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At least 73 records · Page 4

Bright sand/dark dust: The identification of active sand surfaces on the Earth and Mars

Field studies and analysis of LANDSAT Thematic Mapper data in the Gran Desierto, Mexico may shed light on a technique to distinguish active from inactive (relict) sand surfaces. Active sand bodies in the study area are consistently brighter (by an average of 20%) at visual and near infrared wavelengths and darker at thermal infrared wavelengths than compositionally similar inactive sands. The reasons for the albedo difference between active and inactive sands are reviewed and the mixing model of Johnson et al. is examined for tracing the provenance of sands based on albedo and spectral variations. Portions of the wavelengths covered by the Mars Orbiter correspond to the Thematic Mapper data. The identification of active sands on Earth, with a priori knowledge of bulk composition and grain size distribution, may allow the remote mapping of active sand surfaces on Mars. In conjuction with thermal infrared remote sensing for composition, it may also provide a method for the remote determination of grain size distributions within sand/silt mixtures.

Blount, H. G., II↗

Global Land Surface Soil Moisture Database Derived from IASI Measurements on Board MetOp Satellites

Global land surface soil moisture has been derived from MetOp IASI ultraspectral infrared measurements over the past 16 years. Monthly and spatially gridded soil moisture is produced to show some phenomena associated with its natural variability. This study has aimed to demonstrate that thermal infrared remote sensing data can be used for monitoring global surface environmental characteristics and associated change through the continuity observations provided by the MetOp series. The time-series of soil moisture can be used to estimate its associated trend.

Remote sensing↗

International Geoscience and Remote Sensing Symposium, Washington, DC, June 8-10, 1981, Digest. Volumes 1 & 2

Sensors used for the geophysical and remote sensing studies are reviewed; in particular, NIMBUS-7 Scanning Multichannel Microwave Radiometer results are given. Remote sensing analyses consider such areas as hydrology, soil moisture, and planetary surfaces. In addition, Shuttle remote sensing experiments and SAR systems, remote sensing in the thermal infrared, and microwave remote sensing of the earth are considered. Finally, attention is given to lithologic and geobotanical studies, and geophysical sensing techniques and inversion theory.

Source record↗

Data for A Hybrid Biophysical-Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81–0.94) and H (R2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

AI/ML↗

Determination of rock type on Mercury and the moon through remote sensing in the thermal infrared

Thermal infrared emission spectra of the moon and Mercury have been obtained using the Si:As photoconductor and circular variable filter at the NASA Infrared Telescope Facility on Mauna Kea, Hawaii. Lunar spectra from 7.2 to 12.2 microns for two different locations in the south polar highlands have Christiansen frequency peaks at 8.1 microns and 7.9 microns, respectively. This indicates different compositions at the two locations; mafic in the first case, more felsic in the second. Emission spectra from Mercury are not as spatially localized,; however, the longitude of maximum contribution to the spectrum can be calculated from thermal models of the earth-facing disk. Results for areas centered at two longitudes have been obtained. Two locations in the intercrater plains were observed. At 40-deg longitude (very near the crater Homer), a peak at 7.9 microns indicates mafic igneous rock type. Spectra emanating from 46-deg longitude have peaks at 7.8 microns, indicating a region borderline between mafic and intermediate composition.

Tyler, Ann L.↗

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗

Thermal Infrared Spectroscopy from Mars Landers and Rovers: A New Angle on Remote Sensing

The MINUTES instrument of the Athena Precursor Experiment (APEX) on the Mars Surveyor 2001 lander mission will perform the first thermal infrared remote sensing observations from the surface of another planet. Experience gained from this experiment will be used to guide observations from identical instruments mounted on the Athena rovers, to be launched in 2003 and 2005. The utility of infrared spectrometers in determining the mineralogic composition of geologic surfaces from airborne and spaceborne platforms has been amply demonstrated. However, relatively little experience exists in using functionally similar instruments on the ground in the context of planetary science. What work has been done on this problem has mostly utilized field spectrometers that are designed to look down on nearby target rocks. While many Mini-TES observations will be made with this type of geometry, it is likely that other observations will be made looking horizontally at the more vertically-oriented facets of rock targets, to avoid spectral contamination from dust mantles. On rover missions, the Mini-TES may also be pointed horizontally at rocks several meters away, to determine if they are worthy of approaching for in situ observations and possible sample cacheing. While these observations will undoubtedly prove useful, there are important, and perhaps unappreciated, differences between horizontal-viewing, surface-based spectroscopy and the more traditional nadir-viewing, orbit or aircraft-based observations. Plans also exist to step the Mini-TES in a rastering motion to build hyperspectral scenes. Horizontal viewing hyperspectral cubes also possess unique qualities that call for innovative analysis techniques. The effect of viewing geometry: In thermal emission spectroscopy, regardless of whether an instrument is looking down on or horizontally at a target, the same basic equation governs the radiance reaching the sensor .

Moersch, J.↗

Mapping variations in weight percent silica measured from multispectral thermal infrared imagery - Examples from the Hiller Mountains, Nevada, USA and Tres Virgenes-La Reforma, Baja California Sur, Mexico

Remotely sensed multispectral thermal infrared (8-13 mu m) images are increasingly being used to map variations in surface silicate mineralogy. These studies utilize the shift to longer wavelengths in the main spectral feature in minerals in this wavelength region (reststrahlen band) as the mineralogy changes from felsic to mafic. An approach is described for determining the amount of this shift and then using the shift with a reference curve, derived from laboratory data, to remotely determine the weight percent SiO2 of the surface. The approach has broad applicability to many study areas and can also be fine-tuned to give greater accuracy in a particular study area if field samples are available. The approach was assessed using airborne multispectral thermal infrared images from the Hiller Mountains, Nevada, USA and the Tres Virgenes-La Reforma, Baja California Sur, Mexico. Results indicate the general approach slightly overestimates the weight percent SiO2 of low silica rocks (e.g. basalt) and underestimates the weight percent SiO2 of high silica rocks (e.g. granite). Fine tuning the general approach with measurements from field samples provided good results for both areas with errors in the recovered weight percent SiO2 of a few percent. The map units identified by these techniques and traditional mapping at the Hiller Mountains demonstrate the continuity of the crystalline rocks from the Hiller Mountains southward to the White Hills supporting the idea that these ranges represent an essentially continuous footwall block

Stock, Joann M.↗

Conceptual thermal design and analysis of a far-infrared/mid-infrared remote sensing instrument

This paper presents the conceptual thermal design and analysis results for the Spectroscopy of the Atmosphere using Far-Infrared Emission (SAFIRE) instrument. SAFIRE has been proposed for Mission to Planet Earth to study ozone chemistry in the middle atmosphere using remote sensing of the atmosphere in the far-infrared (21-87 microns) and mid-infrared (9-16 microns) spectra. SAFIRE requires that far-IR detectors be cooled to 3-4 K and mid-IR detectors to 80 K for the expected mission lifetime of five years. A superfluid helium dewar and Stirling-cycle cryocoolers provide the cryogenic temperatures required by the infrared detectors. The proposed instrument thermal design uses passive thermal control techniques to reject 465 watts of waste heat from the instrument.

Roettker, William A.↗

Introduction to the Special Session on Thermal Remote Sensing Data for Earth Science Research: The Critical Need for Continued Data Collection and Development of Future Thermal Satellite Sensors

There is a rich and long history of thermal infrared (TIR) remote sensing data for multidisciplinary Earth science research. The continuity of TIR data collection, however, is now in jeopardy given there are no planned future Earth observing TIR remote sensing satellite systems with moderately high spatial resolutions to replace those currently in orbit on NASA's Terra suite of sensors. This session will convene researchers who have actively worked in the field of TIR remote sensing to present results that elucidate the importance of thermal remote sensing to the wider Earth science research community. Additionally, this session will also exist as a forum for presenting concepts and ideas for new thermal sensing systems with high spatial resolutions for future Earth science satellite missions, as opposed to planned systems such as the Visible/Infrared Imager/Radiometer (VIIRS) suite of sensors on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) that will collect TIR data at very coarse ~~~iairesolutions.

Quattrochi, Dale a.↗

Assimilation of Satellite Derived Soil Moisture Profiles into a Crop Modeling System for Robust Yield Estimates

Soil Moisture Measurement - Remote Sensing. - Microwave (MW) Remote Sensing: Physically based and quantitative in nature; Based on difference in dielectric constant; Coarse spatial resolution 25-40 km; Shallow SM estimation depth 0-5 cm (approx.); All weather capabilities (e.g. Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) etc.) - Thermal Infrared (TIR) Remote Sensing: Indirect SM retrieval through energy flux estimations; Relatively better spatial resolution 1-10 km; Root-zone moisture retrieval capability; Can not penetrate through clouds, hence data gaps (e.g. Surface Energy Balance Algorithm for Land (SEBAL), Atmospheric Land Exchange Inverse (ALEXI) etc.)

SERVIR↗

Remotely Sensed High‐Resolution Soil Moisture and Evapotranspiration: Bridging the Gap Between Science and Society

This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.

54 ENVIRONMENTAL SCIENCES↗

The Need for High Spatial Resolution Multispectral Thermal Remote Sensing Data In Urban Heat Island Research

Although the study of the Urban Heat Island (UHI) effect dates back to the early 1800's when Luke Howard discovered London s heat island, it has only been with the advent of thermal remote sensing systems that the extent, characteristics, and impacts of the UHI have become to be understood. Analysis of the UHI effect is important because above all, this phenomenon can directly influence the health and welfare of urban residents. For example, in 1995, over 700 people died in Chicago due to heat-related causes. UHI s are characterized by increased temperature in comparison to rural areas and mortality rates during a heat wave increase exponentially with the maximum temperature, an effect that is exacerbated by the UHI. Aside from the direct impacts of the UHI on temperature, UHI s can produce secondary effects on local meteorology, including altering local wind patterns, increased development of clouds and fog, and increasing rates of precipitation either over, or downwind, of cities. Because of the extreme heterogeneity of the urban surface, in combination with the sprawl associated with urban growth, thermal infrared (TIR) remote sensing data have become of significant importance in understanding how land cover and land use characteristics affect the development and intensification of the UHI. TIR satellite data have been used extensively to analyze the surface temperature regimes of cities to help observe and measure the impacts of surface temperatures across the urban landscape. However, the spatial scales at which satellite TIR data are collected are for the most part, coarse, with the finest readily available TIR data collected by the Landsat ETM+ sensor at 60m spatial resolution. For many years, we have collected high spatial resolution (10m) data using an airborne multispectral TIR sensor over a number of cities across the United States. These high resolution data have been used to develop an understanding of how discrete surfaces across the urban environment (e.g., rooftops, pavements) interact from a surface-lower atmosphere energy flux perspective, to force the development of the UHI. Moreover, the airborne TIR sensor we used in our UHI studies was a multispectral sensor that had six channels in the 8-12pm range. The advantages of collecting multispectral TIR data became readily evident as a valuable tool for better calculation of unique surface thermal energy responses for urban materials over the 8-12 micrometer region, and also for getting a better handle on surface emissivity characteristics for these discrete surfaces. In this presentation, we will provide evidence on the virtues of how high spatial resolution multispectral TIR data can provide for better analysis of the UHI that cannot now be attained via TIR data obtained from satellites. Furthermore, we wish to provide compelling evidence on why future TIR satellite sensors should collect data at fine spatial resolutions (e.g. less than or equal to 30m) to better allow for measurement of surface thermal energy fluxes from discrete urban surfaces, and to better understand how surface fluxes from different urban materials in cities around the world in different climatic regimes, affect development of the UHI characteristics.

Quattrochi, Dale A.↗

Mineralogic variability of the Kelso Dunes, Mojave Desert, California derived from Thermal Infrared Multispectral Scanner (TIMS) data

Mineral identification and mapping of alluvial material using thermal infrared (TIR) remote sensing is extremely useful for tracking sediment transport, assessing the degree of weathering and locating sediment sources. As a result of the linear relation between a mineral's percentage in a given area (image pixel) and the depth of its diagnostic spectral features, TIR spectra can be deconvolved in order to ascertain mineralogic percentages. Typical complications such as vegetation, particle size and thermal shadowing are minimized upon examination of dunes. Actively saltating dunes contain little to no vegetation, are very well sorted and lack the thermal shadows that arise from rocky terrain. The primary focus of this work was to use the Kelso Dunes as a test location for an accuracy analysis of temperature/emissivity separation and linear unmixing algorithms. Accurate determination of ground temperature and component discrimination will become key products of future ASTER data. A decorrelation stretch of the TIMS image showed clear color variations within the active dunes. Samples collected from these color units were analyzed for mineralogy, grain size, and separated into endmembers. This analysis not only revealed that the dunes contained significant mineralogic variation, but were more immature (low quartz percentage) than previously reported. Unmixing of the TIMS data using the primary mineral endmembers produced unique variations within the dunes and may indicate near, rather than far, source locales for the dunes. The Kelso Dunes lie in the eastern Mojave Desert, California, approximately 95 km west of the Colorado River. The primary dune field is contained within a topographic basin bounded by the Providence, Granite Mountains, with the active region marked by three northeast trending linear ridges. Although active, the dunes appear to lie at an opposing regional wind boundary which produces little net movement of the crests. Previous studies have estimated the dunes range from 70% to 90% quartz mainly derived from a source 40 km to the west. The dune field is assumed to have formed in a much more arid climate than present, with the age of the deposit estimated at greater than 100,000 years.

Ramsey, Michael S.↗

The Critical Need for Continued Data Collection and Development of Future Thermal Satellite Sensors

There is a rich and long history of thermal infrared (TIR) remote sensing data for multidisciplinary Earth science research. The continuity of TIR data collection, however, is now in jeopardy given there are no planned future Earth observing TIR remote sensing satellite systems with moderately high spatial resolutions to replace those currently in orbit on NASA's Terra suite of sensors. This session will convene researchers who have actively worked in the field of TIR remote sensing to present results that elucidate the importance of thermal remote sensing to the wider Earth science research community. Additionally, this session will also exist as a forum for presenting concepts and ideas for new thermal sensing systems with high spatial resolutions for future Earth science satellite missions, as opposed to planned systems such as the Visible/Infrared Imager/Radiometer (VIIRS) suite of sensors on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) that will collect TIR data at very coarse spatial resolutions.

Quattrochi, Dale A.↗

Thermal infrared (2.5-13.5-micron) spectroscopic remote sensing of igneous rock types on particulate planetary surfaces

The compositional significance of outstanding spectral features in the reflectance spectra of particulate igneous rocks was investigated by analyzing directional reflectance spectra, obtained with an FTIR spectrophotometer at 2.17-13.5 microns, of a suite of well-characterized igneous rocks ground to fine particle size. It was found that, using spectral features associated with the principal Christiansen frequency and with a region of relative transparency between the Si-O stretching and bending bands, it is possible to identify general rock type using the Walter and Salisbury (1989) SCFM chemical index, SCFM, defined as SCFM = SiO2/(SiO2 + CaO + FeO + MgO). It is emphasized, however, that the appearance and the wavelength of these features may be affected by environmental factors.

Salisbury, John W.↗

ASTER: The Spaceborne TIMS

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multispectral thermal infrared remote sensing high↗