The Ecological Research, Assessment and Prediction's Tidal Creeks: Sentinel Habitat Database was developed to support the National Oceanic and Atmospheric Administrations' (NOAA) Hollings Marine Laboratory (HML) Oceans and Human Health Initiative (OHHI). The goal of the program is to provide the scientific information and framework for forecasting environmental and human health risks across estuarine habitats, watersheds, and regions which includes the testing of new technologies developed by other HML OHH groups. This includes a wide range of data from tidal creek systems which are being used as the sentinel habitat for assessing and predicting the impact of coastal development on estuarine systems. Sampling has occurred in South Carolina, Georgia, North Carolina, Alabama, and Mississippi. Historical data from 1994, 1995, 2000 as well as recent data from 2005, 2006, and 2008 are included in the database. A wide range of parameters have been sampled in the estuarine tidal creek systems and their watersheds to obtain data on water quality (e.g., nutrients, pathogens, dissolved oxygen, salinity), sediment quality (e.g., characteristics, chemical contaminants), biological condition (e.g., macrobenthos, fish, organism health) , human exposure (e.g., pathogens), and watershed attributes (e.g., land cover, impervious cover, demographics). Each creek was sampled from its headwaters to its junction with a large open estuary. The creeks represented the range of land use types and human uses that occur in the Southeastern and Gulf regions, including forested, suburban, and urban watersheds. Results of these studies indicate that the amount and type of watershed development are linked to changes in creek environmental quality including increased fecal coliform levels, decreased sediment quality, changes in the kinds and abundances of biota, changes in the abundance of juvenile fish, and decreases in the abundance of shrimp that use these habitats as nurseries. These findings suggest that the shallow estuarine habitats that form the primary link with the land provide early warning of impairment and may be sentinels of ensuing harm from land-based activities. The levels of microbial and chemical contamination in these headwater environments are frequently an order of magnitude greater than that reported for deeper open water environments. Shallow or headwater tidal creeks are, in effect, the "first responders" to impacts of non-point source pollution runoff.

Defra and JNCC aim to provide timely, cost effective and high quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. These products will be produced using the Copernicus satellites, Sentinel 1 and Sentinel 2.

The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.

This dataset contains the calibration/validation (CalVal) database for the OPERA DSWx-S1 product. The CalVal database is a zip file of an Amazon Web Services S3 bucket containing classification items that enable algorithm calibration and validation of OPERA products. The CalVal database contains a Reference Document that further describes the structure and usage of the database, as well as a Validation Results document. Example notebooks demonstrating how to read the database tables, query for specific items, and download corresponding data files are available through the CalVal GitHub repository here: https://github.com/OPERA-Cal-Val/calval-database

description: The FTC produces the Consumer Sentinel Network Data Book annually using a data set of fraud, identity theft, and other reports from consumers received by the Consumer Sentinel Network. These include reports made directly by consumers to the FTC, as well as reports received by federal, state, local, and international law enforcement agencies and other non-governmental organizations. This data set includes national statistics, as well as a state-by-state listing of top report categories in each state and a listing of metropolitan areas that generated the most complaints per capita, for calendar year 2016.; abstract: The FTC produces the Consumer Sentinel Network Data Book annually using a data set of fraud, identity theft, and other reports from consumers received by the Consumer Sentinel Network. These include reports made directly by consumers to the FTC, as well as reports received by federal, state, local, and international law enforcement agencies and other non-governmental organizations. This data set includes national statistics, as well as a state-by-state listing of top report categories in each state and a listing of metropolitan areas that generated the most complaints per capita, for calendar year 2016.

Sentinel-1 Interferometric Wide (IW) and Extra Wide (EW) swath modes are collected using a form of ScanSAR imaging called Terrain Observation with Progressive Scans SAR (TOPSAR). With TOPSAR data is acquired in bursts by cyclically switching the antenna beam between multiple adjacent sub-swaths. Sentinel-1 Single Look Complex (SLC) products contain one image per sub-swath and one per polarization channel. Each sub-swath image consists of a series of overlapping bursts, where each burst has been processed as a separate SLC image. The Sentinel-1 Single Look Complex (SLC) Bursts collection identifies each burst from an individual IW or EW SLC product. The granule metadata describes the burst and provides links to a service which extracts the burst image from the SLC product and returns a GeoTIFF file. A link is also provided to the same service to extract the supplemental metadata files from the SLC product and return an XML file. The granules in the collection are generated for the life of the Sentinel-1 mission and include both Sentinel-1A and Sentinel-1B SLC products from both the IW and EW mode.

Sentinel-1 performs systematic acquisition of bursts in both IW and EW modes. The bursts overlap almost perfectly between different passes and are always located at the same place. With the deployment of the SAR processor S1-IPF 3.4, a new element has been added to the products annotations: the Burst ID, which should help the end user to identify a burst area of interest and facilitate searches. The Burst ID map is a complementary auxiliary product. The maps have a validity that covers the entire time span of the mission and they are global, i.e., they include as well information where no SAR data is acquired. Each granule contains information about burst and sub-swath IDs, relative orbit and burst polygon, and should allow for an easier link between a certain burst ID in a product and its corresponding geographic location.

The Inland Waters dataset (ILW) provides data for lakes and other water bodies across the contiguous United States (CONUS) and Alaska. ILW significantly reduces the processing effort required by end users and is a standardized community resource for lake and reservoir algorithm development and performance assessment. The data is provided for 15,450 CONUS waterbodies with a size of at least one 300 m pixel and over 2,300 resolvable lakes with sizes greater than three 300 m pixels. Alaska has 5,874 lakes resolvable lakes. ILW was developed in collaboration with the Cyanobacteria Assessment Network (CyAN). Additional inland water details and resources, including maps of resolvable lakes and additional inland water products, such as true color imagery, are available at the CyAN site.

Multispectral imagery captured by Sentinel-2 satellites, featuring 13 spectral bands (visible, near-infrared, and short-wave infrared). Available globally since 2018 (Europe since 2017) with 10-60 m spatial resolution and revisit times of 2-3 days at mid-latitudes. Accessible through the EOSDA LandViewer platform for visualization, analysis, and download.

These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.

The majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.

The Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’.

The Idaho Department of Water Resources (IDWR) maintains a groundwater level database containing data gathered by various sources, including measurements taken by IDWR staff, contractors, consultants, and other public and private entities. IDWR staff periodically measure the depth to water in wells throughout Idaho. Groundwater pressures are also collected in some areas where artesian groundwater systems exist, and are represented as negative values in the time series data (i.e. water pressure is above ground surface). Pressure transducers installed in select wells provide near-continuous water level observations. IDWR strives to generate accurate and beneficial data, and may revise data when necessary. Data can also be viewed, graphed, and downloaded through the IDWR Groundwater Data Portal https://idwr-groundwater-data.idaho.govand additional information can be obtained by visiting https://idwr.idaho.gov/water-data/groundwater-levels/.

Get the latest USA Sentinel import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

The images have been collected from sentinel-hub apps.sentinel-hub.com, and captured by Sentinel-2 mission satellites with the MSI(Multispectral Instrument), bands 1,2,3,4,8,12 have been merged as a single 6 band GEOTIFF file, which is further cut into tiles of 512x512 resolution. Access the GEOTIFF images using rasterio library in python incase your machine doesn't have the said library; run this on command line/shell pip install rasterio then import the library with import rasterio in your code. For further help on using rasterio, read official documentation https://rasterio.readthedocs.io/en/stable/

This dataset contains a curated landslide inventory compiled to support supervised machine learning applications, particularly deep learning models such as U-Net. It includes labeled reference data for large landslides that occurred worldwide from July 2015 onwards, enabling the availability of both Sentinel-1 and Sentinel-2 satellite data for each event.

The majority of landslides were identified through reports on https://eos.org/landslide-blog" target="_blank" rel="noopener">The Landslide Blog and subsequently verified via visual interpretation of pre- and post-event Sentinel imagery. An exception is the dataset subset from southern Kyrgyzstan, based on previous studies and manual corrections.

The inventory features landslides of varying sizes and types across diverse environmental and climatic conditions, with rainfall-triggered events being the most common. It encompasses both single-event landslides and clusters of multiple landslides within a single event.

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

The Sentinel1 Single Look Complex (SLC) unzipped dataset contains Synthetic Aperture Radar (SAR) data from the European Space Agency’s Sentinel-1 mission. Different from the zipped data provided by ESA, this dataset allows direct access to individual swaths required for a given study area, thus drastically minimizing the storage and downloading time requirements of a project. Since the data is stored on S3, users can utilize the boto3 library and s3 get_object method to read the entire content of the object into the memory for processing, without actually having to download it. The Sentinel-1 constellation consists of two satellites equipped with SAR sensors and a combined revisit time of six days. SAR imagery gets recorded regardless of weather conditions and daylight, which makes it ideally suited for monitoring land-use changes, surface deformations, land applications, oil spills, sea-ice, natural hazards, and for emergency response. In its current first stage, the dataset covers the entirety of Germany and is being updated continuously. As a next stage, the dataset will provide up-to-date coverage of the sentinel-1 SLC data over Europe. This dataset is retrieved from Alaska Satellite Facility (ASF) and consists of all Sentinel1-SLC imagery from the beginning (2014) to present.

High-frequency observations of surface water at fine spatial scales are critical to effectively manage aquatic habitat, flood risk and water quality. We developed inundation algorithms for Sentinel-1 and Sentinel-2 across 12 sites within the conterminous United States (CONUS) covering >536,000 km2 and representing diverse hydrologic and vegetation landscapes. These algorithms were trained on data from 13,412 points spread throughout the 12 sites. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables not only from Sentinel-1 and Sentinel-2, but also variables derived from topographic and weather datasets. The Sentinel-1 model was developed distinct from the Sentinel-2 model to enable the two time series to be integrated into a single high-frequency time series, while open water and vegetated water were both mapped to retain mixed pixel inundation. Results were validated against 7,200 vis ...

Risk for sentinel lymph node metastasis by study variable (n = 633).

This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024. Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers.

The OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025).

To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME

Radar (SAR) imagery from Sentinel-1 satellites offering all-weather, day-and-night monitoring. The dataset provides dual-polarization data (VV/VH or HH/HV) with 10–100 m spatial resolution, depending on acquisition mode. Available globally since 2014 with an approximately 6-day revisit time. Ideal for observing surface changes under any atmospheric conditions. Accessible via the EOSDA LandViewer platform for visualization, analysis, and download.