The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested:

GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided.

GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present.

Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information.
polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation.
patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs)
Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites.
gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground.
gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties.
Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification.
grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides).

Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016

The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _\(Terms of Applicability\) https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 .

Preparing a satellite-operator-year panel from UCS and space-track dataThis repository contains the data and R code needed to reproduce the dataset UCS-JSpOC-soy-panel-22.csv. This dataset combines and cleans data from the Union of Concerned Scientists and Space-Track.org to create a panel of satellites, operators, and years. This dataset is used in the paper "Oligopoly competition between satellite constellations will reduce economic welfare from orbit use". The final dataset can also be downloaded from the replication files for that paper: https://doi.org/10.57968/Middlebury.23816994.v1A "living" version of this repository can be found at: https://github.com/akhilrao/orbital-ownership-data# Repository structure* /UCS data contains Excel and CSV data files from the Union of Concerned Scientists, as well as output files generated from data cleaning. You can find the UCS Satellite Database here: https://www.ucsusa.org/resources/satellite-database . Historical data was obtained from Dr. Teri Grimwood.* /Space-Track data contains JSON data from Space-Track.org, files to help identify operator names for harmonization in UCS_text_cleaner.R, and output generated from cleaning and merging data. * API queries to generate the JSON files can be found in json_cleaned_script.R. They are restated below for convenience. These queries were run on January 1, 2023 to produce the data used in "Oligopoly competition between satellite constellations will reduce economic welfare from orbit use". * 33999/OBJECT_TYPE/PAYLOAD/orderby/INTLDES asc/emptyresult/show* /Current R scripts contains R scripts to process the data. * combined_scripts.R loads and cleans UCS data. It takes the raw CSV files from /UCS data as input and produces UCS_Combined_Data.csv as output. * UCS_text_cleaner.R harmonizes various text fields in the UCS data, including operator and owner names. Best efforts were made to ensure correctness and completeness, but some gaps may remain. * json_cleaned_script.R loads and cleans Space-Track data, and merges it with the cleaned and combined UCS data. * panel_builder.R uses the cleaned and merged files to construct the satellite-operator-year panel dataset with annual satellite histories and operator information. The logic behind the dataset construction approach is described in this blog post: https://akhilrao.github.io/blog//data/2020/08/20/build_stencil_cut/* /Output_figures contains figures produced by the scripts. Some are diagnostic, some are just interesting.* /Output_data contains the final data outputs.* /data-cleaning-notes contains Excel and CSV files used to assist in harmonizing text fields in UCS_text_cleaner.R. They are included here for completeness.# Creating the datasetTo reproduce the UCS-JSpOC-soy-panel-22.csv dataset:1. Ensure R is installed along with the required packages2. Run the scripts in /Current R scripts in the following order: * combined_scripts.R (this will call UCS_text_cleaner.R) * json_cleaned_script.R * panel_builder.R3. The output file UCS-JSpOC-soy-panel-22.csv, along with several intermediate files used to create it, will be generated in /Output data

Assessment of long-term changes in lakes and natural drainage patterns of Bengaluru city using historical satellite images is a research paper from November 2019. Authors: R.Hebbar, V.Poompavai, R.Sudha, T.R.Nagashree, S.Ramasubramoniam, K.S.Ramesh, K.Ganesha Raj, Uday Raj : NSRC, ISRO, India Seema Garg, C.K. Shivanna, Ramacharya, Honniah, R.Velumani, K.P. Akash: KLCDA, Government of Karnataka, India

The National Oceanic and Atmospheric Administration (NOAA) AVHRR series satellites were used to generate 1 km, NDVI composites based on surface reflectance data, annually producing 36 composites every 10-days for all of Canada from 1985 to 2013 (Latifovic et al., 2005). Resulting cloud free NDVI composites were used to derive NDVI metrics both annually and for the growing season (defined as May 1st through August 31st) for all 6-digit DMTI Spatial single link postal code locations in Canada.

Lockheed Martin has updated six years of GOES-16 and GOES-17 data from the Geostationary Lightning Mapper (GLM), enhancing the accessibility of this crucial information. Previously, the GLM Level-0 (L0) data stored in the Comprehensive Large Array-data Stewardship System (CLASS) at the National Centers for Environmental Information (NCEI) was in a format difficult for users to work with, limiting its potential benefits. To address this, NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) plans to replace the existing L0 archive with this reprocessed data, now in the more user-friendly NetCDF/HDF5 format. This collection consists of archived L0 data from the GLM aboard the Geostationary Operational Environmental Satellite-R (GOES-R) Series, covering both GOES-East and GOES-West satellites during their operational and post-launch test phases. The GOES-R Series, extending the GOES mission through 2035, enhances our geostationary satellite observation capabilities. GOES-16, the first satellite of the GOES-R series, began operations as GOES-East on December 18, 2017. GOES-17 followed as GOES-West, starting on February 12, 2019. The reprocessed GLM L0 data spans from March 21, 2017, to September 6, 2022. The GLM, equipped to detect near-infrared optical transients over the Western Hemisphere, provides data including science, housekeeping, engineering, and diagnostic telemetry, along with orbit and attitude information from the GOES spacecraft. Each data packet, identified by a unique Application Process Identifier (APID) in its header, contains valuable information for interpretation. For detailed information on the L0 data, the GOES-R Series Product Definition and Users' Guide (PUG) offers comprehensive documentation, including instrument calibration and data reprocessing insights. Originally, the GLM L0 data was formatted in netCDF-4 with CCSDS packets stored as byte arrays, rendering them unreadable by standard netCDF applications. These were archived as hourly tar files by satellite. The newly reprocessed L0 data, however, is organized into daily files, including both background images (in FITS format) and event data (in netCDF-4), packaged as *.tgz files for easier access and use. By transitioning to a more accessible NetCDF/HDF5 file format, Lockheed Martin has significantly enhanced the utility of the GLM L0 archive, making it more beneficial for scientific and operational communities.

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, the department's Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on satellite imagery. \r \r The method uses standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). \r \r Here we provide statewide historical severity mapping of fires >100ha for the 2009-10 fire year, which is based on Landsat satellite imagery (30m pixels). From 2016/17 to the current fire year is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery (10m pixels).

Fire severity is a metric of the loss of biomass caused by fire. In collaboration with the NSW Rural Fire Service, DPE Remote Sensing & Regulatory Mapping team has developed a semi-automated approach to mapping fire extent and severity through a machine learning framework based on satellite imagery. \r \r The method uses standardised classes to allow comparison of different fires across the landscape. The FESM severity classes include: unburnt, low severity (burnt understory, unburnt canopy), moderate severity (partial canopy scorch), high severity (complete canopy scorch, partial canopy consumption), extreme (full canopy consumption). \r \r Here we provide historical severity mapping for the Royal-Heathcote region from 1989/90 to 2015/16, which is based on Landsat satellite imagery. From 2016/17 to the current fire year, this region is covered in the statewide FESM data, which is based on Sentinel 2 satellite imagery.

This data collection consists of archived Geostationary Operational Environmental Satellite-R (GOES-R) Series Geostationary Lightning Mapper (GLM) Level 0 data from the GOES-East and GOES-West satellites in the operational (OPS) and the post-launch test (PLT) phases. The GOES-R Series provides continuity of the GOES mission through 2035 and improvements in geostationary satellite observational data. GOES-16, the first GOES-R satellite, began operating as GOES-East on December 18, 2017. GOES-17 began operating as GOES-West on February 12, 2019. GOES-T launched on March 1, 2022, and was renamed to GOES-18 on March 14, 2022. GOES-U, the final satellite in the series, is scheduled to launch in 2024. GLM is a near-infrared optical transient detector observing the Western Hemisphere. The GLM Level 0 data are composed of Consultative Committee for Space Data Systems (CCSDS) packets containing the science, housekeeping, engineering, and diagnostic telemetry data downlinked from the instrument. The Level 0 data files also contain orbit and attitude/angular rate packets generated by the GOES spacecraft. Each CCSDS packet contains a unique Application Process Identifier (APID) in the primary header that identifies the specific type of packet, and is used to support interpretation of its contents. Users may refer to the GOES-R Series Product Definition and Users’ Guide (PUG) Volume 1 (Main) and Volume 2 (Level 0 Products) for Level 0 data documentation. Related instrument calibration data and Level 1b processing information are archived and available for order at the NOAA CLASS website. The GLM Level 0 data files are delivered in a netCDF-4 file format, however, the constituent CCSDS packets are stored in a byte array making the data opaque for standard netCDF reader applications. The GLM Level 0 data files are packaged in hourly tar files (data bundles) by satellite for the archive. Recently ingested archive tar files are available for 14 days on an anonymous FTP server for users to download. Data archived on offline tape may be requested from NCEI.

Aerial photographs were taken along the Little Missouri River in 2003, however the 2003 IKONOS satellite imagery is proprietary and therefore cannot be served here. The channel delineations for all years, including 2003, and the delineation of the outer flood-plain boundary are stored as shapefiles and are included in this data release. All images were geo-referenced to the 1995 digital orthophoto quarter quadrangles as described by Miller and Friedman (2009). Both the flood plain and active channel of the river were delineated on the 1995 digital orthophoto quadrangles and overlaid on rectified photos. ArcGIS was used to draw the polygons that delineate the flood plain and active channel; the delineation was saved as a SHP file. The separate images (geoTIFFs) can be viewed as a composite along with that year's channel delineation (SHP file) using a geographic information system (GIS) application. Reference: Miller, J.R., and J.M. Friedman. 2009. Influence of flow variability on flood-plain formation and destruction, Little Missouri River, North Dakota. Geological Society of America Bulletin 121:752-759.

Abstract from: 'An inventory of present glaciers on Heard Island and their historical variation' by Andrew Ruddell.\r \r Heard Island is a large ice-covered volcanic cone situated in the south Indian Ocean. Its location enables unique climatic information to be obtained from a very remote and predominantly maritime region. Past studies show that while some glaciers have undergone major recession since the late 1940s, others, such as large non-calving glaciers, have shown little change in extent. The island is usually cloud covered and this has hampered aerial and ground based surveys. Using SPOT satellite imagery acquired in 1988 and supplemented by aerial photography in 1987 and a digital elevation model derived from 1997 Radarsat imagery, an inventory of glacier extent is provided and this indicates that there are a total of 29 glaciated basins (41 termini), with an area of 257 km2 and an estimated volume of 14.2 km3. The satellite imagery is used to rectify earlier estimates of glacier extent based on aerial photography in 1947 and 1980. Between 1947 and 1988 the glaciated area had decreased by 11% and volume by 12%. Approximately half of this occurred during the 1980s.\r \r A variety of historical records have been compiled and these provide evidence of glacier behaviour since the mid-1800s when they were at their greatest extent during the recorded period. The elevation range of a glacier is a good indication of glacier hypsometry and its sensitivity to mass balance and climate variations. Glaciers such as the Gotley are of large elevation range and high mass turnover. Such glaciers show little sensitivity to climate variations as they lose much of their ice through calving into the sea rather than surface melt. Glaciers of low elevation range such as those on the Laurens Peninsula are especially sensitive to climate change. Glaciers of this type indicate that while minor decadal fluctuations have occurred in the period from at least 1902 to the 1950s, the recession of many glaciers during the past 50 years has been unprecedented. The glacier variations correlate with observed temperature records.\r \r Observations of occasional volcanic eruptions since the 1880s indicate that most activity is related to lava flows from Mawson Peak and fumerole activity on its upper southwestern slope. This activity appears to have had little effect on the Gotley and Lied glaciers.

The GOES-R Geostationary Lightning Mapper (GLM) Gridded Data Products consist of full disk extent gridded lightning flash data collected by the Geostationary Lightning Mapper (GLM) on board each of the Geostationary Operational Environmental Satellites R-Series (GOES-R). These satellites are a part of the GOES-R series program: a four-satellite series within the National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Association (NOAA) GOES program. These are GOES-16, -17, -18, and -19. Currently, GOES-18 and GOES-19 are the active satellites. GLM is the first operational geostationary optical lightning detector that provides total lightning data (in-cloud, cloud-to-cloud, and cloud-to-ground flashes). While it detects each of these types of lightning, the GLM is unable to distinguish between each type. The GLM GOES L3 dataset files contain gridded lightning flash data over the Western Hemisphere in netCDF-4 format from December 31, 2017 to present as this is an ongoing dataset.

The GOES-R Advanced Baseline Imager (ABI) Cloud Top Temperature product contains an image with pixel values identifying the atmospheric temperature at the top of a cloud layer. The product is generated in combination with the Cloud Top Height and Cloud Top Pressure products by the same algorithm. The product includes data quality information that provides an assessment of the cloud top height data values for on-earth pixels. The units of measure for the cloud top temperature value is kelvin. The product image is provided at 2 km resolution on the ABI fixed grid for Full Disk, CONUS, and Mesoscale coverage regions from GOES East and West. Product data is produced under the following conditions: cloudy; Geolocated source data to local zenith angles of 70 degrees for both daytime and nighttime conditions.

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

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Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018. NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019). The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image. In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0). Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel. The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available at https://podaac.jpl.nasa.gov/dataset/ABI_G16-STAR-L3C-v2.70, where gridded L2P SSTs are reported, and BT layers omitted.

The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Rational Polynomial Coefficients (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Accuracy <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Projection UTM WGS84 Accuracy <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite. PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

The National Centers for Environmental Information in partnership with the Cooperative Institute for Climate and Satellites - North Carolina is reprocessing the GOES (Geostationary Operational Environmental Satellite) Variable (GVAR) period of record: 1994-2015. GridSat GOES represents a reformatted, remapped and calibrated GOES brightness temperatures and reflectance provided in Climate and Forecasting (CF)-compliant netCDF format. This is similar to the current GridSat-B1 CDR, but at a higher spatial and temporal resolution. The data are provided near the original spatial resolution of the infrared channels (4 km) on an equal angle grid (0.04 degrees). Data are mapped to a region spanning the view of GOES East and West (150 deg East to 5 deg East). The data are provided hourly, with all data mapping to the nearest hour. Currently, the data are limited to variables including the observations from the GOES satellites: 5 total channels. However, future efforts are planned to include some basic cloud information (cloud probability, temperature, etc.). Other possible updates include: improved coverage by expanding the GOES inventory (currently, gaps exist in the CLASS archive) and expand to the predecessor to the GOES Imager: GOES VISSR, which would expand coverage back to the 1980s.

This product delivers a monthly timeseries of estimated water volume for On-Farm Storages (OFS) across the five valleys of the northern Murray-Darling Basin (Border River, Gwydir, Namoi, Macquarie-Castlereagh, and Barwon-Darling). OFS are critical for agricultural water supply, enabling farmers to store water for irrigation and supporting effective water resource management in the face of water scarcity, regulatory requirements, climate variability, and the need for sustainable agricultural practices.\r \r Estimated OFS water volume is derived through a two-step remote sensing approach. First, water surface area is calculated from multispectral satellite imagery (Landsat and Sentinel-2) using a geospatial model [1][2]. Second, LiDAR-derived storage capacity curves are applied to relate the observed surface area to water height above the Australian Height Datum (AHD), enabling calculation of the estimated water volume for each storage [3].\r \r The dataset is updated monthly using the latest available cloud-free satellite imagery, and historical volume are computed from archived images. The timeseries spans from 1987 to the present, providing a consistent monthly record of estimated OFS water volume across the region.\r \r \r References\r \r [1] https://datasets.seed.nsw.gov.au/dataset/remote-sensing-earth-observation-water-toolkit.\r \r [2] https://www.tandfonline.com/doi/full/10.1080/01431160600589179 \r \r [3] https://hls.gsfc.nasa.gov \r \r \r -----------------------------------\r \r Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.\r \r \r

The NSW Imagery web service provides access to a repository of the Spatial Services (DCS) maintained standard imagery covering NSW, plus additional sourced imagery. It depicts an imagery map of NSW showing a selection of LANDSAT® satellite imagery, standard 50cm orthorectified imageries, High resolution 10cm Town Imageries. It also contains high resolution imageries within multiple areas of NSW within DFSI, Spatial Services maintained projects and captured by AAM, VEKTA and Jacobs (previously SKM). The image web service is updated periodically when new imageries are available. The imageries are shown progressively from scales larger than 1:150,000 higher resolution imagery overlays lower resolution imagery and most recent imagery overlays older imagery within each resolution. The characteristics of each image such as accuracy, resolution, viewing scale, image format etc varies by sensor, location, capture methodology, source and processing. For specific information about the metadata for the imagery used, please refer to the individual data series within the NSW Data Catalogue. As a consequence of the variety of source data, each map displayed by the user within this map service may have a number of copyright permissions. It is emphasised that the user should check the use constraints for each image data series.\r \r - - - \r NOTE: Please contact the Customer HUB https://customerhub.spatial.nsw.gov.au/ for advice on datasets access.\r - - -\r

This paper outlines the background to the preparation of a digital pre-clearing vegetation map of the shires of Wentworth and Balranald (Southern Mallee). The data set was prepared in order to provide the Southern Mallee Regional Planning Committee with a platform from which to discuss the scope for any further clearing in tbe region. The Basin Care M305 Structural Vegetation digital coverage was used as the base layer, significantly modifying codes (1, 2, 3, 4, 7) that mapped areas where the native vegetation had been replaced. Satellite imagery, aerial photographs and historical reports where used to identify where and what the vegetation types were that had been cleared. A total of 33 1:100,000 M305 map sheets tiles were modified to complete the pre-clearing map of the region. A summary of the status of each vegetation community in the region is outlined showing the extent of clearing and reservation.\r \r VIS_ID 1044