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In-Flight Validation of Mid and Thermal Infrared Remotely Sensed Data
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Effect of metal stress on the thermal infrared emission of soybeans: A greenhouse experiment - Possible utility in remote sensing
Manganese-sensitive forest and manganese-tolerant lee soybean cultivars were subjected to differential manganese stress in loring soil in a greenhouse experiment. Leaf temperature measurements were made using thermistors for forest and lee. Manganese-stressed plants had higher leaf temperatures than control plants in both forest and lee. Results of this experiment have potential applications in metal stress detection using remote sensing thermal infrared data over large areas of vegetation. This technique can be useful in reconnaissance mineral exploration in densely-vegetated regions where conventional ground-based methods are of little help.
Influence of spatial variability of hydraulic characteristics of soils on surface parameters obtained from remote sensing data in infrared and microwaves
The correct interpretation of thermal and hydraulic soil parameters infrared from remotely sensed data (thermal infrared, microwaves) implies a good understanding of the causes of their temporal and spatial variability. Given this necessity, the sensitivity of the surface variables (temperature, moisture) to the spatial variability of hydraulic soil properties is tested with a numerical model of heat and mass transfer between bare soil and atmosphere. The spatial variability of hydraulic soil properties is taken into account in terms of the scaling factor. For a given soil, the knowledge of its frequency distribution allows a stochastic use of the model. The results are treated statistically, and the part of the variability of soil surface parameters due to that of soil hydraulic properties is evaluated quantitatively.
A comparison of the thermal and radar characteristics of Mars
Results of thermal infrared sensing and radar observations of the Martian surface are compared for the region centered on +22 deg latitude. Values of the apparent thermal inertia of the surface were derived from surface brightnesses observed by the Viking Orbiter Infrared Thermal Mapper, while radar cross sections were obtained in earth-based experiments at wavelengths of 3.8, 12.5 and 70 cm. A correlation between the thermal inertia and radar cross section values is observed which is strongest with the 70-cm radar data except between longitudes of 10 and 90 deg, where a slight anticorrelation is found. The mixing of small (much less than 70 cm) rocks into a surface of fine material can account for the data between 90 and 370 deg longitude, with as much as 50% of the surface covered by rock in Syrtis Major, Isidis Planitia and parts of Elysium Planitia, and little or no rock cover near Olympus Mons, Elysium Mons, and Amazonis Planitia. The remaining longitudes may be explained in terms of the effects of atmospheric dust on the surface temperature or the effects of local variations in large-scale roughness or scattering from rocks. Data from the 90 to 370 deg longitude region are consistent with the division of the Martian surface into two types of terrain, possibly related to the erosional or depositional nature of the regions.
Thermal infrared emission spectroscopy of natural surfaces: Application to desert varnish coatings on rocks
Thermal infrared spectroscopy has become an increasingly important tool for remote compositional analysis and geologic mapping. Most published laboratory measurements have been obtained in bidirectional reflection or transmission, whereas remotely sensed thermal infrared data are obtained by measuring the emitted energy. Section 2 of this paper describes a laboratory technique for determining calibrated emissivities of natural surfaces. Equations are developed to account for the energy reflected from the environment and to determine directly the sample temperature from measurements of hot and cold blackbody targets. Two methods for determining emissivity are developed: one in which only a hot sample measurement is made and the reflected background energy is removed by modeling, and a second in which the sample is cooled and the reflected energy is measured directly. Relative emissivity can be obtained to approximately 1% and absolute emissivities can be obtained to 2-15%, depending on the validity of the assumption that the emissivity of the sample is unity at some wavelength. The emission data agree well within the hemispherically integrated reflection data but point out probelms associated with bidirectional reflectance measurements. Section 3 applies emissivity measurements to the study of layered surfaces consisting of desert varnish coatings on granite and granodiorite rock suites. Two linear models are developed: the first assumes linear mixing of independent emission from the substrate and varnish (checkerboard model); the second models tansmission through an absorbing/emitting medium. Regardless of whether the varnish is or is not relatively transparant and strongly absorptive, the spectral effect of varnish increases linearly with varnish thickness, indicating that thick patches of varnish dominate the spectral properties. As a result, medium varnish thickness can be determined from spectral measurements. In addition, the composition of a substrate can be estimated through varnish layers up to 40-50 micrometers in median thickness, and the composition of the varnish material can be determined if the substrate material is known. The varnishes studied are composed primarily of clay materials, consistent with previous studies of varnish composition.
Remote sensing for defining aquifers in glacial drift
Investigations were undertaken to determine the properties of shallow aquifers and related features that influence electromagnetic energy, to determine how these properties can be detected remotely, and to establish remote sensing procedures for aiding in ground water mapping. The direct influence of aquifier characteristics on surface thermal contrasts is discussed. Conclusions reached for late summer, predawn missions were: (1) Dynamic thermal changes near the surface can be used for thermal infrared sensing to detect shallow aquifers in glacial drift. (2) Under ideal conditions, surface temperatures may be used to predict certain features related to the occurrence of shallow aquifers. (3) The timing of missions and the optimum meteorological conditions occurring prior to and during the mission are critical for night thermal missions. (4) Repetitive flights made under variable conditions provide additional evidence to verify the occurrence of shallow aquifers. (5) Imagery from this investigation, along with ERTS-simulated imagery, indicates the feasibility of applying ERTS A and B data to reconnaissance studies for detection of shallow aquifers.
The basis for the spectral behaviour of silicates in the thermal infrared and applications to remote sensing
Variations in the thermal infrared (TIR) spectral response of silicate rocks is related to changes in the structures and divalent cation contents of the minerals which form the rocks. These considerations lead to a chemical parameter, SCFM, which reflects mineral structures, rock types, and their spectra. The parameter is the ratio of silica to the abundance of depolymerizing cations, defined as SCFM = SiO2/SiO2 + CaO + MgO + FeO. Parameter SCFM is therefore proposed for use in TIR remote sensing of igneous rocks. It is also demonstrated that two or three broad bands are sufficient for distinguishing among major rock types and the system noise has little effect on the quality of the results. These factors can be traded off against improved spatial resolution in instrument design.
The use of satellites in environmental monitoring of coastal waters
The feasibility of using satellites in an operational system for monitoring the type, concentration, location, drift, and dispersion of pollutants in coastal waters is evaluated. Visible, microwave, and thermal infrared sensing are considered. Targets to be detected include photosynthetic pigments, iron acid waste, and sewage sludge.
Appendix C. LANDSAT: A worldwide perspective
Images characteristics of geographic regions other than the northeastern part of the United States are presented for interpretation. Pre- and post-eruption imagery of Mt. St. Helens volcano serves to demonstrate the advantages of thermal infrared sensing, and the potential for developing a timely, decision oriented thematic map to be used in solving drought-related problems in Upper Volta is examined to show the applicability of satellite remote sensing in all geographic areas.
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.
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.
Cirrus Parameter Retrieval by Combined Analysis of Spectral Thermal Infrared and Lidar Sensing from Aircraft and Space
The distribution, radiative and microphysical parameters of cirrus clouds are an important factor for cloud effects on the global radiation balance and climate. Multispectral thermal infrared observation are the most significant method used to remote sense cirrus parameter from current and planned passive satellite observations. Passive sensing alone has the limitation that both the cloud radiative temperature, or height, and the spectral emissivity must be derived in the analysis. Factors such as multiple cloud laying, which is very common, are known to introduce ambiguities in results. The addition of lidar cloud height structure measurements significantly improves retrievals. Such active/passive observations have been applied from the NASA ER-2 high altitude remote sensing aircraft since 1983. Applications include the study of the effective effective particle size of cirrus and application toward remote sensing of the ice/water content of cirrus clouds in addition to radiative parameters. Limitations, accuracy and examples of retrievals are presented. In 1997 a space shuttle hitch hiker experiment was flown which included a new technology Infrared Spectral Imaging Radiometer and a laser altimeter for direct cloud height measurements. A 30 orbit data set to test global application of combined spectral infrared and laser height measurements were obtained. Initial results for cirrus analysis from the shuttle experiment will also be presented.
Mapping Weathering and Alteration Minerals in the Comstock and Geiger Grade Areas using Visible to Thermal Infrared Airborne Remote Sensing Data
To support research into both precious metal exploration and environmental site characterization a combination of high spatial/spectral resolution airborne visible, near infrared, short wave infrared (VNIR/SWIR) and thermal infrared (TIR) image data were acquired to remotely map hydrothermal alteration minerals around the Geiger Grade and Comstock alteration regions, and map the mineral by-products of weathered mine dumps in Virginia City. Remote sensing data from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), SpecTIR Corporation's airborne hyperspectral imager (HyperSpecTIR), the MODIS-ASTER airborne simulator (MASTER), and the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) were acquired and processed into mineral maps based on the unique spectral signatures of image pixels. VNIR/SWIR and TIR field spectrometer data were collected for both calibration and validation of the remote data sets, and field sampling, laboratory spectral analyses and XRD analyses were made to corroborate the surface mineralogy identified by spectroscopy. The resulting mineral maps show the spatial distribution of several important alteration minerals around each study area including alunite, quartz, pyrophyllite, kaolinite, montmorillonite/muscovite, and chlorite. In the Comstock region the mineral maps show acid-sulfate alteration, widespread propylitic alteration and extensive faulting that offsets the acid-sulfate areas, in contrast to the larger, dominantly acid-sulfate alteration exposed along Geiger Grade. Also, different mineral zones within the intense acid-sulfate areas were mapped. In the Virginia City historic mining district the important weathering minerals mapped include hematite, goethite, jarosite and hydrous sulfate minerals (hexahydrite, alunogen and gypsum) located on mine dumps. Sulfate minerals indicate acidic water forming in the mine dump environment. While there is not an immediate threat to the community, there are clearly sources of acidic drainage that were identified remotely.
Fourier domain target transformation analysis in the thermal infrared
Remote sensing uses of principal component analysis (PCA) of multispectral images include band selection and optimal color selection for display of information content. PCA has also been used for quantitative determination of mineral types and abundances given end member spectra. The preliminary results of the investigation of target transformation PCA (TTPCA) in the fourier domain to both identify end member spectra in an unknown spectrum, and to then calculate the relative concentrations of these selected end members are presented. Identification of endmember spectra in an unknown sample has previously been performed through bandmatching, expert systems, and binary classifiers. Both bandmatching and expert system techniques require the analyst to select bands or combinations of bands unique to each endmember. Thermal infrared mineral spectra have broad spectral features which vary subtly with composition. This makes identification of unique features difficult. Alternatively, whole spectra can be used in the classification process, in which case there is not need for an expert to identify unique spectra. Use of binary classifiers on whole spectra to identify endmember components has met with some success. These techniques can be used, along with a least squares fit approach on the endmembers identified, to derive compositional information. An alternative to the approach outlined above usese target transformation in conjunction with PCA to both identify and quantify the composition of unknown spectra. Preprocessing of the library and unknown spectra into the fourier domain, and using only a specific number of the components, allows for significant data volume reduction while maintaining a linear relationship in a Beer's Law sense. The approach taken here is to iteratively calculate concentrations, reducing the number of endmember components until only non-negative concentrations remain.
EcoSpec: Highly Equipped Tower-Based Hyperspectral and Thermal Infrared Automatic Remote Sensing System for Investigating Plant Responses to Environmental Changes
Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.
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.
Discrimination of soil hydraulic properties by combined thermal infrared and microwave remote sensing
Using the De Vries models for thermal conductivity and heat capacity, thermal inertia was determined as a function of soil moisture for 12 classes of soil types ranging from sand to clay. A coupled heat and moisture balance model was used to describe the thermal behavior of the top soil, while microwave remote sensing was used to estimate the soil moisture content of the same top soil. Soil hydraulic parameters are found to be very highly correlated with the combination of soil moisture content and thermal inertia at the same moisture content. Therefore, a remotely sensed estimate of the thermal behavior of the soil from diurnal soil temperature observations and an independent remotely sensed estimate of soil moisture content gives the possibility of estimating soil hydraulic properties by remote sensing.