This is a landing page. To access the datasets, expand the RELATED DATASETS section below, and follow the link to the dataset you require. \r \r --------------------------------------\r \r The Remote Sensing Organisational Unit as part of the Water Group, within the NSW Department of Climate Change, Energy, the Environment and Water (NSW DCCEEW) is dedicated to harnessing the power of satellite earth observations, aerial imagery, in-situ data, and advanced modelling techniques to produce cutting-edge remote sensing information products. Our team employs a multi-faceted approach, integrating remote sensing data captured by satellites operating at various temporal and spatial scales with on-the-ground observations and key spatial datasets, including land-use mapping, weather data, and ancillary verification datasets. This synthesis of diverse information sources enables us to derive critical insights that significantly contribute to water resource planning, policy formulation, and advancements in scientific research.\r \r Drawing upon satellite imagery from reputable sources such as NASA, the European Space Agency, and commercial providers like Planet and SPOT, our team places a special emphasis on leveraging Landsat and Sentinel satellite imagery. Renowned for their archived, calibrated, and consistent datasets, these sources provide a significant advantage in our pursuit of delivering accurate and reliable information. To ensure the robustness of our information products, we implement thorough validation processes, incorporating semi-automation techniques that facilitate rapid turnaround times.\r \r Our operational efficiency is further enhanced through strategic interventions in our workflows, including the automation of processes through efficient computing scripts and the utilization of Google Earth Engine for cloud computing. This integrated approach allows us to maintain high standards of data quality while meeting the increasing demand for timely and accurate information.\r \r Our commitment to providing high-quality, professional, and technically accurate Remote Sensing - Geographic Information System (RS-GIS) data packages, maps, and information is underscored by our recognition of the growing role of technology in information transfer and the promotion of information sharing. Moreover, our dedication to ensuring the currency of RS-GIS methods, interpretation techniques, and 3D modelling enables us to continually deliver innovative products that align with evolving client expectations. Through these efforts, our team strives to contribute meaningfully to the advancement of remote sensing applications for improved environmental understanding and informed decision-making.\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 \r \r

ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Transmission Electron Microscopy (TEM), Aerosol Mass Spectrometer (AMS), Single Particle Soot Photometer (DMT SP2), Ultra-High Sensitivity Aerosol Spectrometer (DMT UHSAS), Scanning Mobility Particle Sizer (SMPS), and the TSI-3563 Nephelometer are featured in this collection. Data collection for this product is complete.The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

Improve the use of land cover data by developing an advanced framework for robust classification using multi-source datasets:
Develop, validate and optimize a generalized multi-kernel, active learning (MKL-AL) pattern recognition framework for multi-source data fusion.
Develop both single- and ensemble-classifier versions (MKL-AL and Ensemble-MKL-AL) of the system.
Utilize multi-source remotely sensed and in situ data to create land-cover classification and perform accuracy assessment with available labeled data; utilize first results to query new samples that, if inducted into the training of the system, will significantly improve classification performance and accuracy.
 

This dataset contains oceanographic and meteorological data collected during the Dedicated JPSS VIIRS Ocean Color Calibration/Validation Cruise (NF-14-09). The measured variables include optical backscatter, particulate organic carbon, pigments, salinity, water temperature, chromophoric dissolved matter (CDOM), chlorophyll, air temperature, wind direction and speed. The purpose of this cruise aboard NOAA Ship Nancy Foster was to collect in situ optical and ancillary data for validation of JPSS VIIRS satellite ocean color radiometry and derived products. The project interval was 9 to 22 November 2014. This 14-day interval included 10 days at sea (including transits), 2 staging days, 1 de-staging day and 1 day crew rest. Days at sea were 11 to 20 November 2014. The primary area of operations was the Western Atlantic along the U.S. Mid- and Southeastern Coast, including cross-shelf, Gulf Stream and blue waters. The cruise track was optimized to accommodate sampling transient features present in the region while respecting weather conditions during the time of the cruise. The cruise transected over 1800 km and occupied 23 stations for collection of underway and profile ocean color measurements during the 10-day duration.

The Kimberley region is vast, remote and difficult and expensive to access and carry out field work in. Remote sensing technologies can provide cost effective methods to gather historical and baseline data at synoptic scales as well as near-real-time observations from metre to kilometre resolution. The Kimberley Node Project 1.4 focused on monitoring turbidity with reference to its impact on the water column and substrate light environment. The projects objectives were to analyse uncertainties of remotely sensed turbidity products by comparison of different algorithms and different resolution products with each other and with archived in situ data; and to analyse time series of remotely sensed turbidity data to provide first-stage pilot products that may be applicable for future use as marine management tools. In-situ water quality data was obtained from a number of cruises that occurred along the Kimberley coastline including Collier Bay; Walcott Inlet, Outer King Sound, Koolama Bay and Lesueur Islands, Van Diemen Gulf and the Pilbara Coast and used to validate remote sensing products. Data associated with this metadata record relates to in-situ water quality. MODIS satellite data obtained from IMOS has not been stored as part of this record, but can be accessed direct via IMOS (http://www.imos.org.au/).

The goal of this study was to develop a suite of inter-related water quality monitoring approaches capable of modeling and estimating spatial and temporal gradients of particulate and dissolved total mercury (THg) concentration, and particulate and dissolved methyl mercury (MeHg), concentration, in surface waters across the Sacramento / San Joaquin River Delta (SSJRD). This suite of monitoring approaches included: a) data collection at fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, b) spatial mapping using boat-mounted flow-through sensors, and c) satellite-based remote sensing. The focus of this specific Child Page is to document a series of derived remote sensing products for turbidity and fluorescent dissolved organic matter (fDOM) based on Sentinel 2 (S2) A/B Multispectral Imager (MSI) imagery acquired between June 1, 2019 and May 31, 2021 for the SSJRD. These remote sensing products were developed using S2 A/B Level 1C input data with less than 25 ...

Aims: Remote sensing approaches could be beneficial for monitoring and compiling essential biodiversity data because it is cost-effective and allows for coverage of large areas over a short period. This study investigated the relationship between multispectral remote sensing data from Landsat 8 and Sentinel 2 and species richness and diversity in mountainous and protected grasslands. Locations: Golden Gate Highlands National Park, Free State, South Africa. Methods: In-situ data of plant species composition and cover from 142 plots with 16 releves each were distributed across the study site and used to calculate species richness and Shannon-wiener species diversity index (species diversity. We used a machine-learning random forest algorithm to optimise the prediction of species richness and diversity. The algorithm was used to identify the optimal spectral bands and vegetation indices for estimating species richness and diversity. Subsequently, the selected bands and vegetatio...

The main idea of this research is to exploit multiple observations including time-series of optical, thermal (TIR) and soil moisture data for remote sensing of vegetation properties and functioning under normal and dry conditions. It is significant to investigate the information content of such observations and quantify the impact of their synergistic use to explain drought effects on vegetation functioning. Therefore, understanding how much information one can get from different sensors (e.g., optical, TIR and soil moisture) to see vegetation (here for annual C3 grasses) properties and functioning (notably canopy photosynthesis [gross primary production (GPP)] and evapotranspiration (ET)) variations during a drought episode and whether combined use of this information can enhance vegetation functioning estimations is of great interest. This study describes the importance of plant functioning, drought effects, application of remote sensing and in-situ observations, methods for plant functioning assessment, the proposed coupled modeling approach. For more information, the reader is refereed to the digital version of the thesis here:https://library.itc.utwente.nl/papers_2018/phd/bayat.pdf

Against the backdrop of global change, both in terms of climate and demography, there is an increasing need for monitoring global water cycle. The publicly available global database is very limited in its spatial and temporal coverage worldwide. Moreover, the acquisition of in situ data and their delivery to the database are on the decline since the late 1970s be it for economical, political or other reasons. Given the insufficient monitoring from in situ gauge networks, and without any outlook of improvement, spaceborne approaches are currently being investigated. Satellite-based Earth observation with its global coverage and homogeneous accuracy has been demonstrated to be a potential alternative to in situ measurements. The Institute of Geodesy (GIS), within the Faculty of Aerospace Engineering and Geodesy at University of Stuttgart has a long-standing expertise, both theoretically and practically, in dynamic satellite geodesy. In recent years, GIS initiated and participated in studies and projects on application of spaceborne geodetic sensors for hydrological studies. HydroSat provides the results of these studies and projects, in which spaceborne geodetic sensors are used to estimate Surface water extent from satellite imagery Water level from satellite altimetry Water Storage Anomaly from satellite gravimetry River discharge from satellite altimetry, imagery or gravimetry

This dataset is a high-resolution (60 cm) grass biomass map derived from drone/UAS (unmanned aerial system)-based multispectral remote sensing, calibrated to in situ field data. It was developed for research on prescribed fire behavior responses to vegetation conditions, and vegetation community regrowth-responses post-fire. It addressed a key need for nondestructive grassland biomass measurements, in a use case where directly measuring grass biomass by destructive harvest would have disturbed the intact fuel beds needed for burning in the prescribed fire experiment.

A global compilation of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO) between 1997 and 2017. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter. The data were from multi-project archives acquired via open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version.

ASIA-AQ_MetNav_AircraftInSitu_DC8_Data is the in-situ meteorology and navigation data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Diode Laser Hygrometer (DLH) and the Meteorological Measurement System (MMS) are featured in this collection. Data collection for this product is complete.The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

The Kimberley region is vast, remote and difficult and expensive to access and carry out field work in. Remote sensing technologies can provide cost effective methods to gather historical and baseline data at synoptic scales as well as near-real-time observations from metre to kilometre resolution.
The Kimberley Node Project 1.4 focused on monitoring turbidity with reference to its impact on the water column and substrate light environment. The projects objectives were to analyse uncertainties of remotely sensed turbidity products by comparison of different algorithms and different resolution products with each other and with archived in situ data; and to analyse time series of remotely sensed turbidity data to provide first-stage pilot products that may be applicable for future use as marine management tools.

In-situ water quality data was obtained from a number of cruises that occurred along the Kimberley coastline including Collier Bay; Walcott Inlet, Outer King Sound, Koolama Bay and Lesueur Islands, Van Diemen Gulf and the Pilbara Coast and used to validate remote sensing products. Data associated with this metadata record relates to in-situ water quality. MODIS satellite data obtained from IMOS has not been stored as part of this record, but can be accessed direct via IMOS (http://www.imos.org.au/).

The U.S. Geological Survey Oregon Water Science Center, in cooperation with The Klamath Tribes initiated a project to understand changes in surface-water prevalence of Klamath Marsh, Oregon and changes in groundwater levels within and surrounding the marsh. The initial phase of the study focused on developing datasets needed for future interpretive phases of the investigation. This data release documents the creation of a geospatial dataset of January through June maximum surface-water extent (MSWE) based on a model developed by Jones (2015; 2019) to detect surface-water inundation within vegetated areas from satellite imagery. The Dynamic Surface Water Extent (DSWE) model uses Landsat at-surface reflectance imagery paired with a digital elevation model to classify pixels within a Landsat scene as one of the following types: “not water”, “water – high confidence”, “water – moderate confidence”, “wetland – moderate confidence”, “wetland – low confidence”, and “cloud/shadow/snow” (Jones, 2015; Walker and others, 2020). The model has been replicated by Walker and others (2020) for use within the Google Earth Engine (GEE, https://code.earthengine.google.com/) online geospatial processing platform. The GEE platform was used to create 37 annual composite raster images of maximum surface water inundation within the Klamath Marsh during January through June 1985–2021. The dataset presented here includes surface area calculations of January through June MSWE in tabular (.csv) format, 37 years of composite January through June MSWE datasets in raster (.tif) and vector (.shp) format, and a study area polygon in vector (.shp) format. References Cited: Jones, J.W., 2015, Efficient Wetland Surface Water Detection and Monitoring via Landsat: Comparison with in situ Data from the Everglades Depth Estimation Network. Remote Sensing, 7, 12503–12538. Jones, J.W., 2019, Improved Automated Detection of Subpixel-Scale Inundation—Revised Dynamic Surface Water Extent (DSWE) Partial Surface Water Tests. Remote Sensing, 11, 374. https://doi.org/10.3390/rs11040374 Walker, J.J., Petrakis, R.E., and Soulard, C.E., 2020, Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps: U.S. Geological Survey data release, https://doi.org/10.5066/P9LH9YYF.

The Kimberley region is vast, remote and difficult and expensive to access and carry out field work in. Remote sensing technologies can provide cost effective methods to gather historical and baseline data at synoptic scales as well as near-real-time observations from metre to kilometre resolution. The Kimberley Node Project 1.4 focused on monitoring turbidity with reference to its impact on the water column and substrate light environment. The projects objectives were to analyse uncertainties of remotely sensed turbidity products by comparison of different algorithms and different resolution products with each other and with archived in situ data; and to analyse time series of remotely sensed turbidity data to provide first-stage pilot products that may be applicable for future use as marine management tools. In-situ water quality data was obtained from a number of cruises that occurred along the Kimberley coastline including Collier Bay; Walcott Inlet, Outer King Sound, Koolama Bay and Lesueur Islands, Van Diemen Gulf and the Pilbara Coast and used to validate remote sensing products. Data associated with this metadata record relates to in-situ water quality. MODIS satellite data obtained from IMOS has not been stored as part of this record, but can be accessed direct via IMOS (http://www.imos.org.au/).

LISTOS_SurfaceMobile_InSitu_Data is the Long Island Sound Tropospheric Ozone Study (LISTOS) surface mobile data collected via mobile platforms during the LISTOS field campaign. This product is a result of a joint effort across multiple agencies, including NASA, NOAA, the EPA Northeast States for Coordinated Air Use Management (NESCAUM), Maine Department of Environmental Protection, New Jersey Department of Environmental Protection, New York State Department of Environmental Conservation and several research groups at universities. This product features data collected by the Connecticut Department of Energy and Environmental Protection (CT DEEP) special purpose mobile monitor located on the Park City ferry on Long Island Sound and other mobile platforms. Data collection is complete.The New York City (NYC) metropolitan area (comprised of portions of New Jersey, New York, and Connecticut in and around NYC) is home to over 20 million people, but also millions of people living downwind in neighboring states. This area continues to persistently have challenges meeting past and recently revised federal health-based air quality standards for ground-level ozone, which impacts the health and well-being of residents living in the area. A unique feature of this chronic ozone problem is the pollution transported in a northeast direction out of NYC over Long Island Sound. The relatively cool waters of Long Island Sound confine the pollutants in a shallow and stable marine boundary layer. Afternoon heating over coastal land creates a sea breeze that carries the air pollution inland from the confined marine layer, resulting in high ozone concentrations in Connecticut and, at times, farther east into Rhode Island and Massachusetts. To investigate the evolving nature of ozone formation and transport in the NYC region and downwind, Northeast States for Coordinated Air Use Management (NESCAUM) launched the Long Island Sound Tropospheric Ozone Study (LISTOS). LISTOS was a multi-agency collaborative study focusing on Long Island Sound and the surrounding coastlines that continually suffer from poor air quality exacerbated by land/water circulation. The primary measurement observations took place between June-September 2018 and include in-situ and remote sensing instrumentation that were integrated aboard three aircraft, a network of ground sites, mobile vehicles, boat measurements, and ozonesondes. The goal of LISTOS was to improve the understanding of ozone chemistry and sea breeze transported pollution over Long Island Sound and its coastlines. LISTOS also provided NASA the opportunity to test air quality remote sensing retrievals with the use of its airborne simulators (GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS), and Geostationary Trace gas and Aerosol Sensory Optimization (GeoTASO)) for the preparation of the Tropospheric Emissions; Monitoring of Pollution (TEMPO) observations for monitoring air quality from space. LISTOS also helped collaborators in the validation of Tropospheric Monitoring Instrument (TROPOMI) science products, with use of airborne- and ground-based measurements of ozone, NO2, and HCHO.

The GENICE II remote sensing team is led by Dr. Dustin Isleifson and is involved with Activity 1 under the GENICE II framework. Metadata and/or data collected by the remote sensing group are included on this sub-project page, divided into three categories: active remote sensing, passive remote sensing, and in-situ data.

ASIA-AQ_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Condensation Nuclei Counter (CCN), Cloud Droplet Probe (CDP), Cloud Particle Spectrometer with Polarized Detection (CPSPD), and Cloud Particle Counter (CPC) are featured in this collection. Data collection for this product is complete.The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

The goal of this project is to quantify the reliability of remotely sensed turbidity products for use in the Kimberley region. There are two specific objectives. 1: Analyze uncertainties of remotely sensed turbidity products by comparison of different algorithms and different resolution products with each other and with archived in situ data 2: Analyze time series of remotely sensed turbidity data to provide first-stage pilot products that may be applicable for future use as marine management tools. The deliverables are: Analysis of ensemble variability between different algorithms; Assessment of sub-km scale variability from comparison with high-resolution products; Quantification of uncertainty from comparison with archived in situ data; Maps of turbidity "hotspot" regions (i.e. regions of frequently occurring high turbidity events and regions of extreme variability).; Alternative: Maps of different turbidity regimes (e.g. permanently high turbidity, frequent turbid events, infrequent turbid events, persistently clear water).; Turbidity indicator products (e.g. days above a set turbidity threshold)

EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters.

The current data set is a sample of the data generated within the first project year (2017), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area.
Data sets of the second (2018) and third (2019) year of EOMORES will also be submitted.

The following is included:
- Estonia lakes and coast: in situ data 2017
- Finland: links to repositories of EO data
- Italy Trasimeno: sample of EO data 2017
- Lithuania Curonian Lagoon: sample of EO data 2017
- Netherlands Lake Markermeer: sample of EO data 2017
- Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017
- UK Scotland: in situ data Loch Leven and Loch Lomond 2017
- UK WCO Sentinel2A match ups: Western Channel Observatory match ups with Sentinel-2 satellite 2016, 2017

http://eomores-h2020.eu