SEARCH · Search USGS

Results for “Journal of Remote Sensing”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

USGS2019

A bibliometric profile of the Remote Sensing Open Access Journal published by MDPI between 2009 and 2018

Remote Sensing Open Access Journal (RS OAJ) is an international leading journal in the field of remote sensing science and technology. It was first published in the year 2009 and is currently celebrating tenth year of publications. In this research, a bibliometric analysis of RS OAJ was conducted based on 5588 articles published during the 10-year (2009–2018) time-period. The bibliometric analysis includes a comprehensive set of indicators such as dynamics and trends of publications, journal impact factor, total cites, eigenfactor score, normalized eigenfactor, CiteScore, h-index, h-classic publications, most productive countries (or territories) and institutions, co-authorship collaboration about countries (territories), research themes, citation impact of co-occurrences keywords, intellectual structure, and knowledge commutation. We found that publications of RS OAJ presented an exponential growth in the past ten years. From 2010 to 2017 (for which complete years data were available), the h-index of RS OAJ is 67. From 2009–2018, RS OAJ includes publications from 129 countries (or territories) and 3826 institutions. The leading nations contributing articles, based on 2009–2018 data, and listed based on ranking were: China, United States, Germany, Italy, France, Spain, Canada, England, Australia, Netherlands, Japan, Switzerland and Austria. The leading institutions, also for the same period and listed based on ranking were: Chinese Academy of Sciences, Wuhan University, University of Chinese Academy of Sciences, Beijing Normal University, The university of Maryland, National Aeronautics and Space Administration, National Oceanic and Atmospheric Administration, China University of Geosciences, United States Geological Survey, German Aerospace Centre, University of Twente, and California Institute of Technology. For the year 2017, RS OAJ had an impressive journal impact factor of 3.4060, a CiteScore of 4.03, eigenfactor score of 0.0342, and normalized eigenfactor score of 3.99. In addition, based on 2009–2018, data co-word analysis determined that “remote sensing”, “MODIS”, “Landsat”, “LiDAR” and “NDVI” are the high-frequency of author keywords co-occurrence in RS OAJ. The main themes of RS OAJ are multi-spectral and hyperspectral remote sensing, LiDAR scanning and forestry remote sensing monitoring, MODIS and LAI data applications, Remote sensing applications and Synthetic Aperture Radar (SAR). Through author keywords citation impact analysis, we find the most influential keyword is Unmanned Aerial Vehicle (UAV), followed, forestry, Normalized Difference Vegetation Index (NDVI), terrestrial laser scanning, airborne laser scanning, forestry inventory, urban heat island, monitoring, agriculture, and laser scanning. By analyzing the intellectual structure of RS OAJ, we identify the main reference publications and find that the themes are about Random Forests, MODIS vegetation indices and image analysis, etc. RS OAJ ranks first in cited journals and third in citing, this indicates that RS OAJ has the internal knowledge flow. Our results will bring more benefits to scholars, researchers and graduate students, who hopes to get a quick overview of the RS OAJ. And this article will also be the starting point for communication between scholars and practitioners. Finally, this paper proposed a nuanced h-index (nh-index) to measure productivity and intellectual contribution of authors by considering h-index based on whether the one is first, second, third, or nth author. This nuanced approach to determining h-index of authors is powerful indicator of an academician’s productivity and intellectual contribution.

Remote Sensing↗

USGS2010

The IGBP-DIS global 1 km land cover data set, DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.

International Journal of Remote Sensing↗

USGS2026

Hyperspectral narrowband imaging spectroscopy: A new paradigm for Earth observation

This editorial introduces the Special Issue entitled “Hyperspectral Narrowband Imaging Spectroscopy: A New Paradigm for Earth Observation” in the August 2026 issue of Photogrammetric Engineering & Remote Sensing (PE&RS), the flagship journal of the American Society for Photogrammetry and Remote Sensing (ASPRS). This volume represents the fourth dedicated hyperspectral special issue published in PE&RS, following earlier contributions by Thenkabail et al. (2025, 2024a, 2024b), and continues ASPRS’s commitment to advancing cutting‑edge imaging spectroscopy research and its applications across Earth system science. Remote sensing is undergoing one of the most profound transformations in its history. The emergence of hyperspectral narrowband (HNB) imaging spectroscopy data, capable of acquiring hundreds of contiguous, narrow spectral bands, has shifted the discipline from observing Earth in a handful of broad spectral windows to capturing continuous spectral signatures of the Planet. This transition marks a decisive break from the multispectral paradigm that has dominated satellite remote sensing for nearly five decades, driven by the advent of new orbital imaging spectrometers such as EnMAP, PRISMA, and NASA’s EMIT, and by the forthcoming Surface Biology and Geology (SBG) mission ((Pires Silva et al., 2026; Bourriz et al., 2025; Thenkabail et al., 2025; Chabrillat et al., 2024; Aneece et al., 2024; Dave et al., 2024; Thenkabail et al., 2024a; Thenkabail et al., 2024b; Thenkabail, 2024a; Thenkabail, 2024b; Thompson et al., 2022; Kokaly et al., 2022; Aneece & Thenkabail, 2022; Cawse Nicholson et al., 2021; Guanter et al., 2021; Vangi et al., 2021; Thenkabail et al., 2021). These missions (e.g., Table 1) deliver unprecedented spectral fidelity, improved signal to noise ratios, and global coverage capabilities, enabling a new era of quantitative, spectroscopy based Earth observation. Where multispectral broadbands (MBBs) provide only a few discrete measurements along the electromagnetic spectrum, HNB systems deliver rich, diagnostic information that enables scientists to characterize Earth’s surface with unprecedented biochemical, biophysical, and structural detail (Figure 1a, 1b). The implications for environmental monitoring, agriculture, water resources, and mineral exploration are profound. Several overarching themes emerge: • Spectral fidelity matters. The ability to preserve subtle absorption features is essential for mineral mapping, vegetation trait retrieval, and biochemical modeling. • AI and deep learning are indispensable. From destriping to classification, modern analytics must be scalable, label‑efficient, and capable of exploiting the full spectral–spatial richness of HNB data. • Physics‑based and data‑driven approaches must converge. Radiative transfer models such as PROSAIL, enhanced with localized soil parameterizations, remain foundational for biophysical retrievals and model‑based inference. • Dimensionality reduction and feature extraction are critical. Techniques such as L1‑ISOMAP demonstrate that intelligent manifold learning can unlock the structure of fused, high‑dimensional datasets. • Next‑generation architectures must be interactive and multimodal. ICTNet exemplifies the future of hyperspectral classification: hybrid, synergistic, and capable of modeling both local textures and global spectral dependencies.

Photogrammetric Engineering and Remote Sensing (PE↗

USGS1980

A selected bibliography: Remote sensing applications in wildlife management

Citations of 165 selected technical reports, journal articles, and other publications on remote sensing applications for wildlife management are presented in a bibliography. These materials summarize developments in the use of remotely sensed data for wildlife habitat mapping, habitat inventory, habitat evaluation, and wildlife census. The bibliography contains selected citations published between 1947 and 1979.

Report↗

USGS2010

Land cover characterization and mapping of continental southeast Asia using multi-resolution satellite sensor data

Land use/land cover change, particularly that of tropical deforestation and forest degradation, has been occurring at an unprecedented rate and scale in Southeast Asia. The rapid rate of economic development, demographics and poverty are believed to be the underlying forces responsible for the change. Accurate and up-to-date information to support the above statement is, however, not available. The available data, if any, are outdated and are not comparable for various technical reasons. Time series analysis of land cover change and the identification of the driving forces responsible for these changes are needed for the sustainable management of natural resources and also for projecting future land cover trajectories. We analysed the multi-temporal and multi-seasonal NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite data of 1985/86 and 1992 to (1) prepare historical land cover maps and (2) to identify areas undergoing major land cover transformations (called ‘hot spots’). The identified ‘hot spot’ areas were investigated in detail using high-resolution satellite sensor data such as Landsat and SPOT supplemented by intensive field surveys. Shifting cultivation, intensification of agricultural activities and change of cropping patterns, and conversion of forest to agricultural land were found to be the principal reasons for land use/land cover change in the Oudomxay province of Lao PDR, the Mekong Delta of Vietnam and the Loei province of Thailand, respectively. Moreover, typical land use/land cover change patterns of the ‘hot spot’ areas were also examined. In addition, we developed an operational methodology for land use/land cover change analysis at the national level with the help of national remote sensing institutions.

International Journal of Remote Sensing↗

USGS2010

The impact of bottom brightness on spectral reflectance of suspended sediments

Two experiments were conducted outdoors to investigate how bottom brightness impacts the spectral response of a water column under varied suspended sediment concentrations. A white aluminum panel placed at the bottom of the tank was used as the bright bottom, and a flat-black tank liner served as the dark bottom. Sixteen levels of suspended sediment from 25 to 400 mg litre -1 were used in each experiment. Spectral data were collected using a Spectron SE-590 spectroradiometer. The major findings include the following: the bright bottom had the greatest impact at visible wavelengths; when suspended sediment concentrations exceeded 100 mg litre -1 , the bright bottom response was found to be negligible; and, substrate brightness has minimal impact between 740 and 900 nm, suggesting that these wavelengths are best for measuring suspended sediment concentrations by means of remote sensing.

International Journal of Remote Sensing↗

USGS2017

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature ( T s ) normalized difference vegetation index (NDVI) and daily maximum air temperature ( T a ) . The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T s and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T s from Landsat relative to the T s from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

International Journal of Remote Sensing↗

USGS2026

Advances and applications of Unoccupied Aerial Systems (UAS) research in landscape ecology

Landscape ecologists have long depended on satellite and aerial remote sensing to address questions about landscape pattern and process, structure, and change (Foody 2023 ). Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) technology is becoming an increasingly popular research tool in environmental sciences allowing scientists to generate low-cost, high-quality, and high-resolution imagery on demand that can be tailored to specific research questions. While satellite data are of a fixed resolution and temporal interval, UAS offer researchers control and flexibility to design studies and collect data at resolutions and scales that provide ecologically relevant information at finer spatial resolutions (e.g., < 30 cm) than what is currently available from satellite platforms (typically > 3m), thus helping capture objects such as individual plant canopies, micro-topography, and individual animals. Unlike satellites with fixed orbits, UAS can be deployed at more optimal temporal frequencies for ecological monitoring. We organized the special collection “Advances and Applications of Unoccupied Aerial Systems (UAS) Research in Landscape Ecology” to showcase the many ways that UAS tools and technologies are currently applied to advance landscape ecological research. When we announced the collection in 2023, only 11 papers published in the journal Landscape Ecology used UAS data, which was a notably small number compared to many other general ecology, environmental science and remote sensing journals. In an attempt to understand why UAS were not more widely used in landscape ecology and provide possible solutions, we published a review article (Villarreal et al. 2025) that identified the challenges, knowledge gaps, and obstacles for the adoption of UAS technologies in landscape ecology research. The main issues we identified include: (1) an abundance of UAS methods papers in the existing literature, with comparatively few studies demonstrating how UAS can be applied to address ecological questions; (2) a perceived scale mismatch between the geographic extent of UAS data collection (local) compared to larger study areas (landscapes) and a need to design robust scaling approaches to connect fine-scale UAS data with broader ecological patterns; and (3) a need for improved integration of UAS data with other commonly used remote sensing datasets including historical high resolution aerial imagery. Additionally, researchers new to UAS remote sensing may be discouraged or overwhelmed by the general lack of scientific consensus and standardized protocols for typical tasks such as data collection, vegetation classification, and change detection, as well as restrictive and/or confusing policy, regulatory, and legal issues surrounding UAS operations (Villarreal et al. 2025).

Landscape Ecology↗

USGS2007

Preliminary measurements of leaf spectral reflectance in the 8-14 μm region

Previous broad band measurements of the spectral reflectance of leaves indicate variations in spectral emissivity that, although small, might be detected with current airborne thermal infrared imaging systems. Preliminary high spectral resolution measurements of the spectral reflectance of leaves of four different species reported here show a different spectral response for each species. These data suggest that species discrimination using remote sensing data in the thermal infrared may be feasible, and raise the possibility that other factors that might affect leaf surface composition and spectral response, such as metal stress, might also be detected.

International Journal of Remote Sensing↗

USGS1994

Extraction of spatial information from remotely sensed image data - an example: gloria sidescan sonar images

A method to extract spatial amplitude and variability information from remotely sensed digital imaging data is presented. High Pass Filters (HPFs) are used to compute both a Spatial Amplitude Image/Index (SAI) and Spatial Variability Image/Index (SVI) at the local, intermediate, and regional scales. Used as input to principal component analysis and automatic clustering classification, the results indicate that spatial information at scales other than local is useful in the analysis of remotely sensed data. The resultant multi-spatial data set allows the user to study and analyze an image based more on the complete spatial characteristics of an image than only local textural information.

Canadian Journal of Remote Sensing↗

USGS2026

Bridging remote sensing advances and management needs for small Prairie Pothole waterbodies using a multiscale accuracy assessment

Remote sensing of surface water provides a powerful tool to inform the management of waterfowl habitat, but there is little information available to directly assess the relative accuracy of different remote sensing datasets. Our objective was to understand how the characteristics of remotely sensed inundation datasets inform dataset accuracy, the reliable detection of small waterbodies, the distribution of surface water, observation frequency and completeness of data: all attributes relevant to the management of waterfowl habitat. We compared surface water composites from 10 remotely sensed surface water datasets from 2016 to 2021 to in-situ surface water data for three complexes of small waterbodies in the U.S. Prairie Pothole Region and evaluated their accuracy at the pixel, waterbody and local landscape scales. While all products had high per-pixel balanced accuracies (>0.75), we found distinct differences in waterbody area and landscape distribution estimates among datasets. Sentinel-1-based datasets provided a more complete set of observations over time and were more sensitive in detecting water presence in smaller waterbodies but were less accurate at identifying waterbody area than other datasets evaluated. Landsat datasets, alternatively, produced simpler landscape distributions that largely omitted the smallest waterbodies. While the datasets that either fused Sentinel-1 and −2 data collections or utilized local training data had the highest performances (e.g. balanced accuracy = 0.92), all datasets had use-case scenarios for which they may be informative. Our comparisons revealed differences that were not evident in traditional pixel-scale accuracy assessment, such as an 18-fold difference in the number of inundated waterbodies identified across remote sensing datasets. These findings provide novel insights for waterfowl conservation management on howremote sensing datasets may differ in their ability to monitor annual spring surface water presence within landscapes dominated by small waterbodies, such as the Prairie Pothole Region.

Minnesota, North Dakota↗

USGS2009

Synergistic use of optical and InSAR data for urban impervious surface mapping: A case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.

International Journal of Remote Sensing↗

USGS2010

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

International Journal of Remote Sensing↗

USGS2010

The role of C 3 and C 4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C 3 and C 4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C 3 - and C 4 -dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C 4 grasslands), whereas the C 3 -dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

International Journal of Remote Sensing↗

USGS2014

Terrestrial reference standard sites for postlaunch sensor calibration

In an era when the number of Earth observation satellites is rapidly growing and measurements from satellite sensors are used to address increasingly urgent global issues, often through synergistic and operational combinations of data from multiple sources, it is imperative that scientists and decision-makers are able to rely on the accuracy of Earth observation data products. The characterization and calibration of these sensors, particularly their relative biases, are vital to the success of the developing integrated Global Earth Observation System of Systems (GEOSS) for coordinated and sustained observations of the Earth. This can only reliably be achieved in the postlaunch environment through the careful use of observations by multiple sensor systems over common, well-characterized terrestrial targets (i.e., on or near the Earth's surface). Through greater access to and understanding of these vital reference standard sites and their use, the validity and utility of information gained from Earth remote sensing will continue to improve. This paper provides a brief overview of the use of reference standard sites for postlaunch sensor radiometric calibration from historical, current, and future perspectives. Emphasis is placed on optical sensors operating in the visible, near-infrared, and shortwave infrared spectral regions.

Canadian Journal of Remote Sensing↗

USGS2026

Sentinel-2 for chlorophyll-a water quality monitoring: A review of validation evidence and application potential

Water quality monitoring is integral to preserving the health of freshwater ecosystems, and satellite remote sensing has emerged as one monitoring method. Sentinel-2, in particular, has been valuable for water quality monitoring due to its 5-day global temporal revisit time and spatial resolution that ranges from 10 to 60 metres. Sentinel-2 can be used to measure and monitor chlorophyll-a, which historically has been used as an indicator of water quality, eutrophication and harmful algal blooms. Our goal was to review aquatic chlorophyll-a Sentinel-2 research to assess the types of validation evidence reported. Validation evidence is defined here as the set of information key to assessing algorithm performance, and include the spatial and temporal scales of satellite validation, reported in situ sampling method context information, demonstration of validation results through plots, and appropriate algorithm performance metrics. We highlight how the body of literature collectively contributes to advancing a national scale chlorophyll-a product that could support future resource management applications. Our review of 122 published studies indicated that much of the validation evidence corresponded to early stages, as defined by the NASA data maturity framework, due to a limited focus on individual lakes and limited detail on methodology for reproducibility. Prioritizing data accessibility for both in situ data and satellite workflows used in published studies; reporting methods with transparency and consistency; and using standard algorithm performance metrics could provide a consistent framework to support and enhance the utility of satellite inland water quality research. These three quality assurance mechanisms can promote effective evaluation of approaches for remote sensing of chlorophyll-a. Adopting these quality criteria could enable the integration of validation evidence from multiple studies, supporting more spatially and temporally representative products that would advance these approaches towards maturation for broader application.

International Journal of Remote Sensing↗

USGS2013

Emerging methods for the study of coastal ecosystem landscape structure and change

Coastal landscapes are heterogeneous, dynamic, and evolve over a range of time scales due to intertwined climatic, geologic, hydrologic, biologic, and meteorological processes, and are also heavily impacted by human development, commercial activities, and resource extraction. A diversity of complex coastal systems around the globe, spanning glaciated shorelines to tropical atolls, wetlands, and barrier islands are responding to multiple human and natural drivers. Interdisciplinary research based on remote-sensing observations linked to process studies and models is required to understand coastal ecosystem landscape structure and change. Moreover, new techniques for coastal mapping and monitoring are increasingly serving the needs of policy-makers and resource managers across local, regional, and national scales. Emerging remote-sensing methods associated with a diversity of instruments and platforms are a key enabling element of integrated coastal ecosystem studies. These investigations require both targeted and synoptic mapping, and involve the monitoring of formative processes such as hydrodynamics, sediment transport, erosion, accretion, flooding, habitat modification, land-cover change, and biogeochemical fluxes.

International Journal of Remote Sensing↗

USGS2005

InSAR studies of Alaska volcanoes

Interferometric synthetic aperture radar (InSAR) is a remote sensing technique capable of measuring ground surface deformation with sub-centimeter precision and spatial resolution in tens-ofmeters over a large region. This paper describes basics of InSAR and highlights our studies of Alaskan volcanoes with InSAR images acquired from European ERS-l and ERS-2, Canadian Radarsat-l, and Japanese JERS-l satellites.

Alaska↗