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USGS2024

System characterization report on the Gaofen-6

Executive Summary Gaofen-6 represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellites in this series is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium.” The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances of Gaofen-6. Results of these analyses indicate that Gaofen-6 has an interior geometric performance root mean square error ranging from 2.84 meters (m; 0.18 pixel) to 7.42 m (0.46 pixel) in easting and from 2.84 m (0.18 pixel) to 11.57 m (0.72 pixel) in northing in band-to-band registration, an exterior geometric performance root mean square error ranging from 154.50 m (8.80 pixels) in easting to 14.65 m (0.80 pixel) in northing in comparison to a corresponding Sentinel-2 scene, a radiometric performance ranging from 0.018 to 0.055 (in offset) and from 0.620 to 0.858 (in slope), and a spatial performance ranging from 2.10 to 2.30 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency ranging from 0.040 to 0.055.

Open-File Report↗

USGS2010

An overview of the Landsat Data Continuity Mission

The Landsat Data Continuity Mission (LDCM) is the follow-on mission to Landsat 7 and will be the eighth mission in the Landsat series. The mission is in development via an interagency partnership between the National Aeronautics and Space Administration (NASA) and the Department of Interior (DOI) / United States Geological Survey (USGS). The LDCM satellite will carry two earth-observing sensors, the Operational Land Imager (OLI) to collect image data for nine spectral bands in the reflective portion of the spectrum and the Thermal Infrared Sensor (TIRS) to collect coincident image data for two thermal spectral bands. The LDCM ground segment will control the satellite and will receive, process, archive, and distribute the science data collected by the OLI and TIRS instruments. The USGS Earth Resources Observation & Science Center (EROS) will distribute LDCM data products at no cost to requestors. The mission objective is to continues the Landsat program's collection, archive, and distribution of multispectral imagery affording global, synoptic, and repetitive coverage of the Earth's land surfaces at a scale where natural and human-induced changes can be detected, differentiated, characterized, and monitored over time. The LDCM launch readiness date is currently December, 2012.

Conference Paper↗

USGS2001

Satellite images for land cover monitoring - Navigating through the maze

Policy makers, managers, scientists and the public can view the changing environment using satellite images. More than 60 Earth observing satellites are collecting images of the Earth's surface. Remote sensing satellite systems for land cover assessment are operated by a growing number of countries including India, the United States, Japan, France, Canada and Russia. The focus of this publication is satellite systems for land cover monitoring. On the reverse is a table that compares a selection of these systems, whose data are globally available in a form suitable for land cover analysis. We hope the information presented will help you assess the utility of remotely sensed image to meet your needs.

Report↗

USGS2009

Foreword to the issue on remote sensing of regional land use and land cover

This issue showcases some good work from Chinese authors who attended the International Workshop of Earth Observation and Remote Sensing Applications (EORSA), held in Beijing, China, June 30-July 2, 2008. The five articles in this issue focus on remote sensing of regional land use and land cover.

IEEE Journal of Selected Topics in Applied Earth O↗

USGS2007

Consistency of L4 TM absolute calibration with respect to the L5 TM sensor based on near-simultaneous image acquisition

The Landsat archive provides more than 35 years of uninterrupted multispectral remotely sensed data of Earth observations. Since 1972, Landsat missions have carried different types of sensors, from the Return Beam Vidicon (RBV) camera to the Enhanced Thematic Mapper Plus (ETM+). However, the Thematic Mapper (TM) sensors on Landsat 4 (L4) and Landsat 5 (L5), launched in 1982 and 1984 respectively, are the backbone of an extensive archive. Effective April 2, 2007, the radiometric calibration of L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) was updated to use an improved lifetime gain model, based on the instrument's detector response to pseudo-invariant desert site data and cross-calibration with the L7 ETM+. However, no modifications were ever made to the radiometric calibration procedure of the Landsat 4 (L4) TM data. The L4 TM radiometric calibration procedure has continued to use the Internal Calibrator (IC) based calibration algorithms and the post calibration dynamic ranges, as previously defined. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The number of coincident image pairs in the USGS EROS archive is limited, so the scene selection for the cross-calibration studies proved to be a challenge. Additionally, because of the lack of near-simultaneous images available over well-characterized and traditionally used calibration sites, alternate sites that have high reflectance, large dynamic range, high spatial uniformity, high sun elevation, and minimal cloud cover were investigated. The alternate sites were identified in Yuma, Iraq, Egypt, Libya, and Algeria. The cross-calibration approach involved comparing image statistics derived from large common areas observed eight days apart by the two sensors. This paper summarizes the average percent differences in reflectance estimates obtained between the two sensors. The work presented in this paper is a first step in understanding the current performance of L4 TM absolute calibration and potentially serves as a platform to revise and improve the radiometric calibration procedures implemented for the processing of L4 TM data.

Conference Paper↗

USGS2010

The ASTER data system: An overview of the data products in Japan and in the United States

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data system is a cooperative system, which is operated jointly by Japan’s Ministry of Economy, Trade, and Industry (METI) through its Earth Remote Sensing Data Analysis Center (ERSDAC), and by the National Aeronautics and Space Administration (NASA) primarily through its Goddard Space Flight Center (GSFC) and Land Processes (LP) Distributed Active Archive Center (DAAC). ASTER is a moderate-resolution land remote sensing system onboard the Earth Observing System (EOS) Terra spacecraft. ASTER-acquired data are received at the White Sands, New Mexico, ground receiving station, and then transmitted via land network to the EOS Data and Operations System (EDOS) within the Goddard DAAC, located at the GSFC. EDOS pre-processes raw ASTER data to Level-0 (L0) data, and sends them via the high-speed Asia-Pacific Advanced Network (APAN) to the ASTER Ground Data System (GDS) in Japan. ASTER GDS processes the L0 data to level-1 (L1) datasets; they distribute these data to users, and also use them to generate higher-level products for their user community. ASTER GDS sends a copy of all L1A data they produce to NASA’s LP DAAC, located at the U.S. Geological Survey’s Center for Earth Resources Observation and Science (EROS) near Sioux Falls, South Dakota. All L1 data received from Japan are ingested, archived, and available for users at LP DAAC. The LP DAAC also generates and distributes higher-level products from L1 data based on requests from users. To meet time-critical needs related to sensor health and performance, natural disasters, national emergencies, and certain field campaigns, the ASTER Expedited Data System (EDS) was developed, and is operated jointly by U.S. and Japanese partners.

Book chapter↗

USGS2026

Satellite embeddings for crop type classification: A comparative examination

Embedding datasets encode complex relationships among multiple sources of Earth observation data into a compact format. Here, we evaluated the utility of a 10-m global Satellite Embedding product (SE) for classifying crop types in central California for the year 2020. We compared the classification accuracy of a random forest model based exclusively on the SE layer to an existing random forest model with multiple imagery inputs. Our results showed the SE-based classification had higher agreement with the reference dataset (California Department of Water Resources crop map) than the classification based on Landsat and National Agricultural Imagery Program inputs (94.7% versus. 91.9% overall accuracy, respectively). The performance of individual crop types was consistent across models, ranging from high agreement for rice (98.4% versus 98% accuracy) to lower agreement for pasture, grain, and fallow/young perennial classes (< 65% accuracy in both models). The SE-based workflow used three times less cloud-based computational resources and represented substantial savings of predictor development time. Geospatial embedding products can aid classification efforts by reducing predictor development time and processing demands while maintaining classification accuracy.

California↗

USGS2013

Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy ~70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using ~20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was ~ 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or “the curse of high dimensionality”) in hyperspectral data for a particular application (e.g., biophysi- al characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI.

Africa;Central Asia;Middle East↗

USGS2013

EO-1 Hyperion reflectance time series at calibration and validation sites: stability and sensitivity to seasonal dynamics

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12+ year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO 2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

Nevada↗

USGS2011

Terrestrial essential climate variables (ECVs) at a glance

The Global Terrestrial Observing System, Global Climate Observing System, World Meteorological Organization, and Committee on Earth Observation Satellites all support consistent global land observations and measurements. To accomplish this goal, the Global Terrestrial Observing System defined 'essential climate variables' as measurements of atmosphere, oceans, and land that are technically and economically feasible for systematic observation and that are needed to meet the United Nations Framework Convention on Climate Change and requirements of the Intergovernmental Panel on Climate Change. The following are the climate variables defined by the Global Terrestrial Observing System that relate to terrestrial measurements. Several of them are currently measured most appropriately by in-place observations, whereas others are suitable for measurement by remote sensing technologies. The U.S. Geological Survey is the steward of the Landsat archive, satellite imagery collected from 1972 to the present, that provides a potential basis for deriving long-term, global-scale, accurate, timely and consistent measurements of many of these essential climate variables.

Scientific Investigations Map↗

USGS2011

Globally Gridded Satellite observations for climate studies

Geostationary satellites have provided routine, high temporal resolution Earth observations since the 1970s. Despite the long period of record, use of these data in climate studies has been limited for numerous reasons, among them that no central archive of geostationary data for all international satellites exists, full temporal and spatial resolution data are voluminous, and diverse calibration and navigation formats encumber the uniform processing needed for multisatellite climate studies. The International Satellite Cloud Climatology Project (ISCCP) set the stage for overcoming these issues by archiving a subset of the full-resolution geostationary data at ~10-km resolution at 3-hourly intervals since 1983. Recent efforts at NOAA's National Climatic Data Center to provide convenient access to these data include remapping the data to a standard map projection, recalibrating the data to optimize temporal homogeneity, extending the record of observations back to 1980, and reformatting the data for broad public distribution. The Gridded Satellite (GridSat) dataset includes observations from the visible, infrared window, and infrared water vapor channels. Data are stored in Network Common Data Format (netCDF) using standards that permit a wide variety of tools and libraries to process the data quickly and easily. A novel data layering approach, together with appropriate satellite and file metadata, allows users to access GridSat data at varying levels of complexity based on their needs. The result is a climate data record already in use by the meteorological community. Examples include reanalysis of tropical cyclones, studies of global precipitation, and detection and tracking of the intertropical convergence zone.

Bulletin of the American Meteorological Society↗

USGS2021

Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements

Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different EOS requires compensating for atmospheric and view angle effects before integrating comparable surface reflectance (SR) values. The objectives of this study were i) to assess how different atmospheric constituents affect the atmospheric correction results in Sentinel-2 and WorldView-3 imagery, ii) to establish a relationship with field spectra measurements, and iii) to develop an empirical approach to ensure that SR values extracted from different EOS can be normalized for use in monitoring vegetation and land cover status. We compared surface reflectance values derived from Sentinel-2 images corrected with Sen2Cor, MODTRAN or FLAASH atmospheric correction approaches for the visible-to-near infrared regions. Additionally, this information was compared to SR values extracted from WorldView-3 imagery acquired from the same dates and location (Central Spain) and corrected with MODTRAN and FLAASH approaches. Assessment of the atmospheric correction was conducted by comparing satellite image SR with ground-truth spectra acquired with a FieldSpec hand-held spectroradiometer. The results emphasized the importance of using common atmospheric parameters collected from ancillary data sources (i.e. MODIS Atmosphere & Land products) to ensure a reliable SR comparison. When compared to field-collected spectral data, SR from corrected Sentinel-2 push-broom imagery showed a reliable match (<4% difference in the visible bands and <0.52% difference in the near infrared bands). However, SR imagery from the pointable WorldView-3 instrument showed significant deviation, likely resulting from the effects of steep off-nadir acquisition angles (24.6° to 39.1°) combined with surface anisotropy. The magnitude and sign of the deviation in SR differed depending on the vegetation type, wavelength and sun-surface-sensor geometry. Therefore, it was necessary to account for angular effects to ensure reliable comparisons of imagery from the different EOS. In this study, an empirical angular correction approach was developed based on calibrating each WorldView-3 band against the ground-truth spectra. This correction allowed for the accurate signal normalization of WorldView-3 and Sentinel-2 imagery SR in the visible-to-near infrared regions.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2024

Lake water temperature modeling in an era of climate change: Data sources, models, and future prospects

Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process-based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process-based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

Review of Geophysics↗

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↗