallow water features such as cays and islands. This image was produced from B5 (far red), B8 (nir), B11 (nir), where blue represents depths from approximately 0.5 – 5 m, green areas with 0 – 0.5 m depth, and brown and white corresponding to dry land. 4. The various contrast enhanced composite images were exported from Google Earth Engine (default of 32 bit GeoTiff) and reprocessed to smaller LZW compresed 8 bit GeoTiff images GDAL.

Cloud Masking

Prior to combining the best images each image was processed to mask out clouds and their shadows. The cloud masking uses the COPERNICUS/S2_CLOUD_PROBABILITY dataset developed by SentinelHub (Google, n.d.; Zupanc, 2017). The mask includes the cloud areas, plus a mask to remove cloud shadows. The cloud shadows were estimated by projecting the cloud mask in the direction opposite the angle to the sun. The shadow distance was estimated in two parts.

A low cloud mask was created based on the assumption that small clouds have a small shadow distance. These were detected using a 40% cloud probability threshold. These were projected over 400 m, followed by a 150 m buffer to expand the final mask.

A high cloud mask was created to cover longer shadows created by taller, larger clouds. These clouds were detected based on an 80% cloud probability threshold, followed by an erosion and dilation of 300 m to remove small clouds. These were then projected over a 1.5 km distance followed by a 300 m buffer.

The parameters for the cloud masking (probability threshold, projection distance and buffer radius) were determined through trial and error on a small number of scenes. As such there are probably significant potential improvements that could be made to this algorithm.

Erosion, dilation and buffer operations were performed at a lower image resolution than the native satellite image resolution to improve the computational speed. The resolution of these operations were adjusted so that they were performed with approximately a 4 pixel resolution during these operations. This made the cloud mask significantly more spatially coarse than the 10 m Sentinel imagery. This resolution was chosen as a trade-off between the coarseness of the mask verse the processing time for these operations. With 4-pixel filter resolutions these operations were still using over 90% of the total processing resulting in each image taking approximately 10 min to compute on the Google Earth Engine.

Sun glint removal and atmospheric correction.

Sun glint was removed from the images using the infrared B8 band to estimate the reflection off the water from the sun glint. B8 penetrates water less than 0.5 m and so in water areas it only detects reflections off the surface of the water. The sun glint detected by B8 correlates very highly with the sun glint experienced by the ultra violet and visible channels (B1, B2, B3 and B4) and so the sun glint in these channels can be removed by subtracting B8 from these channels.

This simple sun glint correction fails in very shallow and land areas. On land areas B8 is very bright and thus subtracting it from the other channels results in black land. In shallow areas (< 0.5 m) the B8 channel detects the substrate, resulting in too much sun glint correction. To resolve these issues the sun glint correction was adjusted by transitioning to B11 for shallow areas as it penetrates the water even less than B8. We don't use B11 everywhere because it is half the resolution of B8.

Land areas need their tonal levels to be adjusted to match the water areas after sun glint correction. Ideally this would be achieved using an atmospheric correction that compensates for the contrast loss due to haze in the atmosphere. Complex models for atmospheric correction involve considering the elevation of the surface (higher areas have less atmosphere to pass through) and the weather conditions. Since this dataset is focused on coral reef areas, elevation compensation is unnecessary due to the very low and flat land features being imaged. Additionally the focus of the dataset it on marine features and so only a basic atmospheric correction is needed. Land areas (as determined by very bright B8 areas) where assigned a fixed smaller correction factor to approximate atmospheric correction. This fixed atmospheric correction was determined iteratively so that land areas matched the tonal value of shallow and water areas.

Image selection

Available Sentinel 2 images with a cloud cover of less than 0.5% were manually reviewed using an Google Earth Engine App 01-select-sentinel2-images.js. Where there were few images available (less than 30 images) the cloud cover threshold was raised to increase the set of images that were raised.

Images were excluded from the composites primarily due to two main factors: sun glint and fine scattered clouds. The images were excluded if there was any significant uncorrected sun glint in the image, i.e. the brightness of the sun glint exceeded the sun glint correction. Fine

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 …

GLobAl building MOrphology dataset for URban climate modelling (GLAMOUR) offers the building footprint and height files at the resolution of 100 m in global urban centers.

the BH_100m contains the building height files where each file is named as BH_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif.

the BF_100m contains the building footprint files where each file is named as BF_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif.

Here lon_start, lon_end, lat_start, lat_end denote the starting and ending positions of the longitude and latitude of target mapping areas.

To avoid possible confusion, it should be clarified that the 'building footprint' in GLAMOUR represents the 'building surface fraction', i.e., the ratio of building plan area to total plan area.

We also offer the snapshot of source code used for the generation of the GLAMOUR dataset including:

GC_ROI_def.py defines regions of interest (ROI) used in the mapping of the GLAMOUR dataset.

GC_user_download.py retrieves satellite images including Sentinel-1/2, NASADEM and Copernicus DEM from Google Earth Engine and exports them into Google Cloud Storage.

GC_master_pred.py downloads exported data records from Google Cloud Storage and then performs the estimation of building footprint and height using Tensorflow-based models.

GC_postprocess.py performs postprocessing on initial estimations by pixel masking with the World Settlement Footprint layer for 2019 (WSF2019).

GC_postprocess_agg.py aggregates masked patches into larger tiles contained in the GLAMOUR dataset.

Please note that this dataset represents a preliminary version for review and quality checking purposes.

Here we present a dataset of Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia (HMA) based on Landsat satellite imagery and digital elevation model data. The data were obtained using the MountAiN glacier Transient snowline Retrieval Algorithm (MANTRA), a Google Earth Engine tool to measure the average altitude of the snow-ice boundary.

Each MANTRA result consists of reference data (e.g. Landsat scene, date, glacier ID), relevant topographic metrics (glacier area, minimum and maximum elevation of the glacier), results of the surface material classification (areas covered by ice, snow, debris and clouds), summary statistics of the TSLA measurement, and quality metrics (cloud cover close to snow-ice boundary, class coverage).

For the dataset presented here, we applied MANTRA to all glaciers in HMA with an area larger than 0.5 km² (ca. 28,500 based on Randolph Glacier Inventory v6 glacier outlines). After filtering and postprocessing, the dataset comprises ca. 9.66 million TSLA measurements with an average of 341 ± 160 measurements per glacier, covering the time span 1986 to 2021.

Time series of Transient Snowline Altitude (TSLA) metrics for glaciers in High Mountain Asia, 1986 to 2021. The file is in NetCDF format, with the date of the Landsat measurement (LS_DATE) as index. Individual glacier are identified through Randolph Glacier Inventory v6 IDs (RGI_ID). The recommended metric to use for analyses is the median elevation of the detected TSLA range (TSLrange_median_masl).