Interpreting the Shortwave Infrared &amp; Thermal Infrared Regions of Remote Sensed Electromagnetic Spectrum with Application for Mineral-Deposits Exploration

Abstract Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.

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Shortwave-infrared (SWIR) emitters for biological imaging: a review of challenges and opportunities

Nanophotonics ◽

10.1515/nanoph-2017-0039 ◽

2017 ◽

Vol 6 (5) ◽

pp. 1043-1054 ◽

Cited By ~ 52

Author(s):

Elijah Thimsen ◽

Bryce Sadtler ◽

Mikhail Y. Berezin

Keyword(s):

Quantum Efficiency ◽

Near Infrared ◽

Materials Science ◽

Energy Gap ◽

Tissue Imaging ◽

Quantum Yields ◽

Electromagnetic Spectrum ◽

Surrounding Matrix ◽

Low Efficiency ◽

Shortwave Infrared

AbstractShortwave infrared radiation (SWIR) is the portion of the electromagnetic spectrum from approximately 900 nm to 2500 nm. Recent advances in imaging systems have expanded the application of SWIR emitters from traditional fields in materials science to biomedical imaging, and the new detectors in SWIR opened an opportunity of deep tissue imaging. Achieving deep photon penetration while maintaining high resolution is one of the main objectives and challenges in bioimaging used for the investigation of diverse processes in living organisms. The application of SWIR emitters in biological settings is, however, hampered by low quantum efficiency. So far, photoluminescent properties in the SWIR region have not been improved by extending concepts that have been developed for the visible (400–650 nm) and near-infrared (NIR, 700–900 nm) wavelengths, which indicates that the governing behavior is fundamentally different in the SWIR. The focus of this minireview is to examine the mechanisms behind the low efficiency of SWIR emitters as well as to highlight the progress in their design for biological applications. Several common mechanisms will be considered in this review: (a) the effect of the energy gap between the excited and ground state on the quantum efficiency, (b) the coupling of the excited electronic states in SWIR emitters to vibrational states in the surrounding matrix, and (c) the role of environment in quenching the excited states. General strategies to improve the quantum yields for a diverse type of SWIR emitters will be also presented.

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Mapping methane point emissions with imaging spectroscopy satellite missions

10.5194/egusphere-egu21-14811 ◽

2021 ◽

Author(s):

Luis Guanter ◽

Itziar Irakulis-Loitxate ◽

Elena Sánchez-García ◽

Javier Gorroño ◽

Yongguang Zhang ◽

...

Keyword(s):

Imaging Spectroscopy ◽

Electromagnetic Spectrum ◽

Spectral Window ◽

Remote Sensing Technique ◽

The Earth ◽

Spatial Coverage ◽

Airborne Imaging ◽

Emission Detection ◽

Shortwave Infrared ◽

Spectral Channels

Imaging spectroscopy, also known as hyperspectral imaging, is a remote sensing technique in which images of the solar radiation reflected by the Earth are produced in hundreds of spectral channels between the visible and the shortwave infrared part of the electromagnetic spectrum (roughly 400&#8211;2500 nm). The 2100-2450 nm spectral window can be used for methane retrievals, as it has been demonstrated over the last years with airborne imaging&#160; spectrometers, and very recently also with space-based instruments. Satellite-based hyperspectral images are acquired with a typical spatial sampling for satellite data of 30 m, a spatial coverage between 30x30 and 60x60 km per scene, and a spectral sampling of 10 nm. In this work, we will present an overview of the state-of-the-art of methane mapping with imaging spectroscopy missions. We will review the characteristics of the available missions, the main retrieval approaches, and will present examples of methane emission detection from a number of missions and locations around the Earth.&#160;

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Estimating clear-sky all-wave net radiation from combined visible and shortwave infrared (VSWIR) and thermal infrared (TIR) remote sensing data

Remote Sensing of Environment ◽

10.1016/j.rse.2015.03.022 ◽

2015 ◽

Vol 167 ◽

pp. 31-39 ◽

Cited By ~ 22

Author(s):

Dongdong Wang ◽

Shunlin Liang ◽

Tao He ◽

Qinqing Shi

Keyword(s):

Remote Sensing ◽

Remote Sensing Data ◽

Thermal Infrared ◽

Net Radiation ◽

Sensing Data ◽

Clear Sky ◽

Shortwave Infrared

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Remote sensing—A geophysical perspective

Geophysics ◽

10.1190/1.1441885 ◽

1985 ◽

Vol 50 (12) ◽

pp. 2595-2610 ◽

Cited By ~ 7

Author(s):

Ken Watson

Keyword(s):

Remote Sensing ◽

Hydrothermal Alteration ◽

Physical Property ◽

Digital Processing ◽

Thermal Infrared ◽

Electromagnetic Spectrum ◽

Multispectral Data ◽

Spectral Bands ◽

Bearing Materials ◽

Wavelength Radiation

In this review of developments in the field of remote sensing from a geophysical perspective, the subject is limited to the electromagnetic spectrum from 0.4 μm to 25 cm. Three broad energy categories are covered: solar reflected, thermal infrared, and microwave. The reflected solar region has been the most intensely studied. Photointerpretation of images from individual spectral bands or from color composites remains the most widely used method of analysis. New instrumentation and digital processing, based on analysis of laboratory and field spectra, provide significant advances that are beginning to be applied to resource exploration. Color compositing techniques have been effectively used to detect the characteristic spectral reflectance features of iron oxides and hydroxyl‐bearing materials in satellite multispectral data for mapping areas of hydrothermal alteration. Airborne spectrometers can now detect individual spectral features of many minerals which are diagnostic of different stages of hydrothermal alteration. Evolution was from discrimination, based on empirical experience, to mineralogic identification and leading to quantification. Current developments also indicate new, promising extensions to vegetated terrains. Advances in thermal infrared studies are due to development of thermal models that permit mapping of physical property variations and to detection of spectral differences that provide important compositional information. Analysis techniques are still in their infancy, and thermal satellite data remain appropriate only for regional investigations. Microwave data have been acquired mainly with radar systems, which can provide very high resolution from space, but use of textural and slope information has had limited application. Long‐wavelength radiation has been shown to penetrate dry materials, and this may be applicable in extremely arid regions.

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Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13163117 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3117

Author(s):

Huize Liu ◽

Ke Wu ◽

Honggen Xu ◽

Ying Xu

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Near Infrared ◽

Three Dimensional ◽

Thermal Infrared ◽

Hyperspectral Data ◽

Gansu Province ◽

Electromagnetic Spectrum ◽

Lithological Mapping ◽

Classification Feature

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

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Influence of the daylight illumination a weather conditions on Airborne Thermal Infrared Hyperspectral geological mapping

10.5194/egusphere-egu21-7494 ◽

2021 ◽

Author(s):

Stephane Boubanga Tombet ◽

Jean-Philippe Gagnon ◽

Holger Eichstaedt ◽

Joanne Ho

Keyword(s):

Near Infrared ◽

Thermal Infrared ◽

Hyperspectral Data ◽

Geological Mapping ◽

Electromagnetic Spectrum ◽

Spectral Features ◽

Absorption Bands ◽

Mineral Mapping ◽

Atmospheric Compensation ◽

Spectral Ranges

The use of airborne remote sensing techniques for geological mapping offers many benefits as it allows coverage of large areas in a very efficient way. &#160;While hyperspectral imaging from airborne/spaceborne platforms is now a well-established method applied to resolve many geological problems, it has mostly been developed only in the Visible-Near Infrared (VNIR, 0.4&#8211;1.0 mm) and Shortwave Infrared (SWIR, 1.0&#8211;2.5 mm) regions of the electromagnetic spectrum. However, the reflectance spectral features measured in the VNIR and SWIR spectral ranges are generally overtones and combination bands from fundamental absorption bands at longer wavelengths, such as in the Longwave Infrared (LWIR, 8&#8211;12 mm). The single absorption bands in the VNIR and SWIR spectral ranges are often very closely spaced so that the reflectance features measured by common spectrometers in this spectral region are typically broad and/or suffer from strong overlapping, which raises selectivity issues for mineral identification in some cases.The inherent self-emission associated with LWIR under ambient conditions allows airborne mineral mapping in various weather (cloudy, partly cloudy or clear sky) and illumination (day or night) conditions. For this reason, LWIR often refers to the thermal infrared (TIR) spectral range. Solid targets such as minerals not only emit but also reflect TIR radiation. Since the two phenomena occur simultaneously, they end-up mixed in the radiance measured at the sensor level. The spectral features observed in a TIR spectrum of the sky and the atmosphere mostly correspond to ozone, water&#160; vapor, carbon dioxide, methane and nitrous oxide with pretty sharp and narrow features compared with the infrared signature of solid materials such as minerals. The sharp spectral features of atmospheric gases are mixed up with broad minerals features in the collected geological mapping data, to unveil the spectral features associated with minerals from TIR measurements, the respective contributions of self-emission and reflection in the measurement must be &#171;unmixed&#187; and the atmospheric contributions must be compensated. This procedure refers to temperature-emissivity separation (TES). Therefore, to achieve an efficient TES and atmospheric compensation, the collection time and conditions of LWIR airborne hyperspectral data is of importance. Data of a flight mission in Southern Spain collected systematically at different times of the day (morning, mid-day and night) and in different altitudes using the Telops Hyper-Cam airborne system, a passive TIR hyperspectral sensor based on Fourier transform spectroscopy, were analyzed. TES was carried out on the hyperspectral data using two different approaches: a) Telops Reveal FLAASH IR software and b) DIMAP In-scene atmospheric compensation algorithm in order to retrieve thermodynamic temperature map and spectral emissivity data. Spectral analysis of the emissivity data with different mineral mapping methods based on commercial spectral libraries was used to compare results obtained during the different flight times and altitudes using the two post-processing methodologies. The results are discussed in the light of optimizing LWIR-based airborne operations in time and altitude to achieve best results for routine field mineral mapping applications such as in mining, soil science or archaeology, where the spatial analysis of mineral and chemical distribution is essential

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Observing Water Vapour in the Planetary Boundary Layer from the Short-Wave Infrared

Remote Sensing ◽

10.3390/rs10091469 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1469 ◽

Cited By ~ 3

Author(s):

Tim Trent ◽

Hartmut Boesch ◽

Peter Somkuti ◽

Noëlle Scott

Keyword(s):

Boundary Layer ◽

Water Vapour ◽

Planetary Boundary Layer ◽

Short Wave ◽

Electromagnetic Spectrum ◽

Climate Data ◽

Data Set ◽

Near Surface ◽

Land Surfaces ◽

Shortwave Infrared

Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global observations of water vapour fields are made from numerous instruments measuring in the ultraviolet/visible, through the infrared bands, to the microwave regions of the electromagnetic spectrum. While these observations provide a wealth of information on columnar, free-tropospheric and upper troposphere/lower stratosphere water vapour amounts, there is still an observational gap regarding resolved bulk planetary boundary layer (PBL) concentrations. In this study we demonstrate the ability of the Greenhouse Gases Observing SATellite (GOSAT) to bridge this gap from highly resolved measurements in the shortwave infrared (SWIR). These new measurements of near surface columnar water vapour are free of topographic artefacts and are interpreted as a proxy for bulk PBL water vapour. Validation (over land surfaces only) of this new data set against global radiosondes show low biases that vary seasonally between −2% to 5%. Analysis on broad latitudinal bands show biases between −3% and 2% moving from high latitudes to the equatorial regions. Finally, with the extension of the GOSAT program out to at least 2027, we discuss the potential for a new GOSAT PBL water vapour Climate Data Record (CDR).

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Radiometric Degradation Curves for the ASTER VNIR Processing Using Vicarious and Lunar Calibrations

Remote Sensing ◽

10.3390/rs12030427 ◽

2020 ◽

Vol 12 (3) ◽

pp. 427 ◽

Cited By ~ 2

Author(s):

Satoshi Tsuchida ◽

Hirokazu Yamamoto ◽

Toru Kouyama ◽

Kenta Obata ◽

Fumihiro Sakuma ◽

...

Keyword(s):

Near Infrared ◽

Thermal Emission ◽

Thermal Infrared ◽

Vicarious Calibration ◽

The Cross ◽

Sensor Degradation ◽

Cross Calibration ◽

Shortwave Infrared ◽

Infrared Radiometer

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra platform, which was launched in 1999, has three separate subsystems: a visible and near-infrared (VNIR) radiometer, a shortwave-infrared radiometer, and a thermal-infrared radiometer. The ASTER VNIR bands have been radiometrically corrected for approximately 14 years by the sensor degradation curves estimated from the onboard calibrator according to the original calibration plan. However, this calibration by the onboard calibrator encountered a problem; specifically, it is inconsistent with the results of vicarious calibration and cross calibration. Therefore, the ASTER VNIR processing was applied by the radiometric degradation curves calculated from the results of three calibration approaches, i.e., the onboard calibrator, the vicarious calibration, and the cross calibration since February 2014. Even though the current degradation curves were revised, the inter-band and lunar calibrations show some inconsistencies owing to the different traceability in the bands by different calibration approaches. In this study, the current degradation curves and their problems are explained, and the new curves that are derived from the vicarious calibration with lunar calibration are discussed. The new degradation curves that have the same traceability in the bands will be used for future ASTER VNIR processing.

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OCEAN COLOR RETRIEVAL USING LANDSAT-8 IMAGERY IN COASTAL CASE 2 WATERS (CASE STUDY PERSIAN AND OMAN GULF)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-1161-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1161-1164

Author(s):

N. Moradi ◽

M. Hasanlou ◽

M. Saadatseresht

Keyword(s):

Water Quality ◽

Persian Gulf ◽

Ocean Color ◽

Thermal Infrared ◽

Thematic Mapper ◽

Landsat 8 ◽

Electromagnetic Spectrum ◽

Infrared Sensor ◽

Oman Sea ◽

The Persian Gulf

Ocean color (OC) monitoring using satellite imageries provides an appropriate tool for a better understanding of marine processes and changes in the coastal environment. Radiance measurements in the range of visible light of the electromagnetic spectrum provides information of ocean color that is associated with the water constituents. This measurements are used to monitor the level of biological activity and the presence of particles in the water. Ocean features such as the concentration of chlorophyll, suspended sediment concentration and sea surface temperature have a significant impact on the dynamics of the ocean. The concentration of chlorophyll (chla), active pigments of phytoplankton photosynthesis, as a key indicator applied for assessment of water quality and biochemistry. Experimental algorithms chla related to internal communication various optical components in the water that may be change in space and time in the water with different optical characteristics. Therefore, the algorithms have been developed for one area may not work for other places and each region according to its specific characteristics needs that determined by an algorithm may be appropriate to local. We have tried treatment several algorithms for determination of chlorophyll, including experimental algorithms with a simple band ratio of blue-green band (i.e. OCx) and algorithms includes two bands ratio with variable 𝑅𝑟𝑠(λ2)/𝑅𝑟𝑠(λ1), the three bands ratio with variable [𝑅𝑟𝑠(λ1)−1−𝑅𝑟𝑠(λ2)−1]×𝑅𝑟𝑠(λ3) and four bands ratio with variable [𝑅𝑟𝑠(λ1)−1−𝑅𝑟𝑠(λ2)−1]/[𝑅𝑟𝑠(λ4)−1−𝑅𝑟𝑠(λ3)−1] that desired wavelength (i.e. λ1, λ2, λ3 and λ4) in the range of red and near-infrared wavelengths of the electromagnetic spectrum are in the region of the Persian Gulf and Oman Sea look. Despite the high importance of the Persian Gulf and Oman Sea which can have up basin countries, to now few studies have been done in this area. The focus of this article on the northern part of Oman Sea and Persian Gulf, the shores of neighboring Iran (case 2 water). In this paper, by using Landsat 8 satellite imageries, we have discussed chla concentrations and customizing different OC algorithms for this new dataset (Landsat-8 imagery). This satellite was launched in 2013 and its data using two sensors continuously are provided operating one sensor imager land (OLI: Operational Land Imager) and the Thermal Infrared Sensor (TIRS: Thermal InfraRed Sensor) and are available. This sensors collect image data, respectively, for the nine-band short wavelength in the range of 433-2300 nm and dual-band long wavelength thermal. Seven band of the nine band picked up by the sensor information of OLI to deal with sensors TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) in previous satellite Landsat compatible and two other band, the band of coastal water (433 to 453 nm) and Cirrus band (1360 to 1390 nm), short wave infrared provides to measure water quality and high thin clouds. Since OLI sensor in Landsat satellite 8 compared with other sensors to study OC have been allocated a much better spatial resolution can be more accurate to determine changes in OC. To evaluate the results of the image sensor MODIS (Moderate Resolution Imaging Spectroradiometer) at the same time satellite images Landsat 8 is used. The statistical parameters used in order to evaluate the performance of different algorithms, including root mean square error (RMSE) and coefficient of determination (R2), and on the basis of these parameters we choose the most appropriate algorithm for the area. Extracted results for implementing different OC algorithms clearly shows superiority of utilized method by R2=0.71 and RMSE=0.07.

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