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.

Die Sentinel-1-Mission liefert Daten von einem Dual-Polarisations-C-Band-SAR-Instrument (Synthetic Aperture Radar) bei 5,405 GHz (C-Band). Diese Sammlung umfasst die S1-GRD-Szenen (Ground Range Detected), die mit der Sentinel-1-Toolbox verarbeitet wurden, um ein kalibriertes, orthokorrigiertes Produkt zu generieren. Die Sammlung wird täglich aktualisiert. Neue Assets werden innerhalb von zwei …

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 an UINT8. Areas missing any or all …

The SEN12TP dataset (Sentinel-1 and -2 imagery, timely paired) contains 2319 scenes of Sentinel-1 radar and Sentinel-2 optical imagery together with elevation and land cover information of 1236 distinct ROIs taken between 28 March 2017 and 31 December 2020. Each scene has a size of 20km x 20km at 10m pixel spacing. The time difference between optical and radar images is at most 12h, but for almost all scenes it is around 6h since the orbits of Sentinel-1 and -2 are shifted like that. Next to the \(\sigma^\circ\) radar backscatter also the radiometric terrain corrected \(\gamma^\circ\) radar backscatter is calculated and included. \(\gamma^\circ\) values are calculated using the volumetric model presented by Vollrath et. al 2020.

The uncompressed dataset has a size of 222 GB and is split spatially into a train (~90%) and a test set (~10%). For easier download the train set is split into four separate zip archives.

Please cite the following paper when using the dataset, in which the design and creation is detailed:
T. Roßberg and M. Schmitt. A globally applicable method for NDVI estimation from Sentinel-1 SAR backscatter using a deep neural network and the SEN12TP dataset. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2023. https://doi.org/10.1007/s41064-023-00238-y.

The file sen12tp-metadata.json includes metadata of the selected scenes. It includes for each scene the geometry, an ID for the ROI and the scene, the climate and land cover information used when sampling the central point, the timestamps (in ms) when the Sentinel-1 and -2 image was taken, the month of the year, and the EPSG code of the local UTM Grid (e.g. EPSG:32643 - WGS 84 / UTM zone 43N).

Naming scheme: The images are contained in directories called {roi_id}_{scene_id}, as for some unique regions image pairs of multiple dates are included. In each directory are six files for the different modalities with the naming {scene_id}_{modality}.tif. Multiple modalities are included: radar backscatter and multispectral optical images, the elevation as DSM (digital surface model) and different land cover maps.

Data modalities

name	Modality	GEE collection
s1	Sentinel-1 radar backscatter	COPERNICUS/S1_GRD
s2	Sentinel-2 Level-2A (Bottom of atmosphere, BOA) multispectral optical data with added cloud probability band	COPERNICUS/S2_SR COPERNICUS/S2_CLOUD_PROBABILITY
dsm	30m digital surface model	JAXA/ALOS/AW3D30/V3_2
worldcover	land cover, 10m resolution	ESA/WorldCover/v100

The following bands are included in the tif files, for an further explanation see the documentation on GEE. All bands are resampled to 10m resolution and reprojected to the coordinate reference system of the Sentinel-2 image.

Modality Bands

Modality	Band count	Band names in tif file	Notes
s1	5	VV_sigma0, VH_sigma0, VV_gamma0flat, VH_gamma0flat, incAngle	VV/VH_sigma0 are the \(\sigma^\circ\) values, VV/VH_gamma0flat are the radiometric terrain corrected \(\gamma^\circ\) backscatter values incAngle is the incident angle
s2	13	B1, B2, B3, B4, B5, B7, B7, B8, B8A, B9, B11, B12, cloud_probability	multispectral optical bands and the probability that a pixel is cloudy, calculated with the sentinel2-cloud-detector library optical reflectances are bottom of atmosphere (BOA) reflectances calculated using sen2cor
dsm	1	DSM	Height above sea level. Signed 16 bits. Elevation (in meter) converted from the ellipsoidal height based on ITRF97 and GRS80, using EGM96†1 geoid model.
worldcover	1	Map	Landcover class

Checking the file integrity
After downloading and decompression the file integrity can be checked using the provided file of md5 checksum.
Under Linux: md5sum --check --quiet md5sums.txt

References:

Vollrath, Andreas, Adugna Mullissa, Johannes Reiche (2020). "Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine". In: Remote Sensing 12.1, Art no. 1867. https://doi.org/10.3390/rs12111867.

The CORINE (coordination of information on the environment) Land Cover (CLC) inventory was initiated in 1985 to standardize data collection on land in Europe to support environmental policy development. The project is coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme and implemented by …

El DEM de Copernicus es un modelo digital de superficie (DSM) que representa la superficie de la Tierra, incluidos los edificios, la infraestructura y la vegetación. Este MDT se deriva de un MDV editado llamado WorldDEM&trade, es decir, se incluyó el aplanamiento de las masas de agua y el flujo constante de los ríos. También se aplicó la edición de costas y litorales, y de elementos especiales, como aeropuertos y estructuras de terreno inverosímiles. El producto WorldDEM se basa en los datos de satélite radar adquiridos durante la misión TanDEM-X, que se financia a través de una asociación público-privada entre el Estado alemán, representado por el Centro Aeroespacial Alemán (DLR), y Airbus Defence and Space. En la documentación del conjunto de datos, se incluyen más detalles. Se incorporó el recurso de Earth Engine desde los archivos de DGED. Nota: Consulta el ejemplo de código para conocer la forma recomendada de calcular la pendiente. A diferencia de la mayoría de los DEM en Earth Engine, esta es una colección de imágenes debido a las múltiples resoluciones de los archivos fuente que imposibilitan su mosaico en un solo recurso, por lo que los cálculos de pendiente requieren una reproyección.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2023)
• railways (OpenStreetMap, 2023)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

Contextual information:

• elevation (European Union and ESA, 2021)
• land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)
• administrative boundaries from geoBoundaries (Runfola et al., 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on 09-AUG-2024)
• Copernicus DEM - Global Digital Elevation Model (2021) DOI: 10.5270/ESA-c5d3d65 (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved)
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866.
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

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.

These datasets come from Google Earth Engine and are used in ACEA challenge

The first is daily time series from Copernicus ECMWF ERA5 Daily aggregates, extracted using weather station geolocations. Time series range from 1998 to 2020. 48 different stations are located in Italy.
The extraction have been done with this script :

import pandas as pd
import numpy as np
from datetime import datetime as dt
import ee
def extract_time_series(lat, lon, start, end, product_name, sf):
  # Set up point geometry
  point = ee.Geometry.Point(lon, lat)

  # Obtain image collection for all images within query dates
  coll = ee.ImageCollection(product_name)\
    .filterDate(start, end)

  def setProperty(image):
    dic = image.reduceRegion(ee.Reducer.first(), point)
    return image.set(dic)

  data = coll.map(setProperty)
  data = data.getInfo()
  liste = list(map(lambda x: pd.DataFrame(x['properties']), data['features']))
  df = pd.concat(liste)
  return df

if _name_ == "_main_":
  ee.Initialize()
  for i in locations.keys(): # locations is a dictionnary containing latitude and longitude
    print(i)
    latitude = locations[i]['lat']
    longitude = locations[i]['lon'] 
    while True:
      try: 
        output = extract_time_series(latitude,
                 longitude,
                 '1998-01-01',
                 '2020-01-01',
                 'ECMWF/ERA5/DAILY',
                 1)
        break
      except: 
        print(i + " 1 fail")
        continue              
    name =PATH + i + "_1.csv"        
    output.to_csv(name, index=True)

The second dataset is Forecasted Weather from Global Forecast System.
The purpose of this dataset is to provide forecasted rainfall and temperature for the 16 coming days. Creation_time column is the released date while forecast_hours is forecasted weather for horizon : creation_time + forecast_hours. Time series are daily and range from 2015 to 2020. Unfortunately, there are missing values.
Python script :

import pandas as pd
import numpy as np
from datetime import datetime as dt
import ee
def extract_time_series_gfs(lat, lon, start, end, product_name, sf, h):

  # Set up point geometry
  point = ee.Geometry.Point(lon, lat)

  # Obtain image collection for all images within query dates
  coll = ee.ImageCollection(product_name)\
    .select(['total_precipitation_surface','temperature_2m_above_ground'])\
    .filterDate(start, end)\
    .filterMetadata('forecast_hours', 'equals', h)

  def setProperty(image):
    dic = image.reduceRegion(ee.Reducer.first(), point)
    return image.set(dic)

  data = coll.map(setProperty)
  data = data.getInfo()
  
  liste = list(map(lambda x: pd.DataFrame(x['properties']), data['features']))
  df = pd.concat(liste)
  df=df[df["system:footprint"] == "LinearRing"]

  return df
if _name_ == "_main_":

  ee.Initialize()
  horizon = [i*24 for i in range(1,17)]
  for i in locations.keys():
    print(i)
    latitude = locations[i]['lat']
    longitude = locations[i]['lon'] 
    
    for j in horizon:
      while True:
        try:
          output = extract_time_series_gfs(latitude,
                 longitude,
                 '2015-07-01',
                 '2020-08-01',
                 'NOAA/GFS0P25',
                 1,
                 j)
          break
        except:
          print(i + " " + str(j) +" 1 fail")
          continue
      name = PATH + i + "_" + str(j) +"_1.csv"
           
      output.to_csv(name, index=True)

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.

OFFL/L3_O3_TCL This dataset provides offline tropospheric high-resolution imagery of ozone concentrations between 20N and 20S. See also COPERNICUS/S5P/OFFL/L3_O3 and COPERNICUS/S5P/NRTI/L3_O3 for the total column data. In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but …

The SEN12 Global Urban Mapping (SEN12_GUM) dataset consists of Sentinel-1 SAR (VV + VH band) and Sentinel-2 MSI (10 spectral bands) satellite images acquired over the same area for 96 training and validation sites and an additional 60 test sites covering unique geographies across the globe. The satellite imagery was acquired as part of the European Space Agency's Earth observation program Copernicus and was preprocessed in Google Earth Engine. Built-up area labels for the 30 training and validation sites located in the United States, Canada, and Australia were obtained from Microsoft's open-access building footprints. The other 66 training sites located outside of the United States, Canada, and Australia are unlabeled but can be used for semi-supervised learning. Labels obtained from the SpaceNet7 dataset are provided for all 60 test sites.

The monitoring of tropical forests has benefited from the increased availability of high-resolution earth observation data. However, the seasonality and openness of the canopy of dry tropical forests remains a challenge for optical sensors. The availability of time series of remote sensing images at 10-meters is changing this paradigm.

In the context of REDD+ national reporting requirements, we investigated a methodology that is reproducible and adaptable in order to ensure user appropriation. The overall methodology consists of three main steps: (i) the generation of Sentinel-1 (S1) and Sentinel-2 (S2) layers, (ii) the collection of an ad-hoc training/validation dataset and (iii) the classification of the satellite data. Three different classification workflows are compared in terms of their capability to capture the canopy cover of forests in East Africa. Two types of maps are derived from these mapping approaches: i) binary tree cover/no tree cover (TC/NTC) maps, and ii) maps of canopy cover classes. The method is applied at scale, over Tanzania and one final map for each workflow is shared. Two big data computing platforms are combined to exploit the important volume of satellite data available over a yearly period.

The reference dataset (training and validation), the three best maps and the codes to produce the S1 and S2 composites on Google Earth Engine are shared here.

Date of Image:9/28/2024, 10/3/2024, 10/5/2024, 10/10/2024, 10/15/2024, 10/17/2024Date of Next Image:UnknownSummary:NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated.Pre-event time frame: 2024-01-10 to 2024-09-26Post-event time frame: 2024-09-28 to 2024-10-17This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives.Suggested Use:The red and yellow areas indicate potential zones of dense landsliding.Disclaimer: not verified in field and optical imagery has clouds preventing verification.Satellite/Sensor:Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:10 metersCredits:NASA GSFC Landslides Team, Copernicus Sentinel-1 dataHandwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/hurricane_helene_2024/Landslide_Proxy_Heat_Map_S1_on_Sept_28_2024_for_Hurricane_Helene/MapServer/WMSServerData Download:N/A

OFFL/L3_CH4 This dataset provides offline high-resolution imagery of methane concentrations. Methane (CH4) is, after carbon dioxide (CO2), the most important contributor to the anthropogenically enhanced greenhouse effect. Roughly three-quarters of methane emissions are anthropogenic and as such, it is important to continue the record of satellite based measurements. TROPOMI aims at providing CH4 column concentrations with high sensitivity to the Earth's surface, good spatiotemporal coverage, and sufficient accuracy to facilitate inverse modeling of sources and sinks. TROPOMI uses absorption information from the Oxygen-A Band (760nm) and the SWIR spectral range to monitor CH4 abundances in the Earth's atmosphere. More information. Currently, the following data quality issues are known, are not covered by the quality flags, and should be kept in mind when looking at the methane product and also at preliminary validation results. For more details, see the MPC VDAF website. Filtering on qa_value < 0.5 does not remove all pixels considered bad. Some pixels with too low methane concentrations are still present: Single TROPOMI overpasses show stripes of erroneous CH4 values in the flight direction. Not all pixels above inland water are filtered. Uncertainties for the XCH4 is only based on the single sounding precision due to measurement noise. For applications requiring an overall uncertainty estimate, we propose to multiply the provided error by a factor 2, which reflects the scatter of single sounding errors in the TCCON validation. Data prior to November 2021 only provides XCH4 over land, after which glint ocean observations were added. No data are present between 2022-07-26 and 2022-08-31 due to a provider outage. 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 'CH4_column_volume_mixing_ratio_dry_air_validity>50;derive(datetime_stop {time})' S5P_OFFL_L2_CH4_20190223T202409_20190223T220540_07072_01_010202_20190301T221106.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 'CH4_column_volume_mixing_ratio_dry_air_validity>50; derive(datetime_stop {time}); bin_spatial(2001, 50.000000, 0.01, 2001, -120.000000, 0.01); keep(CH4_column_volume_mixing_ratio_dry_air, aerosol_height, aerosol_optical_depth, sensor_azimuth_angle, sensor_zenith_angle, solar_azimuth_angle, solar_zenith_angle)' S5P_OFFL_L2_CH4_20190223T202409_20190223T220540_07072_01_010202_20190301T221106.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).

Date of Image:3/23/2023Date of Next Image:UnknownSummary:NASA used a Sentinel-1 SAR backscatter change approach developed in GEE (Handwerger et al., 2022) to detect areas with high landslide density. This approach detects potential landslides by calculating the change in the backscatter coefficient before and after the triggering event using the log ratio approach. False positives such as backscatter change due to flooding, agriculture, and more, are removed by using threshold-based masks made from the topographic slope from the 1 arcsec (∼30 m) resolution NASADEM (NASA JPL, 2020). Using stacks of SAR data reduces noise, and furthermore, the pre-event stack provides backscatter data that is more representative of the pre-event ground surface properties. Finally, to detect landslide areas, the backscatter change raster was thresholded using the 99th percentile to highlight strong signals, and the heatmap was calculated.Pre-event time frame: 2022-01-01 to 2023-03-10Post-event time frame: 2023-03-11 - 2023-03-23This map should be used as a guidance to identify areas likely affected by landslides. This is a rapid response product. We have not done any form of manual corrections to remove false positives.Suggested Use:The red and yellow areas indicate potential zones of dense landsliding. The major red zone to the South is not validated, since there is extensive cloud cover, but the smaller red blob in the North-East is where media reports have highlighted landslides from some available optical imagery. Disclaimer: not verified in field and optical imagery has clouds preventing verification.Satellite/Sensor:Copernicus Sentinel-1 Synthetic Aperture Radar (SAR)Resolution:30 metersCredits:NASA GSFC Landslides Team, Copernicus Sentinel-1 dataHandwerger AL, Huang M-H, Jones SY, Amatya P, Kerner HR, Kirschbaum DB. 2022. Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine. Nat. Hazards Earth Syst. Sci. Copernicus Publications, 22(3): 753–773. https://doi.org/10.5194/nhess-22-753-2022.Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/cyclone_freddy_2023/landslide_density_sentinel1_20230323/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2023/cyclone_freddy/landslides/

NRTI/L3_CO This dataset provides near real-time high-resolution imagery of CO concentrations. Carbon monoxide (CO) is an important atmospheric trace gas for understanding tropospheric chemistry. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane …

Dataset from paper: Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting

Consists of target flood maps and various inputs used to predict said flood maps. Each input modality is contained in a separate zip file. Each zip file contains a collection of raster data, one file per ROI.

Files:

• base.zip - Contains flood maps and meta-data (e.g. geolocations). Includes Kuro Siwo labels.
• s1.zip - Contains Copernicus Sentinel-1 data from 2014-2020 obtained through Alaska Foundation, using an EarthData account.
• era5.zip - Contains ERA5 and ERA5-Land data products obtained from Google Earth Engine (GEE), which was originally generated using Copernicus Climate Change Service information 2014-2020.
  
• glofas.zip - Contains GLOFAS v4.0 data from Copernicus Climate Change Service's Climate Data Store (CDS).
• hydroatlas.zip - Contains HydroATLAS v10 data. Rasterized and resampled to match ERA5-Land.
• hand.zip - Contains HAND data (CC0 license). Resampled to match Sentinel-1.
• dem.zip - Contains CopDEM GLO-30 data. Resampled to match Sentinel-1 and ERA5-Land.
• extras.zip - Contains 4 folders of extra data:
  • vit-snunet-extra - Flood maps, meta-data and S1 of ROIs that fell outside era5 availability.
  • vit-snunet-raw - Raw floodmaps generated by Kuro Siwo model (after removing tiling effects, before other post-processing)
  • vit - Alternate flood maps; generated using FloodViT model only
  • snunet - Alternate flood maps; generated using SNUNet model only
• worldcover.zip - Contains worldcover data (CC-BY license) resampled (nearest) to Sentinel-1. Only used at evaluation time.

Instructions:

1. Download whichever inputs you want to use.
2. Extract all in the same folder.
3. Point the code to that folder.

Licenses: The redistributed data products (s1, era5, hydroatlas, hand, dem and worldcover) retain their original licenses. They are, however, very permissive. Only HydroATLAS is not necessarily available for commercial use.

The Copernicus Atmosphere Monitoring Service provides the capacity to continuously monitor the composition of the Earth's atmosphere at global and regional scales. The main global near-real-time production system is a data assimilation and forecasting suite providing two 5-day forecasts per day for aerosols and chemical compounds that are part of the chemical scheme. Prior to 2021-07-01 only two parameters were available, 1. Total Aerosol Optical Depth at 550 nm surface 2. Particulate matter d < 25 um surface Note that system:time_start refers to forecast time.

the copernicus in situ tac (thematic assembly centre) manages ocean in situ observations for copenicus marine environment service.it is divided in 7 area : arctic, baltic, black sea, global ocean, irish-biscay-iberia (ibi), mediterranean sea, north sea.the boudaries of these seven areas are provided in a kml file (googleearth).