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At least 127 records · Page 7Linked to original sources
USGS2013

A new map of standardized terrestrial ecosystems of Africa

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.

African Geographical Review↗
USGS2013

Constraints on the upper crustal magma reservoir beneath Yellowstone Caldera inferred from lake-seiche induced strain observations

Seiche waves in Yellowstone Lake with a ~78-minute period and heights <10 cm act as a load on the solid earth observed by borehole strainmeters with subnanostrain sensitivity throughout the Yellowstone Caldera. The far-field strain induced by the load of the seiche waves calculated with a homogeneous elastic model representing the upper crust is more than an order of magnitude smaller than the measured strain amplitude ~30 km from the lake shore. By contrast, the observed far field strain amplitudes are consistent with the seiche load on a two-layered viscoelastic model representing an elastic upper crust overlying a partially molten body deeper than 3–6 km with Maxwell viscosity less than 10 11 Pa s. These strain observations and models provide independent evidence for the presence of partially molten material in the upper crust, consistent with seismic tomography studies that inferred 10%–30% melt fraction in the upper crust.

Wyoming↗
USGS2013

CEOS visualization environment (COVE) tool for intercalibration of satellite instruments

Increasingly, data from multiple instruments are used to gain a more complete understanding of land surface processes at a variety of scales. Intercalibration, comparison, and coordination of satellite instrument coverage areas is a critical effort of international and domestic space agencies and organizations. The Committee on Earth Observation Satellites Visualization Environment (COVE) is a suite of browser-based applications that leverage Google Earth to display past, present, and future satellite instrument coverage areas and coincident calibration opportunities. This forecasting and ground coverage analysis and visualization capability greatly benefits the remote sensing calibration community in preparation for multisatellite ground calibration campaigns or individual satellite calibration studies. COVE has been developed for use by a broad international community to improve the efficiency and efficacy of such calibration planning efforts, whether those efforts require past, present, or future predictions. This paper provides a brief overview of the COVE tool, its validation, accuracies, and limitations with emphasis on the applicability of this visualization tool for supporting ground field campaigns and intercalibration of satellite instruments.

IEEE Transactions on Geoscience and 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↗
USGS2017

U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data

A partnership established between the European Space Agency (ESA) and the U.S. Geological Survey (USGS) allows for USGS storage and redistribution of images acquired by the MultiSpectral Instrument (MSI) on the European Union's Sentinel-2 satellite mission. The MSI data are acquired from a pair of satellites, Sentinel-2A and Sentinel-2B, which are part of a larger set of ESA missions focusing on different aspects of Earth observation. The primary purpose of the Sentinel-2 series is to collect multispectral imagery over the Earth’s land surfaces, large islands, and inland and coastal waters. Sentinel-2A was launched in 2015 and Sentinel-2B launched in 2017. The collaborative effort between ESA and USGS provides for public access and redistribution of global acquisitions of Sentinel-2 data at no cost, which allows users to download the MSI imagery from USGS access systems such as Earth- Explorer, in addition to the ESA Sentinels Scientific Data Hub. The MSI sensor acquires 13 spectral bands that are highly complementary to data acquired by the USGS Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The product options from USGS include a Full-Resolution Browse (FRB) image product generated by USGS, along with a 100-kilometer (km) by 100-km tile-based Level-1C top-of-atmosphere (TOA) reflectance product that is very similar (but not identical) to the currently (2017) distributed ESA Level 1C product.

Fact Sheet↗
USGS2022

Biology: Integrating core to essential variables (Bio-ICE) task team report for marine mammals

Marine mammals are wide-ranging, relatively long-lived organisms that play a crucial role in maintaining healthy ocean ecosystems. Often referred to as ecosystem engineers and sentinel species in marine ecosystems, these charismatic megafauna feed at a variety of trophic levels, affecting food web dynamics and cycling of chemicals and nutrients in the water column as well as in benthic habitats, both nearshore and in the deep ocean. An understanding of their abundance and distribution is an essential starting point for evaluating their role in ocean ecosystems. Accordingly, marine mammals have been included among key variables to monitor in ocean observing systems, from core variables for the U.S. Integrated Ocean Observing System (IOOS) to an Essential Ocean Variable (EOV) for the Global Ocean Observing System (GOOS). They also contribute to several Essential Biodiversity Variables (EBVs) for the Group on Earth Observations Biodiversity Observation Network (GEO BON). Further, evaluation of the health of marine mammal populations will help deliver societal benefits by contributing to the UN Decade of Ocean Science for Sustainable Development; informing reporting activities such as the World Ocean Assessment; and supporting achievement of Sustainable Development Goal 14, the post-2020 framework for the Convention for Biological Diversity, and a new treaty for conservation and sustainable use of marine biodiversity beyond national jurisdiction. In the U.S., the National Marine Fisheries Service (NMFS) and the U.S. Fish and Wildlife Service (FWS) are required to produce stock assessments for marine mammals under the Marine Mammal Protection Act (MMPA, 16 U.S.C. §1371 et seq.). Stock assessment analyses require accurate, up to-date information on abundance and distribution to inform appropriate management and/or conservation measures. Despite the availability of information on abundance and distribution within the stock assessment reports, availability and accessibility of the underlying data to the broader ocean observing community and contribution to EOVs remain inconsistent.

Report↗
USGS2020

Landsat 9: Empowering open science and applications through continuity

The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.

Remote Sensing of Environment↗
USGS2022

Oklahoma and Landsat

Oklahoma benefits from a varied landscape abundant in resources. Mountains, grasslands, reservoirs, rivers, fields, and forests offer employment and enjoyment in a State that epitomizes the transition from north to south and east to west. Wheat grows in northern Oklahoma; cotton grows in the south. Wetter deciduous forest lands in the southeast contrast with drier mesas in the northwest. Among the many lakes and reservoirs, the Great Salt Plains Lake on the Arkansas River has thousands of acres of salt flats with unique hourglass-shaped selenite crystals. Grassland and grazing permeate the Sooner State, which is the second largest cattle producer in the country. Oil, natural gas, and coal have long played a key role in Oklahoma’s economy and energy industry, whereas wind farms in western Oklahoma are a more recent addition. The Landsat series of Earth observation satellites has allowed analysts to search for oil in Oklahoma from hundreds of miles above the Earth. The satellite program also helps Oklahoma’s agriculture industry keep an eye on the condition of crops and grazing land. Here are just a few ways Landsat has benefited Oklahoma.

Oklahoma↗
USGS2016

Earth as art 4

Landsat 8 is the latest addition to the long-running series of Earth-observing satellites in the Landsat program that began in 1972. The images featured in this fourth installment of the Earth As Art collection were all acquired by Landsat 8. They show our planet’s diverse landscapes with remarkable clarity. Landsat satellites see the Earth as no human can. Not only do they acquire images from the vantage point of space, but their sensors record infrared as well as visible wavelengths of light. The resulting images often reveal “hidden” details of the Earth’s land surface, making them invaluable for scientific research. As with previous Earth As Art exhibits, these Landsat images were selected solely for their aesthetic appeal. Many of the images have been manipulated to enhance color variations or details. They are not intended for scientific interpretation—only for your viewing pleasure. What do you see in these unique glimpses of the Earth’s continents, islands, and coastlines?

General Information Product↗
USGS2020

Soil moisture product validation good practices protocol, version 1.0

The Global Climate Observing System (GCOS) included soil moisture in the list of Essential Climate Variables (ECVs) to express its important role in Earth’s water, energy and carbon cycle. Soil moisture has a major impact on agriculture, land surface hydrology, weather, and climate forecasting. This document is a community-based effort to provide recommendations on good practices for the validation of global to regional soil moisture products. Definitions are given and metrics to adequately describe the quality of soil moisture products are presented. Spaceborne active and passive microwave sensors are listed with their characteristics, and the typical soil moisture retrieval methods are explained, including dielectric mixing models and optical methods. Spatial scaling, root zone soil moisture estimation, and operational implementations are addressed, as these issues continue to gain more and more importance. Standard and advanced in situ measurement techniques are described as well as sensor calibration, spatial representativity, sampling strategies, and the benefit of airborne campaigns.The community has agreed upon the utilization of the International Soil Moisture Network (ISMN) as the main online repository for in situ soil moisture measurements. Different validation methods such as ground-based validation, satellite product intercomparison, and time series analyses are presented. We provide strategies to evaluate the long-term quality of soil moisture products, and give advice on how to handle typical temporal and spatial-scale mismatches and how to effectively report validation results. Moreover, the benefit of blind tests is discussed to gain objective validation results. We encourage data providers, scientists and practitioners to use this Soil Moisture Product Validation Good Practices Protocol to provide, analyze, and improve high quality Earth Observation results.

Report↗
USGS2021

A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

Remote Sensing of Environment↗
USGS2022

Biology: Integrating core to essential variables (Bio-ICE) task team report for hard corals

The Interagency Ocean Observation Committee (IOOC) is chartered by the White House Office of Science and Technology Policy (OSTP) Subcommittee on Ocean Science and Technology (SOST). The purpose of the IOOC is to advise, assist, and make recommendations to the SOST on matters related to ocean observations via task teams such as the Biology - Integrating Core to Essential Variables (Bio-ICE) task team. The goal of the Bio-ICE task team is to advance the integration of biological observations from local, regional, and federal sources using best practices to inform national needs and ultimately feed seamlessly into the Global Ocean Observing System (GOOS), as appropriate. To accomplish this goal, and for the first time at the U.S. federal government level, a subgroup of the Bio-ICE task team focused on tropical, shallow-water (0-30 m) hard corals to identify commonalities between the U.S. Integrated Ocean Observing System (IOOS) core biological variable1 of “coral species and abundance,” the GOOS Essential Ocean Variable2 (EOV) “hard coral cover and composition,” the Group on Earth Observations Biological Observation Network (GEO BON) Essential Biodiversity Variables3 (EBVs), and the Global Climate Observing System (GCOS) Essential Climate Variables4 (ECVs) (Figure 1). The EOV data allows production of EBVs such as time series of maps of genetic composition, species populations, etc. Recognizing the complementarity of the different essential variable frameworks helps to promote best practices in observing and information management to facilitate data interoperability (Figure 1). The task team was charged with identifying where there are synergies in terms of spatial and temporal observing requirements and existing observation infrastructure and data delivery, including best practices and standard operating procedures. The task team also made suggestions to improve pathways for data flow for observations of these variables from Regional Associations of the U.S. IOOS, other nonfederal partners, and federal sources. The focus of the task team was on identifying and implementing best practices surrounding standardized data collection and data delivery to make continued progress toward adhering to the Findability, Accessibility, Interoperability, and Reuse (FAIR) and Collective benefit, Authority to control, Responsibility, and Ethics (CARE) data principles.

Task Team Report↗
USGS1996

Establishing the moon as a spectral radiance standard

A new automated observatory dedicated to the radiometry of the moon has been constructed to provide new radiance information for calibration of earth-orbiting imaging instruments, particularly Earth Observing System instruments. Instrumentation includes an imaging photometer with 4.5-in. resolution on a fully digital mount and a full-aperture radiance calibration source. Interference filters within 0.35–0.95 µm correspond to standard stellar magnitude systems, accommodate wavelengths of lunar spectral contrast, and approximate some band-passes of planned earth-orbiting instruments (ASTER, Landsat-7 ETM, MISR, MODIS, and SeaWIFS). The same equipment is used for lunar and stellar observations, with the use of an aperture stop in lunar imaging to comply with Nyquist's theorem and lengthen exposure times to avoid scintillation effects. A typical robotic night run involves observation of about 60 photometric standard stars and the moon; about 10 of the standard stars are observed repeatedly to determine atmospheric extinction, and the moon is observed several times. Observations are to be made on every photometric night during the bright half of the month for at least 4.5 years to adequately cover phase and libration variation. Each lunar image is reduced to absolute exoatmospheric radiance and reprojected to a fixed selenographic grid system. The collection of these images at various liberators and phase angles will be reduced to photometric models for each of the approximately 120 000 points in the lunar grid for each filter. Radiance models of the moon can then be produced for the precise geometry of an orbiting instrument observation. Expected errors are under 1% relative and 2.5% absolute. A second telescope operating from 1.0 to 2.5 µm is planned.

Journal of Atmospheric and Oceanic Technology↗
USGS2025

Advancements in satellite observations of inland and coastal waters: Building towards a global validation network

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide suggestions for future field campaigns to improve coordination with remote sensing data collection and to ensure that field data is well suited for use in model or algorithm development.

Remote Sensing↗
USGS1996

Mapping playa evaporite minerals and associated sediments in Death Valley, California, with multispectral thermal infrared images

Efflorescent salt crusts and associated sediments in Death Valley, California, were studied with remote-sensing data acquired by the NASA thermal infrared multispectral scanner (TIMS). Nine spectral classes that represent a variety of surface materials were distinguished, including several classes that reflect important aspects of the playa groundwater chemistry and hydrology. Evaporite crusts containing abundant thenardite (sodium sulfate) were mapped along the northern and eastern margins of the Cottonball Basin, areas where the inflow waters are rich in sodium. Gypsum (calcium sulfate) crusts were more common in the Badwater Basin, particularly near springs associated with calcic groundwaters along the western basin margin. Evaporite-rich crusts generally marked areas where groundwater is periodically near the surface and thus able to replenish the crusts though capillary evaporation. Detrital silicate minerals were prevalent in other parts of the salt pan where shallow groundwater does not affect the surface composition. The surface features in Death Valley change in response to climatic variations on several different timescales. For example, salt crusts on low-lying mudflats form and redissolve during seasonal-to-interannual cycles of wetting and desiccation. In contrast, recent flooding and erosion of rough-salt surfaces in Death Valley probably reflect increased regional precipitation spanning several decades. Remote-sensing observations of playas can provide a means for monitoring changes in evaporite facies and for better understanding the associated climatic processes. At present, such studies are limited by the availability of suitable airborne scanner data. However, with the launch of the Earth Observing System (EOS) AM-1 Platform in 1998, multispectral visible/near-infrared and thermal infrared remote-sensing data will become globally available.

Journal of Geophysical Research B: Solid Earth↗
USGS2008

MODIS and SeaWIFS on-orbit lunar calibration

The Moon plays an important role in the radiometric stability monitoring of the NASA Earth Observing System's (EOS) remote sensors. The MODIS and SeaWIFS are two of the key instruments for NASA's EOS missions. The MODIS Protoflight Model (PFM) on-board the Terra spacecraft and the MODIS Flight Model 1 (FM1) on-board the Aqua spacecraft were launched on December 18, 1999 and May 4, 2002, respectively. They view the Moon through the Space View (SV) port approximately once a month to monitor the long-term radiometric stability of their Reflective Solar Bands (RSB). SeaWIFS was launched on-board the OrbView-2 spacecraft on August 1, 1997. The SeaWiFS lunar calibrations are obtained once a month at a nominal phase angle of 7??. The lunar irradiance observed by these instruments depends on the viewing geometry. The USGS photometric model of the Moon (the ROLO model) has been developed to provide the geometric corrections for the lunar observations. For MODIS, the lunar view responses with corrections for the viewing geometry are used to track the gain change for its reflective solar bands (RSB). They trend the system response degradation at the Angle Of Incidence (AOI) of sensor's SV port. With both the lunar observation and the on-board Solar Diffuser (SD) calibration, it is shown that the MODIS system response degradation is wavelength, mirror side, and AOI dependent. Time-dependent Response Versus Scan angle (RVS) Look-Up Tables (LUT) are applied in MODIS RSB calibration and lunar observations play a key role in RVS derivation. The corrections provided by the RVS in the Terra and Aqua MODIS data from the 412 nm band are as large as 16% and 13%, respectively. For SeaWIFS lunar calibrations, the spacecraft is pitched across the Moon so that the instrument views the Moon near nadir through the same optical path as it views the Earth. The SeaWiFS system gain changes for its eight bands are calibrated using the geometrically-corrected lunar observations. The radiometric corrections to the SeaWiFS data, after more than ten years on orbit, are 19% at 865 nm, 8% at 765 nm, and 1-3% in the other bands. In this report, the lunar calibration algorithms are reviewed and the RSB gain changes observed by the lunar observations are shown for all three sensors. The lunar observations for the three instruments are compared using the USGS photometric model. The USGS lunar model facilitates the cross calibration of instruments with different spectra bandpasses whose measurements of the Moon differ in time and observing geometry.

Conference Paper↗
USGS2013

Essential biodiversity variables

Reducing the rate of biodiversity loss and averting dangerous biodiversity change are international goals, reasserted by the Aichi Targets for 2020 by Parties to the United Nations (UN) Convention on Biological Diversity (CBD) after failure to meet the 2010 target (1, 2). However, there is no global, harmonized observation system for delivering regular, timely data on biodiversity change (3). With the first plenary meeting of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) soon under way, partners from the Group on Earth Observations Biodiversity Observation Network (GEO BON) (4) are developing—and seeking consensus around—Essential Biodiversity Variables (EBVs) that could form the basis of monitoring programs worldwide.

Science↗
USGS2001

The Landsat 7 mission: terrestrial research and applications for the 21st century

The Landsat Earth observation approach introduced in 1972 created a new way of monitoring land cover and land use globally. The Landsat 7 mission, successfully launched on April 15, 1999, continues those observations and demonstrates significant progress in precise numerical radiometry, spectral differentiation, and seasonally repetitive monitoring. Substantial improvements in calibration procedures, both prior to launch and during normal operations, have also been made to ensure long-term stability in the acquired spectral radiometry. Landsat 7 data acquisitions are being driven by a long-term data acquisition plan that was designed to ensure that substantially cloud-free, seasonal coverage would be recorded and archived in the US for all land areas of the globe. NASA competitively selected a Landsat Science Team, consisting of representatives from US universities and government agencies, to exploit the Landsat 7 record for global change research. This team is addressing the technical and analytical means to process and analyze the core of this observation record, and for the first time in the history of the Landsat mission, the technical and operational aspects of the mission are being driven by the goals of the US science community. The expected outcome of these efforts is a rapid improvement in understanding the Earth system, as well as conceptual knowledge that will underpin significant advancements in the application of this technology for commercial, operational, educational, and research purposes. Pathways to achieve effective Landsat continuity in the early decades of the 21st century are also being given careful attention, and there is no question that the lessons learned from the Landsat 7 mission will strongly influence these next-generation sensor systems.

Remote Sensing of Environment↗