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At least 127 records · Page 7Linked to original sources
USGS2015

Towards a global terrestrial species monitoring program

Introduction: The Convention for Biological Diversity’s (CBD) Strategic Plan for Biodiversity 2011-2020 envisions that “By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people.” Although 193 parties have adopted these goals, there is little infrastructure in place to monitor global biodiversity trends. Recent international conservation policy requires such data to be up-to-date, reliable, comparable among sites, relevant, and understandable; as is becoming obvious from the work plan adopted by the Intergovernmental Panel for Biodiversity and Ecosystem Services (IPBES: www.ipbes.net/; http://tinyurl.com/ohdnknq). In order to meet the five strategic goals of the Strategic Plan for Biodiversity 2011-2020 and its 20 accompanying Aichi Targets for 2020 (www.cbd.int/sp/targets/), advances need to be made in coordinating large-scale biodiversity monitoring and linking these with environmental data to develop a comprehensive Global Observation Network, as is the main idea behind GEOSS the Global Earth Observation System of Systems (Christian 2005)...Here we identify ten requirements important for the successful implementation of a global biodiversity monitoring network under the flag of GEO BON and especially a global terrestrial species monitoring program.

Journal for Nature Conservation↗
USGS2019

Landsat: The cornerstone of global land imaging

Since 1972, the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites have provided an uninterrupted space-based data record of the Earth’s land surface to help advance scientific research towards the understanding of our planet and the environmental impact of its inhabitants. Early Landsat satellites offered a wealth of new data that improved mapping of remote areas and geologic features along with digital analysis of vegetation. The utility of Landsat’s spatial and spectral resolution has advanced its use for applications that benefit society such as global crop forecasting, forest monitoring, water use, carbon assessments, and the base for Google Maps. Landsat’s long-term data record provides an unrivaled resource for observing land cover and land-use change over a timescale of decades. The free and open Landsat data policy in 2008 was a paradigm shift for the world. Today, due to improved analytical and computing capabilities, the Landsat archive is poised to shift into a more real-time monitoring and understanding of the Earth.

GIM International↗
USGS2018

Advancing marine biological observations and data requirements of the complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) Frameworks

Measurements of the status and trends of key indicators for the ocean and marine life are required to inform policy and management in the context of growing human uses of marine resources, coastal development, and climate change. Two synergistic efforts identify specific priority variables for monitoring: Essential Ocean Variables (EOVs) through the Global Ocean Observing System (GOOS), and Essential Biodiversity Variables (EBVs) from the Group on Earth Observations Biodiversity Observation Network (GEO BON). Both systems support reporting against internationally agreed conventions and treaties. GOOS, established under the auspices of the Intergovernmental Oceanographic Commission (IOC), plays a leading role in coordinating global monitoring of the ocean and in the definition of EOVs. GEO BON is a global biodiversity observation network that coordinates observations to enhance management of the world’s biodiversity and promote both the awareness and accounting of ecosystem services. Convergence and agreement between these two efforts are required to streamline existing and new marine observation programs to advance scientific knowledge effectively and to support the sustainable use and management of ocean spaces and resources. In this context, the Marine Biodiversity Observation Network (MBON), a thematic component of GEO BON, is collaborating with GOOS, the Ocean Biogeographic Information System (OBIS), and the Integrated Marine Biosphere Research (IMBeR) project to ensure that EBVs and EOVs are complementary, representing alternative uses of a common set of scientific measurements. This work is informed by the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM), an intergovernmental body of technical experts that helps international coordination on best practices for observing, data management and services, combined with capacity development expertise. Characterizing biodiversity and understanding its drivers will require incorporation of observations from traditional and molecular taxonomy, animal tagging and tracking efforts, ocean biogeochemistry, and ocean observatory initiatives including deep ocean and seafloor. The partnership between large-scale ocean observing and product distribution initiatives (MBON, OBIS, JCOMM, and GOOS) is an expedited, effective way to support international policy-level assessments (e.g., the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services or IPBES), along with the implementation of international development goals (e.g., the United Nations Sustainable Development Goals).

Frontiers in Marine Science↗
USGS2021

Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.

Remote Sensing↗
USGS1998

Mapping the global land surface using 1 km AVHRR data

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Space Technology↗
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↗
USGS2007

Cross-calibration of the Terra MODIS, Landsat 7 ETM+ and EO-1 ALI sensors using near-simultaneous surface observation over the Railroad Valley Playa, Nevada, test site

A cross-calibration methodology has been developed using coincident image pairs from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS), the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Earth Observing EO-1 Advanced Land Imager (ALI) to verify the absolute radiometric calibration accuracy of these sensors with respect to each other. To quantify the effects due to different spectral responses, the Relative Spectral Responses (RSR) of these sensors were studied and compared by developing a set of "figures-of-merit." Seven cloud-free scenes collected over the Railroad Valley Playa, Nevada (RVPN), test site were used to conduct the cross-calibration study. This cross-calibration approach was based on image statistics from near-simultaneous observations made by different satellite sensors. Homogeneous regions of interest (ROI) were selected in the image pairs, and the mean target statistics were converted to absolute units of at-sensor reflectance. Using these reflectances, a set of cross-calibration equations were developed giving a relative gain and bias between the sensor pair.

Conference Paper↗