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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Can We Better Constrain the Timing of GNAIW/UNADW Variability in the Western Equatorial Atlantic and Its Relationship to Climate Change During the Last Deglaciation?

We have revisited the well-trod VM28-122 core retrieved from the deep Colombian Basin, which includes sediments that reflect modern Upper North Atlantic Deep Water and extends through the last deglaciation into the last glacial period when the site was bathed in Glacial North Atlantic Intermediate Water. Here, we leverage the nearby Cariaco Basin's surface water radiocarbon reconstruction (reservoir age, and ΔR) on the IntCal20 timescale to recast the period of the last deglaciation with a newly constrained age model. Based on the revised age model, we observe that the multimillennial decrease in benthic δ 13 C and B/Ca (which record δ 13 C of dissolved inorganic carbon and Δ[CO 3 –2 ], respectively) began at 18,100 ± 240 (95% CI) calibrated years BP, synchronous with Termination 1, as identified by changes in the Antarctic Ice Sheet composite and by the onset of rapid glacier recession in the Southern Hemisphere (Denton et al., 2010, https://doi.org/10.1126/science.1184119; Denton et al., 2021, https://10.1016/j.quascirev.2020.106771). The beginning of the decrease in benthic δ 18 O is concurrent with the changes in carbon chemistry or at most, a few hundred years later. It is no later than 17,700 ± 300 (95% CI) yrs BP in our record, at the putative start of Heinrich Stadial 1. With sufficient data density (more than 2–3 control points per kyr) and an independent record of past surface water radiocarbon variations, it is possible to achieve late glacial and deglacial chronologies with fidelities similar to those of ice cores. Furthermore, doing so in more oceanographic locations should shed light more broadly on the mechanisms instrumental to abrupt climate change.

58 GEOSCIENCES↗
NASA NTRS2008

Quantitative Morphologic Analysis of Boulder Shape and Surface Texture to Infer Environmental History: A Case Study of Rock Breakdown at the Ephrata Fan, Channeled Scabland, Washington

Boulder morphology reflects both lithology and climate and is dictated by the combined effects of erosion, transport, and weathering. At present, morphologic information at the boulder scale is underutilized as a recorder of environmental processes, partly because of the lack of a systematic quantitative parameter set for reporting and comparing data sets. We develop such a parameter set, incorporating a range of measures of boulder form and surface texture. We use standard shape metrics measured in the field and fractal and morphometric classification methods borrowed from landscape analysis and applied to laser-scanned molds. The parameter set was pilot tested on three populations of basalt boulders with distinct breakdown histories in the Channeled Scabland, Washington: (1) basalt outcrop talus; (2) flood-transported boulders recently excavated from a quarry; and (3) flood-transported boulders, extensively weathered in situ on the Ephrata Fan surface. Size and shape data were found to distinguish between flood-transported and untransported boulders. Size and edge angles (approximately 120 degrees) of flood-transported boulders suggest removal by preferential fracturing along preexisting columnar joints, and curvature data indicate rounding relative to outcrop boulders. Surface textural data show that boulders which have been exposed at the surface are significantly rougher than those buried by fan sediments. Past signatures diagnostic of flood transport still persist on surface boulders, despite ongoing overprinting by processes in the present breakdown environment through roughening and fracturing in situ. Further use of this quantitative boulder parameter set at other terrestrial and planetary sites will aid in cataloging and understanding morphologic signatures of environmental processes.

Ehlmann, Bethany L.↗
NASA NTRS2018

The Influence of Prescribed Boundary Conditions on Near-Surface Temperature over the Arctic in the MERRA-2 Atmospheric Model

An accurate historical record of evolving Arctic conditions is integral to furthering our understanding of climate processes and to providing a foundation for predicting future climate scenarios in northern high latitudes. Atmospheric reanalyses are seen as an important source of information on the recent past for the data-sparse Arctic region. An assessment of near-surface Arctic air temperatures finds significant discrepancies among the various modern reanalyses. An important point is the treatment of surface boundary conditions: specifically, the sea ice cover and sea surface temperatures (SSTs) over the Arctic Ocean. Reanalyses use different methodologies and data sources for SSTs and sea ice concentration boundary forcing. Notably, the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) both use boundary forcing derived from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) over an extended, overlapping period of time. This allows for an examination of differences between the two systems while both concurrently employ the same fractional sea ice coverage. To further understand these differences, an ensemble of AMIP-style simulations using the MERRA-2 atmospheric model - but without data assimilation - shows considerable differences in Arctic temperatures as compared to reanalyses, particularly in autumn and winter months. Results from the AMIP simulations suggest that the surface representation over sea ice used in the MERRA-2 model provides an intrinsic warm bias and obfuscates Arctic Amplification, an established feature present in observations and reanalyses. An additional ensemble of AMIP-style simulations using the MERRA-2 atmospheric model was performed using boundary conditions derived from the ERA-Interim reanalysis. An in-depth comparison of surface temperatures over the Arctic from the two reanalyses and two AMIP-style ensembles will be presented, along with an assessment of the effects of the varying Arctic temperature time series on the atmospheric general circulation and energy budget.

Collow, Allison B. Marquardt↗
NASA NTRSDate not supplied

Nutrient Controls on Net Primary Production Decline in the Subtropical North Atlantic

The subtropical North Atlantic Ocean (STNA) has been experiencing a steady decline in both Chlorophyll a (the primary photosynthetic pigment in phytoplankton used to identify abundance in the ocean) and net primary production (the net amount carbon fixed in the surface ocean by phytoplankton after losses to respiration). This decline is spread across the entire STNA and has been ongoing since the beginning of the satellite ocean color record in 1997. Previous work has attributed these changes to decreased nutrient exchange between nutrient-poor surface waters and nutrient-rich deep waters resulting from increased stratification driven by surface warming due to climate change. Our results from an analysis of satellite data, Argo float data, and historical cruise data show significant inconsistencies with this hypothesis. We observe a trend of increased stratification for half the STNA basin but decreased stratification in the other half. Where we see increased stratification, we also see changes in nitrate that would result in an increased delivery of nitrate to surface waters if vertical transport across the stratified layers was the dominant source of nutrients. Our working hypothesis is that changes in circulation in the subtropical gyre have resulted in an altered mix of source waters with distinct nutrient characteristics. The mechanism behind the decline in primary production in the STNA remains unclear. We will use the ECCO-Darwin data-assimilative global ocean biogeochemistry model to further investigate the mechanisms behind the observed changes in production and nutrients in the STNA.

Nutrient↗
DOE OSTI2026

SetGo: Metadata Readiness for Scientific AI Datasets

Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publication and agent-based consumption. Existing FAIR assessors operate only on published repository records, and no single system covers FAIR compliance, licensing, provenance, governance, reproducibility, and catalog readiness together. We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness across these six dimensions before a dataset is published or archived. Applied to four scientific corpora, SetGo surfaces deficiencies that general-purpose tools do not detect: ERA5 climate metadata scores 4% on ACDD 1.3 compliance; materials datasets fail OPTIMADE species-definition requirements; and PDB-derived proteomics data carries licensing terms incompatible with standard SPDX identifiers. Guided enrichment raises overall FAIR scores from 52–57% to 81–91%, and a single setgo publish command pushes to Hugging Face Hub, CKAN, or OpenMetadata with ML Commons Croissant 1.0 metadata sidecars. To support interactive and automated workflows, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill that enables natural-language execution of the full assess–enrich–publish loop, with user involvement limited to supplying missing metadata values.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗
DOE OSTI2021

Natural variability contributes to model–satellite differences in tropical tropospheric warming

Significance Climate models have, on average, simulated substantially more tropical tropospheric warming than satellite data, with few simulations matching observations. It has been suggested that this discrepancy arises because climate models are overly sensitive to greenhouse gas increases. Tropical tropospheric temperature trends from a large ensemble of simulations performed with a single climate model span a wide range that is solely due to natural climate variability. A subset of these simulations have warming rates in accord with satellite observations. Simulations with diminished tropical tropospheric warming due to climate variability exhibit characteristic patterns of surface warming that are similar to the observed record. Our results indicate that multidecadal variability can explain current model–observational differences in the rate of tropical tropospheric warming.

58 GEOSCIENCES↗
NASA NTRS1990

Solar constant secular changes

A recent model for solar constant secular changes is used to calculate a 'proxy' solar constant for: (1) the past four centuries, based upon the sunspot record, (2) the past nine centuries, based upon C-14 observations and their relation to solar activity, and (3) the next decade, based upon a dynamo theory model for the solar cycle. The proxy solar constant data is tabulated as it may be useful for climate modelers studying global climate changes.

Schatten, Kenneth H.↗
NASA NTRS1990

The scatterometer - Data and applications

The techniques used to extract oceanographic and meteorological parameters from satellite radar scatterometer data are described and demonstrated, primarily on the basis of Seasat data. The principles of backscatter measurement and the general relationships between wind and wave fields are summarized, and the construction of geophysical data records from scatterometer swaths is explained. Particular attention is given to surface-layer dynamics and stresses; planetary-boundary-layer dynamics; averaging methods; the estimation of heat, moisture, and CO2 flux coefficients; the determination of pressure fields; storm monitoring; synoptic-scale analysis; climate analysis; and ocean dynamics. The capabilities and potential applications of the planned ERS-1, NSCAT, and SCANSCAT satellite scatterometers are briefly indicated.

Brown, R. A.↗
NASA NTRS2004

Lidar for Meteorologists 101

The following records were processed for inclusion into the NA&SD database:Basic Lidar Principles and Techniques; Applications I: Radiative Forcing and Climate; Boundary Layer and Pollution; Scattering of laser light:more than just smoke and mirrors; Applications II: Water Vapor and Atmospheric Dynamics; Regional and Global Data Available Now And In The Future.

Source record↗
NASA NTRS1993

Geophysics, Oceanography

Development of decade-long time series of global surface wind measurements for studies ofseasonal-to-interannual climate variability presents unique challenges for space- borne instrumentationbecause of the necessity to combine data sets of 3- to 5-year lifetimes. Before the first Special SensorMicrowave Imager (SSMI), which was launched on the Defence Meteorological Satellite Program(DMSP) F8 spacecraft in July 1987, stopped recording wind speed in December 1991, another SSMIwas launched on DMSP F10 in December 1991. Interpretation of the 1987 - 1993 composite timeseries is dependent upon the space and time characteristics of the differences between concurrent F8and F10 SSMI measurements. This paper emphasizes large geographical regions and 1-month timescale. The F8-F10 area-weighted difference between 60 degrees S and 60 degrees S during 305 daysof 1991 (-0.12 m s^(-1)) was comparable to the year-to-year wind speed variations during 1988-1991. The 10 degree-zonal averaged monthly mean F8-F10 difference was negative (positive) forwind speeds less (greater) than 7.9 m s^(-1), reaching - 0.43(0.32) m s^(-1) at 5(10) m s^(-1). The10 degree-zonal averaged monthly mean F8-F10 bias had considerable variations throughout the yearand between 60 degrees S - 60 degrees N, with the largest temporal variation (1.4 m s^(-1)) in the 50degrees - 60 degrees N region from February to April. The 1991 average value of the monthly meanroot-mean-square (rms) difference between F8 and F10 daily wind speeds in 10 degree-longitudinalbands was 2.0 m s^(-1) over 60 degrees S - 60 degrees N, the amplitude of the annual cycle of therms difference was largest in the northern hemisphere middle latitudes, and the rms difference wasrelated to the wind speed (e.g., at 6 and 10 m s^(-1), the rms difference was 1.7 and 2.7 m s^(-1),respectively). The relationship between monthly mean 1/3 degrees x 1/3 degrees F8-F10 SSMI windspeed differences and integrated water vapor and liquid water content in the atmosphere is discussed.

Halpern, D.↗
NASA NTRS2015

A New Metric for Indian Monsoon Rainfall Extremes

Extreme monsoon rainfall in India has disastrous consequences, including significant socio- economic impacts. However, little is known about the overall trends and climate factors associated with extreme rainfall because rainfall greatly varies across India and because few appropriate methods are available to measure extreme rainfall in the context of such heterogeneity. To provide a comprehensive assessment of extreme monsoon rainfall, we developed a metric using record rainfall data to measure the changes in the likelihood of extreme high and extreme low rainfall over time; this metric is independent of the characteristics of the underlying rainfall distributions. Hence, the metric is ideally suited to aggregate extreme rainfall information across heterogeneous regions covering India. We found that from 1930 to 2013, the likelihood of extreme high and extreme low rainfall increases 2-fold and 4-fold, respectively. These overall trend increases are driven by anomalous increases, particularly in the early 2000s; the likelihood of extreme high and extreme low rainfall increases 5-fold and 18-fold in 2005 and 2002, respectively. These findings imply a broadening of the underlying monsoon rainfall distribution over the past century. We also show that the time patterns of the likelihood of extreme rainfall in recent decades are correlated with the El Nino Southern Oscillation, Indian Ocean Dipole, and surface air temperature in the Northern Hemisphere.

Rain↗
DOE OSTI2026

NGEE Arctic Integrated Modeling (IM3): Improved snow-vegetation interaction

This data product represents the integration of new code capability for arctic tundra snow-vegetation-terrain interactions into the Energy Exascale Earth System Model (E3SM), through the E3SM Land Model (ELM) component. This code integration is the result of collaborative effort between the NGEE Arctic project and the E3SM project. The NGEE Arctic project developed a total of six Integrated Modeling (IM) modules informed by observations and experiments. New ELM capability represented by this data product (IM3) falls into three categories: 1) Downscaling from gridcell to topographic unit level when working through the existing coupler bypass code. 2) Four new parameters (taper, stocking, bendresist, and vegshape) have been added to ELM to allow for flexible definition of snow-vegetation interactions. 3) Vegshape and bendresist parameters are used to calculate the fraction of leaf area and/or stem area buried by snow for a given snow depth. This data record consists of a single document (pdf format) that describes the theoretical basis for the snow-vegetation-terrain interactions added to ELM, and describes the modifications made to the ELM code. The Methods section of this metadata record includes a link to the public E3SM code repository where the exact code modifications as integrated in E3SM can be accessed. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Thornton, Peter E [ORNL] (ORCID:0000000247595158)↗
NASA NTRS2011

Model, Proxy and Isotopic Perspectives on the East African Humid Period

Both North and East Africa experienced more humid conditions during the early and mid-Holocene epoch (11,000-5000yr BP; 11-5 ka) relative to today. The North African Humid Period has been a major focus of paleoclimatic study, and represents a response of the hydrological cycle to the increase in boreal summer insolation and associated ocean, atmosphere and land surface feedbacks. Meanwhile, the mechanisms that caused the coeval East African Humid Period are poorly understood. Here, we use results from isotopeenabled coupled climate modeling experiments to investigate the cause of the East African Humid Period. The modeling results are interpreted alongside proxy records of both water balance and the isotopic composition of rainfall. Our simulations show that the orbitally-induced increase in dry season precipitation and the subsequent reduction in precipitation seasonality can explain the East African Humid Period, and this scenario agrees well with regional lake level and pollen paleoclimate data. Changes in zonal moisture flux from both the Atlantic and Indian Ocean account for the simulated increase in precipitation from June through November. Isotopic paleoclimate data and simulated changes in moisture source demonstrate that the western East African Rift Valley in particular experienced more humid conditions due to the influx of Atlantic moisture and enhanced convergence along the Congo Air Boundary. Our study demonstrates that zonal changes in moisture advection are an important determinant of climate variability in the East African region.

Tierney, Jessica E.↗
NASA NTRS2023

ENSO and MJO Modulation of U.S. Cloud-to-Ground Lightning Activity

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.

Lightning↗
DOE OSTI2024

Informing forest carbon inventories under the Paris Agreement using ground-based forest monitoring data

Human interactions with forests have shaped Earth's climate for millennia and will continue to do so as we target net-zero emission goals. Accurately characterizing these climate impacts requires making reliable forest carbon data available for forest monitoring and planning. Here, we develop a semi-automated process for submitting forest carbon measurements from the largest relevant scientific database to the International Panel on Climate Change's Emission Factor Database, which currently has sparse forest carbon data. Building this bridge from scientific research to international policy is an important step towards managing forests in a net-zero motivated future. Humans have been influencing Earth's climate via transformative impacts on forests for millennia, and forests are now recognized as critical to climate change mitigation under the Paris Agreement. The efficacy of climate change mitigation planning and reporting depends on quality data on forest carbon (C) stocks and changes. The Emission Factor Database (EFDB) of the International Panel on Climate Change (IPCC) is intended to be a definitive source for such data, but needs comprehensive and well-documented data to be so. To facilitate submission of forest C estimates from scientific studies to EFDB, we develop and document a process for semi-automated data submission from the Global Forest C database (ForC v4.0), which is the largest compilation of ground-based forest C estimates. We then assess the data currently available through ForC and provide recommendations for improving forest data collection, analysis, and reporting. As of September 2024, ForC contained ~19,286 records potentially relevant to EFDB, 1068 of which had been submitted and posted to EFDB. These represented 19% of the total EFDB records for forest land. Records were unevenly distributed across variables and geographic regions. ForC records (37%) reviewed could not be submitted because the original publication lacked required information. In the future, ground-based forest C estimates should target gaps in the record, and studies should ensure that they report all information necessary for inclusion in EFDB. Given that climate change is rapidly impacting the world's forests, timely reporting of recent estimates will be critical to accurate forest C inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

Satellite Altimetry, Ocean Circulation, and Data Assimilation

Ocean circulation is a critical factor in determining the Earth's climate. Satellite altimetry has been proven a powerful technique for measuring the height of the sea surface for the study of global ocean circulation dynamics. A major objective of my research is to investigate the utility of altimeter data for ocean circulation studies. The 6 years' data record of TOPEX/POSEIDON have been analyzed to study the spatial and temporal characteristics of large-scale ocean variability. A major result obtained in 1998 is the discovery of large-scale oscillations in sea level with a period of 25 days in the Argentine Basin of the South Atlantic Ocean (see diagram). They exhibit a dipole pattern with counterclockwise rotational propagation around the Zapiola Rise (centered at 45S and 317E), a small seamount in the abyssal plain of the basin. The peak-to-trough amplitude is about 10 cm over a distance of 500-1000 km. The amplitude of these oscillations has large seasonal-to-interannual variations. The period and rotational characteristics of these oscillations are remarkably similar to the observations made by two current meters deployed near the ocean bottom in the region. What TOPEX/POSEIDON has detected apparently are manifestations of the movement of the entire water column (barotropic motion). The resultant transport variation is estimated to be about 50 x 10(exp 6) cubic M/S, which is about 50% of the total water transport in the region. Preliminary calculations suggest that these oscillations are topographically trapped waves. A numerical model of the South Atlantic is used to investigate the nature of and causes for these waves. A very important property of sea surface height is that it is directly related to the surface geostrophic velocity, which is related to deep ocean circulation through the density field. Therefore altimetry observations are not only useful for determining the surface circulation but also for revealing information about the deep ocean. Another objective of my research is to investigate the extent of information one can infer about the deep ocean from altimetric observations. The approach is data assimilation by ocean models. The objective is to develop a modeling/data assimilation system that will produce estimates of the 3-dimensional state of the ocean for the entire duration of the TOPEX/POSEIDON Mission.

Fu, Lee-Lueng↗
NASA NTRS2004

Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991).

Huete, Alfredo↗