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At least 19 recordsLinked to original sources
USGS2007

Terrestrial remote sensing science and algorithms planned for EOS/MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) will be the primary daily global monitoring sensor on the NASA Earth Observing System (EOS) satellites, scheduled for launch on the EOS-AM platform in June 1998 and the EOS-PM platform in December 2000. MODIS is a 36 channel radiometer covering 0·415-14·235 μm wavelengths, with spatial resolution from 250 m to 1 km at nadir. MODIS will be the primary EOS sensor for providing data on terrestrial biospheric dynamics and process activity. This paper presents the suite of global land products currently planned for EOSDIS implementation, to be developed by the authors of this paper, the MODIS land team (MODLAND). These include spectral albedo, land cover, spectral vegetation indices, snow and ice cover, surface temperature and fire, and a number of biophysical variables that will allow computation of global carbon cycles, hydrologic balances and biogeochemistry of critical greenhouse gases. Additionally, the regular global coverage of these variables will allow accurate surface change detection, a fundamental determinant of global change.

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↗
USGS2009

Remote sensing of global croplands for food security: Way forward

This book opens a new pathway for global mapping that is focused on a specific land use theme, such as irrigated or rain-fed croplands and classes within these themes. Since croplands use most of the water consumed by humans, specific knowledge of irrigated and rain-fed croplands will be critical for precise estimates of water use. At present and in the coming decades, irrigated and rain-fed cropland area mapping is crucial for food security studies. Throughout this book, various subjects pertaining to global croplands are discussed comprehensively.

Book chapter↗
USGS1998

Rocky 7 prototype Mars rover field geology experiments: 1. Lavic Lake and Sunshine volcanic field, California

Experiments with the Rocky 7 rover were performed in the Mojave Desert to better understand how to conduct rover-based, long-distance (kilometers) geological traverses on Mars. The rover was equipped with stereo imaging systems for remote sensing science and hazard avoidance and 57 Fe Mössbauer and nuclear magnetic resonance spectrometers for in situ determination of mineralogy of unprepared rock and soil surfaces. Laboratory data were also obtained using the spectrometers and an X ray diffraction (XRD)/XRF instrument for unprepared samples collected from the rover sites. Simulated orbital and descent image data assembled for the test sites were found to be critical for assessing the geologic setting, formulating hypotheses to be tested with rover observations, planning traverses, locating the rover, and providing a regional context for interpretation of rover-based observations. Analyses of remote sensing and in situ observations acquired by the rover confirmed inferences made from orbital and simulated descent images that the Sunshine Volcanic Field is composed of basalt flows. Rover data confirmed the idea that Lavic Lake is a recharge playa and that an alluvial fan composed of sediments with felsic compositions has prograded onto the playa. Rover-based discoveries include the inference that the basalt flows are mantled with aeolian sediment and covered with a dense pavement of varnished basalt cobbles. Results demonstrate that the combination of rover remote sensing and in situ analytical observations will significantly increase our understanding of Mars and provide key connecting links between orbital and descent data and analyses of returned samples.

Journal of Geophysical Research E: Planets↗
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↗
USGS2016

The Land Processes Distributed Active Archive Center (LP DAAC)

The Land Processes Distributed Active Archive Center (LP DAAC) operates as a partnership with the U.S. Geological Survey and is 1 of 12 DAACs within the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS). The LP DAAC ingests, archives, processes, and distributes NASA Earth science remote sensing data. These data are provided to the public at no charge. Data distributed by the LP DAAC provide information about Earth’s surface from daily to yearly intervals and at 15 to 5,600 meter spatial resolution. Data provided by the LP DAAC can be used to study changes in agriculture, vegetation, ecosystems, elevation, and much more. The LP DAAC provides several ways to access, process, and interact with these data. In addition, the LP DAAC is actively archiving new datasets to provide users with a variety of data to study the Earth.

Fact Sheet↗
USGS2014

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Remote Sensing↗
USGS2016

Towards establishing compact imaging spectrometer standards

Remote sensing science is currently undergoing a tremendous expansion in the area of hyperspectral imaging (HSI) technology. Spurred largely by the explosive growth of Unmanned Aerial Vehicles (UAV), sometimes called Unmanned Aircraft Systems (UAS), or drones, HSI capabilities that once required access to one of only a handful of very specialized and expensive sensor systems are now miniaturized and widely available commercially. Small compact imaging spectrometers (CIS) now on the market offer a number of hyperspectral imaging capabilities in terms of spectral range and sampling. The potential uses of HSI/CIS on UAVs/UASs seem limitless. However, the rapid expansion of unmanned aircraft and small hyperspectral sensor capabilities has created a number of questions related to technological, legal, and operational capabilities. Lightweight sensor systems suitable for UAV platforms are being advertised in the trade literature at an ever-expanding rate with no standardization of system performance specifications or terms of reference. To address this issue, both the U.S. Geological Survey and the National Institute of Standards and Technology are eveloping draft standards to meet these issues. This paper presents the outline of a combined USGS/NIST cooperative strategy to develop and test a characterization methodology to meet the needs of a new and expanding UAV/CIS/HSI user community.

International Journal of Experimental Spectroscopi↗
USGS1979

A selected bibliography of remote sensing applications to soil science

The bibliography contains approximately 200 references dealing with the application of remote sensing technology to the identification and analysis of soils. The scientific papers and reports listed describe procedures and methods used in data collection and include specific applications of those data to soil studies. Most citations discuss current work from 1970 to 1978 and all references are categorized according to the type of remotely sensed data used and their application.

Report↗
USGS2014

Ten ways remote sensing can contribute to conservation

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Conservation Biology↗
USGS2003

The U.S. Geological Survey land remote sensing program

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Cartography and Geographic Information Science↗
USGS2011

Applying remote sensing to invasive species science&mdash;A tamarisk example

The Invasive Species Science Branch of the Fort Collins Science Center provides research and technical assistance relating to management concerns for invasive species, including understanding how these species are introduced, identifying areas vulnerable to invasion, forecasting invasions, and developing control methods. This fact sheet considers the invasive plant species tamarisk (Tamarix spp), addressing three fundamental questions: *Where is it now? *What are the potential or realized ecological impacts of invasion? *Where can it survive and thrive if introduced? It provides peer-review examples of how the U.S. Geological Survey, working with other federal agencies and university partners, are applying remote-sensing technologies to address these key questions.

Fact Sheet↗
USGS2013

Advanced and applied remote sensing of environmental conditions

"Remote sensing” is a general term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth’s surface. Remotely sensed data are fundamental to geographic science. The U.S. Geological Survey’s (USGS) Eastern Geographic Science Center (EGSC) is currently conducting and promoting the research and development of several different aspects of remote sensing science in both the laboratory and from overhead instruments. Spectroscopy is the science of recording interactions of energy and matter and is the bench science for all remote sensing. Visible and infrared analysis in the laboratory with special instruments called spectrometers enables the transfer of this research from the laboratory to multispectral (5–15 broad bands) and hyperspectral (50–300 narrow contiguous bands) analyses from aircraft and satellite sensors. In addition, mid-wave (3–5 micrometers, µm) and long-wave (8–14 µm) infrared data analysis, such as attenuated total reflectance (ATR) spectral analysis, are also conducted. ATR is a special form of vibrational infrared spectroscopy that has many applications in chemistry and biology but has recently been shown to be especially diagnostic for vegetation analysis.

Fact Sheet↗
USGS2005

The AmericaView Project - Putting the Earth into Your Hands

The U.S. Geological Survey (USGS) is a leader in collecting, archiving, and distributing geospatial data and information about the Earth. Providing quick, reliable access to remotely sensed images and geospatial data is the driving principle behind the AmericaView Project. A national not-for-profit organization, AmericaView, Inc. was established and is supported by the USGS to coordinate the activities of a national network of university-led consortia with the primary objective of the advancement of the science of remote sensing. Individual consortia members include academic institutions, as well as state, local, and tribal government agencies. AmericaView's focus is to expand the understanding and use of remote sensing through education and outreach efforts and to provide affordable, integrated remote sensing information access and delivery to the American public. USGS's Landsat and NASA's Earth Observing System (EOS) satellite data are downlinked from satellites or transferred from other facilities to the USGS Center for Earth Resources Observation and Science (EROS) ground receiving station in Sioux Falls, South Dakota. The data can then be transferred over high-speed networks to consortium members, where it is archived and made available for public use.

Fact Sheet↗
USGS2024

Science target prioritization framework for remote sensing

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

Conference Paper↗
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↗