Thermal Infrared Radiance Research Articles

An analytical four-component directional brightness temperature model for crop and forest canopies

Measurements of surface thermal infrared (TIR) radiance that are made to extract temperatures display strong directional anisotropy effects. Directional brightness temperature (BT) models that describe this anisotropic behavior of TIR emissions can be applied to separate component temperatures using multi-angle observations. The surface temperature differences that occur between sunlit and shaded areas and the leaf clumping phenomenon jointly affect the directional signatures of out-of-canopy directional BTs. However, these factors are not fully considered in existing directional BT models. This paper therefore extends the FR97 analytical model to 1) a four-component scene containing sunlit and shaded soil and leaves by incorporating the effective emissivity values of the sunlit and shaded parts and 2) row-planted crop and forest canopies by introducing a leaf clumping index. The proposed model was assessed using a synthetic dataset that was generated by the Thermal Radiosity-Graphics Combined Model (TRGM) under various conditions. The evaluation results indicated that the proposed model performed as well as the Scattering by Arbitrarily Inclined Leaves (4SAIL) model over continuous canopies with root mean squared errors (RMSEs) lower than 0.3 °C. Over non-continuous crops and forests, the behavior of the proposed model displayed improved agreement with the TRGM with RMSEs lower than 0.65 °C. The proposed model also displayed a robust performance over both the maize and pine canopies, which was evaluated against the directional anisotropy of measured datasets that were collected at the Huailai remote sensing test site and the Institut National de la Recherche Agronomique (INRA), respectively. From these points, the proposed model has potential for component temperature inversion and rapid assessment of TIR angular effects.

Global Land Surface Temperature From the Along‐Track Scanning Radiometers

AbstractThe Leicester Along‐Track Scanning Radiometer (ATSR) and Sea and Land Surface Temperature Radiometer (SLSTR) Processor for LAnd Surface Temperature (LASPLAST) provides global land surface temperature (LST) products from thermal infrared radiance data. In this paper, the state‐of‐the‐art version of LASPLAST, as deployed in the GlobTemperature project, is described and applied to data from the Advanced Along‐Track Scanning Radiometer (AATSR). The LASPLAST retrieval formulation for LST is a nadir‐only, two‐channel, split‐window algorithm, based on biome classification, fractional vegetation, and across‐track water vapor dependences. It incorporates globally robust retrieval coefficients derived using highly sampled atmosphere profiles. LASPLAST benefits from appropriate spatial resolution auxiliary information and a new probabilistic‐based cloud flagging algorithm. For the first time for a satellite‐derived LST product, pixel‐level uncertainties characterized in terms of random, locally correlated, and systematic components are provided. The new GlobTemperature GT_ATS_2P Version 1.0 product has been validated for 1 year of AATSR data (2009) against in situ measurements acquired from “gold standard reference” stations: Gobabeb, Namibia, and Evora, Portugal; seven Surface Radiation Budget stations, and the Atmospheric Radiation Measurement station at Southern Great Plains. These data show average absolute biases for the GT_ATS_2P Version 1.0 product of 1.00 K in the daytime and 1.08 K in the nighttime. The improvements in data provenance including better accuracy, fully traceable retrieval coefficients, quantified uncertainty, and more detailed information in the new harmonized format of the GT_ATS_2P product will allow for more significant exploitation of the historical LST data record from the ATSRs and a valuable near‐real‐time service from the Sea and Land Surface Temperature Radiometers (SLSTRs).

Predicting Top-of-Atmosphere Thermal Radiance Using MERRA-2 Atmospheric Data with Deep Learning

Image data from space-borne thermal infrared (IR) sensors are used for a variety of applications, however they are often limited by their temporal resolution (i.e., repeat coverage). To potentially increase the temporal availability of thermal image data, a study was performed to determine the extent to which thermal image data can be simulated from available atmospheric and surface data. The work conducted here explored the use of Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) developed by The National Aeronautics and Space Administration (NASA) to predict top-of-atmosphere (TOA) thermal IR radiance globally at time scales finer than available satellite data. For this case study, TOA radiance data was derived for band 31 (10.97 μ m) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Two approaches have been followed, namely an atmospheric radiative transfer forward modeling approach and a supervised learning approach. The first approach uses forward modeling to predict TOA radiance from the available surface and atmospheric data. The second approach applied four different supervised learning algorithms to the atmospheric data. The algorithms included a linear least squares regression model, a non-linear support vector regression (SVR) model, a multi-layer perceptron (MLP), and a convolutional neural network (CNN). This research found that the multi-layer perceptron model produced the lowest overall error rates with an root mean square error (RMSE) of 1.36 W/m 2 ·sr· μ m when compared to actual Terra/MODIS band 31 image data. These studies found that for radiances above 6 W/m 2 ·sr· μ m, the forward modeling approach could predict TOA radiance to within 12 percent, and the best supervised learning approach can predict TOA to within 11 percent.

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

Abstract. Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookup table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation (R2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21 µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.

An Analysis of Rational Green Area Ratio by Land Use Types for Mitigating Heat-Island Effects

The purpose of this study is to analyze reasonable green area ratios for mitigating urban heat island considering various land use types. Land uses of 5 types such as single residential, multi residential, commercial area, public facility, and industrial area were considered. Green areas were extracted from the tree attribution of land cover. Effect of urban heat island was analysed by the surface temperature of ASTER thermal infrared radiance scanned daytime and nighttime. Mitigation effect of green area at daytime was higher than nighttime. Surface temperature of green area was low in single residential at daytime. But the difference of surface temperature by each land use type was small. The effect of surface temperature mitigation of green area was lower in industrial area. The results of reasonable green area ratios for mitigating urban heat island indicate that surface temperature was the lowest with green area ratio of 40~50% in single residential, multi residential, and commercial area at daytime. Surface temperature of nighttime was not changed much by green area ratios. Therefore, the results of this study will be suggested in urban development planning to construct effectively green area for mitigating urban heat island.

Spatial and temporal variation in CO over Alberta using measurements from satellites, aircraft, and ground stations

Abstract. Alberta is Canada's largest oil producer, and its oil sands deposits comprise 30% of the world's oil reserves. The process of bitumen extraction and upgrading releases trace gases and aerosols to the atmosphere. In this study we present satellite-based analysis to explore, for the first time, various contributing factors that affect tropospheric carbon monoxide (CO) levels over Alberta. The multispectral product that uses both near-infrared (NIR) and the thermal-infrared (TIR) radiances for CO retrieval from the Measurements of Pollution in the Troposphere (MOPITT) is examined for the 12-year period from 2002 to 2013. The Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly product from 2001 to 2013 is employed to investigate the seasonal and temporal variations in forest fires. Additionally, in situ CO measurements at industrial and urban sites are compared to satellite data. Furthermore, the available MOZAIC/IAGOS (Measurement of Ozone, Water Vapor, Carbon Monoxide, Nitrogen Oxide by Airbus In-Service Aircraft/In service Aircraft for Global Observing System) aircraft CO profiles (April 2009–December 2011) are used to validate MOPITT CO data. The climatological time curtain plot and spatial maps for CO over northern Alberta indicate the signatures of transported CO for two distinct biomass burning seasons: summer and spring. Distinct seasonal patterns of CO at the urban sites (Edmonton and Calgary) point to the strong influence of traffic. Meteorological parameters play an important role in the CO spatial distribution at various pressure levels. Northern Alberta shows a stronger upward lifting motion which leads to larger CO total column values, while the poor dispersion in central and southern Alberta exacerbates the surface CO pollution. Interannual variations in satellite data depict a slightly decreasing trend for both regions, while the decline trend is more evident from ground observations, especially at the urban sites. MOPITT CO vertical averages and MOZAIC/IAGOS aircraft profiles were in good agreement within the standard deviations at all pressure levels. There is consistency between the time evolution of high-CO episodes monitored by satellite and ground measurements and the fire frequency peak time, which implies that biomass burning has affected the tropospheric CO distribution in northern Alberta. These findings have further demonstrated the potential use of the MOPITT V5 multispectral (NIR + TIR) product for assessing a complicated surface process.

Reducing atmospheric noise in RST analysis of TIR satellite radiances for earthquakes prone areas satellite monitoring

Space–time fluctuations of the Earth’s emitted Thermal Infrared (TIR) radiation observed from satellite from months to weeks before an earthquake are reported in several studies. Among the others, a Robust Satellite data analysis Technique (RST) was proposed (and applied to different satellite sensors in various geo-tectonic contexts) to discriminate anomalous signal transients possibly associated with earthquake occurrence from normal TIR signal fluctuations due to other possible causes (e.g. solar diurnal–annual cycle, meteorological conditions, changes in observational conditions, etc.). Variations in satellite view angle depending on satellite’s passages (for polar satellites) and atmospheric water vapour fluctuations were recognized in the past as the main factors affecting the residual signal variability reducing the overall Signal-to-Noise (S/N) ratio and the potential of the RST-based approach in identifying seismically related thermal anomalies. In this paper we focus on both factors for the first time, applying the RST approach to geostationary satellites (which guarantees stable view angles) and using Land Surface Temperature (LST) data products (which are less affected by atmospheric water vapour variability) instead of just TIR radiances at the sensor.The first results, obtained in the case of the Abruzzo earthquake (6 April 2009, MW∼6.3) by analyzing 6years of SEVIRI (Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat Second Generation satellite) LST products provided by EUMETSAT, seem to confirm the major sensitivity of the proposed approach in detecting perturbations of the Earth’s thermal emission a few days before the main shock. The results achieved in terms of increased S/N ratio (in validation) and reduced “false alarms” rate (in confutation) are discussed comparing results obtained by applying RST to LST products with those achieved by applying an identical RST analysis (using the same MSG-SEVIRI 2005–2010 data-set) to the simple TIR radiances at the sensor.

Quartzose–mafic spectral feature space model: A methodology for extracting felsic rocks with ASTER thermal infrared radiance data

The original spectral features of felsic rocks are often intermingled with other surface objects, which results in difficulty of detecting felsic rocks using remote sensing techniques. Few felsic rock indices were proposed and visual interpretation with RGB false color composition is widely used to detect felsic rocks. This paper aims to construct a two-dimensional spectral feature space model to extract felsic rocks using ASTER thermal infrared radiance data. The study area is located in northern Qinghai Province, western China with average altitude of approximately 4200m. A large number of training pixels of mafic–ultramafic rock, quartz-rich rock, felsic rock, carbonate rock and vegetation were selected from the ASTER images as samples of these surface objects. Then we used a quartz-rich rock index (QI, QI=band14−0.844×band12−1.897) and a mafic–ultramafic rock index (MI, MI=0.915×band10−band13+1.437) to generate a two-dimensional scatter plot. The plot was named after quartzose–mafic spectral feature space (QMFS). The samples show an approximate triangular shape in the QMFS. Mafic–ultramafic rock, quartz-rich rock and carbonate rock are located in separate locations in the three vertex regions, respectively, while felsic rock is located in the central region of the triangle. Next, we calculated a linear belt of silicate rocks in which silicate rocks vary regularly by using a linear regression analysis in the QMFS. Statistical characteristics of the felsic rock samples are analyzed. Afterwards, a polygon which delineates the distribution of felsic rock samples was constructed from the linear belt of silicate rocks. Then we generated a system of inequalities based on the equations of the edges of the polygon. The application of the inequalities to the ASER images shows a good performance of the QMFS for extracting felsic rocks.

Mafic–ultramafic and quartz-rich rock indices deduced from ASTER thermal infrared data using a linear approximation to the Planck function

ASTER thermal infrared (TIR) data are widely used to detect mafic–ultramafic rock and quartz-rich rock, and several rock indices have been proposed based on emissivity features. However, ASTER TIR bands of radiance data correlate highly with each other, which indicates that the independent information derived from different bands may be limited, what's more, ASTER TIR radiance-at-sensor data contain atmospheric effect and temperature information, thus interfering with the availability of these previously proposed indices. In this study, we aim to explain the correlation using a linear approximation of the Planck function and deduce a linear equation that represents the relationship of the radiance between two TIR bands. Theoretical difference indices were deduced based on the linear equation and regression residual characteristics for any two ASTER TIR radiance bands. The study area is located in Qinghai Province, China, and belongs to the Qinghai–Tibet Plateau, where the average elevation is approximately 4200m. A scatter plot of radiance derived from the ASTER image that overlaps the study area indicates that mafic–ultramafic rock and quartz-rich rock can be distinguished from other surface objects well. Two mafic–ultramafic rock indices (MI1=b13−0.9147∗b10−1.4366 and MI2=b13−0.8945∗b11−1.2404) and two quartz-rich rock indices (QI1=b13−0.9261∗b12−1.4623 and QI2=b14−0.844∗b12−1.8971) were proposed; they satisfactorily map these rock units. The atmospheric effect on the indices is weak in arid or high-elevation region, so it will not interfere with the indices obviously in these regions. One-way variance analysis was performed to discuss the stability of the indices with respect to temperature. The mafic–ultramafic rock indices are found to be independent of temperature, whereas the values of quartz-rich rock indices increase with the rising of temperature. We thus conclude that the quartz-rich rock indices are suitable for the high-elevation region only, while the mafic–ultramafic rock indices may be capable of detecting these rocks in regions with different natural conditions.

TIR Spectral Radiance Calibration of the GOSAT Satellite Borne TANSO-FTS With the Aircraft-Based S-HIS and the Ground-Based S-AERI at the Railroad Valley Desert Playa

The thermal infrared (TIR) band of Thermal and Near-Infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS) on the Greenhouse gases Observing SATellite (GOSAT) measures a wide range of scene temperatures using a single detector band with broad spectral coverage. This work describes the vicarious radiometric calibration over a large footprint (10.5 km) and high temperature surface using well-calibrated ground-based and airborne FTS sensors. The vicarious calibration campaign of GOSAT was conducted at Railroad Valley, NV in June 2011. During the campaign, the Scanning High-resolution Interferometer Sounder (S-HIS) mounted on the high-altitude NASA ER-2 aircraft observed upwelling radiation and the ground-based Surface-Atmospheric Emitted Radiance Interferometer (S-AERI) observed infrared thermal emission from the atmosphere and the surface at the same location and time as the GOSAT TANSO-FTS. We validated TANSO-FTS TIR radiance with S-HIS radiance using double difference method, which reduces the effect of differences in the observation geometry. In this paper, we estimated the TANSO-FTS Instantaneous Field of View average temperature and emissivity by the coincident S-AERI and S-HIS observed radiance. The double difference between TANSO-FTS and S-HIS result in a difference of 0.5 K at atmospheric window channels (800 ~ 900 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> warm brightness temperature channels (700 ~ 750 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ), 0.1 K at ozone channels (980 ~ 1080 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ), and more than 2 K at CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> cool brightness temperature channels (650 ~ 700 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ). The main reason of remaining errors is attributed to a calibration error in the TANSO-FTS Level 1B product version under evaluation.

Dependence of thermal infrared emissive behaviors of snow cover on the surface snow type

The potential of the thermal infrared (TIR) remote sensing for discriminating surface snow types was examined by analyzing TIR radiances acquired from space over the Greenland ice sheet. The brightness temperature difference (BTD) between TIR wavelengths of 11 and 12μm was found to increase in accordance with in situ observed evolutions of surface snow type. Spatial and temporal distributions of BTD over the entire ice sheet indicated that BTD has a sensitivity of about 1.2 K for variations of the possible snow types. The observed behaviors of BTD were coincident with those predicted by a radiative transfer calculation using previous in situ measured snow emissivities, although some biases on the order of 0.1-0.3 K remain. The dependence of BTD on the surface snow type was also consistent with the behaviors of snow reflectance at the shortwave infrared (SWIR) wavelength 1.6μm, which is a measure of snow grain size, except for the case of melting wet snow. The inconsistency in the wet snow case was considered to be due to the different optical responses of the TIR and SWIR signals to wet snow, which suggested the possibility of using TIR signals to discriminate wet/dry conditions of snow cover in an old stage. As a result, it is determined that TIR remote sensing has potential not only as an approach supplementary to the SWIR method for assessing surface snow types in daytime but also as the only method for simultaneous retrieval of snow type and surface temperature in nighttime.

Satellite observation of lowermost tropospheric ozone by multispectral synergism of IASI thermal infrared and GOME-2 ultraviolet measurements over Europe

Abstract. We present a new multispectral approach for observing lowermost tropospheric ozone from space by synergism of atmospheric radiances in the thermal infrared (TIR) observed by IASI (Infrared Atmospheric Sounding Interferometer) and earth reflectances in the ultraviolet (UV) measured by GOME-2 (Global Ozone Monitoring Experiment-2). Both instruments are onboard the series of MetOp satellites (in orbit since 2006 and expected until 2022) and their scanning capabilities offer global coverage every day, with a relatively fine ground pixel resolution (12 km-diameter pixels spaced by 25 km for IASI at nadir). Our technique uses altitude-dependent Tikhonov–Phillips-type constraints, which optimize sensitivity to lower tropospheric ozone. It integrates the VLIDORT (Vector Linearized Discrete Ordinate Radiative Transfer) and KOPRA (Karlsruhe Optimized and Precise Radiative transfer Algorithm) radiative transfer codes for simulating UV reflectance and TIR radiance, respectively. We have used our method to analyse real observations over Europe during an ozone pollution episode in the summer of 2009. The results show that the multispectral synergism of IASI (TIR) and GOME-2 (UV) enables the observation of the spatial distribution of ozone plumes in the lowermost troposphere (LMT, from the surface up to 3 km a.s.l., above sea level), in good agreement with the CHIMERE regional chemistry-transport model. In this case study, when high ozone concentrations extend vertically above 3 km a.s.l., they are similarly observed over land by both the multispectral and IASI retrievals. On the other hand, ozone plumes located below 3 km a.s.l. are only clearly depicted by the multispectral retrieval (both over land and over ocean). This is achieved by a clear enhancement of sensitivity to ozone in the lowest atmospheric layers. The multispectral sensitivity in the LMT peaks at 2 to 2.5 km a.s.l. over land, while sensitivity for IASI or GOME-2 only peaks at 3 to 4 km a.s.l. at lowest (above the LMT). The degrees of freedom for the multispectral retrieval increase by 0.1 (40% in relative terms) with respect to IASI only retrievals for the LMT. Validations with ozonesondes (over Europe during summer 2009) show that our synergetic approach for combining IASI (TIR) and GOME-2 (UV) measurements retrieves lowermost tropospheric ozone with a mean bias of 1% and a precision of 16%, when smoothing by the retrieval vertical sensitivity (1% mean bias and 21% precision for direct comparisons).

Suomi‐NPP CrIS radiometric calibration uncertainty

AbstractThe Cross‐track Infrared Sounder (CrIS) is the high spectral resolution spectroradiometer on the Suomi National Polar‐Orbiting Partnership (NPP) satellite, providing operational observations of top‐of‐atmosphere thermal infrared radiance spectra for weather and climate applications. This paper describes the CrIS radiometric calibration uncertainty based on prelaunch and on‐orbit efforts to estimate calibration parameter uncertainties, and provides example results of recent postlaunch validation efforts to assess the predicted uncertainty. Prelaunch radiometric uncertainty (RU) estimates computed for the laboratory test environment are less than ~0.2 K 3 sigma for blackbody scene temperatures above 250 K, with primary uncertainty contributions from the calibration blackbody temperature, calibration blackbody reflected radiance terms, and detector nonlinearity. Variability of the prelaunch RU among the longwave band detectors and midwave band detectors is due to different levels of detector nonlinearity. A methodology for on‐orbit adjustment of nonlinearity correction parameters to reduce the overall contribution to RU and to reduce field of view (FOV)‐to‐FOV variability is described. The resulting on‐orbit RU estimates for Earth view spectra are less than 0.2 K 3 sigma in the midwave and shortwave bands, and less than 0.3 K 3 sigma in the longwave band. Postlaunch validation efforts to assess the radiometric calibration of CrIS are underway; validation results to date indicate that the on‐orbit RU estimates are representative. CrIS radiance products are expected to reach “Validated” status in early 2014.

Validation of MOPITT Version 5 thermal‐infrared, near‐infrared, and multispectral carbon monoxide profile retrievals for 2000–2011

AbstractValidation results are reported for the MOPITT (Measurements of Pollution in the Troposphere) “Version 5” (V5) product for tropospheric carbon monoxide (CO) and are compared to results for the “Version 4” product. The V5 retrieval algorithm introduces (1) a method for reducing retrieval bias drift associated with long‐term instrumental degradation, (2) a more exact representation of the effects of random errors in the radiances and, for the first time, (3) the use of MOPITT's near‐infrared (NIR) radiances to complement the thermal‐infrared (TIR) radiances. Exploiting TIR and NIR radiances together facilitates retrievals of CO in the lowermost troposphere. V5 retrieval products based (1) solely on TIR measurements, (2) solely on NIR measurements and (3) on both TIR and NIR measurements are separately validated and analyzed. Actual retrieved CO profiles and total columns are compared with equivalent retrievals based on in situ measurements from (1) routine NOAA aircraft sampling mainly over North America and (2) the “HIAPER Pole to Pole Observations” (HIPPO) field campaign. Particular attention is focused on the long‐term stability and geographical uniformity of the retrieval errors. Results for the retrieved total column clearly indicate reduced temporal bias drift in the V5 products compared to the V4 product, and do not exhibit a positive bias in the Southern Hemisphere, which is evident in the V4 product.

Preliminary signs of the initiation of deep convection by GNSS

Abstract. This study reports on the exploitation of GNSS (Global Navigation Satellite System) and a new potential application for weather forecasts and nowcasting. We focus on GPS observations (post-processing with a time resolution of 5 and 15 min and fast calculations with a time resolution of 5 min) and try to establish typical configurations of the water vapour field which characterise convective systems and particularly which supply precursors of their initiation are associated with deep convection. We show the critical role of GNSS horizontal gradients of the water vapour content to detect small scale structures of the troposphere (i. e. convective cells), and then we present our strategy to obtain typical water vapour configurations by GNSS called "H2O alert". These alerts are based on a dry/wet contrast taking place during a 30 min time window before the initiation of a convective system. GNSS observations have been assessed for the rainfall event of 28–29 June 2005 using data from the Belgian dense network (baseline from 5 to 30 km). To validate our GNSS H2O alerts, we use the detection of precipitation by C-band weather radar and thermal infrared radiance (cloud top temperature) of the 10.8-micrometers channel [Ch09] of SEVIRI instrument on Meteosat Second Generation. Using post-processed measurements, our H2O alerts obtain a score of about 80%. Final and ultra-rapid IGS (International GNSS Service) orbits have been tested and show equivalent results. Fast calculations (less than 10 min) have been processed for 29 June 2005 with a time resolution of 5 min. The mean bias (and standard deviation) between fast and reference post-processed ZTD (zenith total delay) and gradients are, respectively, 0.002 (± 0.008) m and 0.001 (± 0.004) m. The score obtained for the H2O alerts generated by fast calculations is 65%.

Maximum Entropy Temperature–Emissivity Separation in the TIR spectral range using the MaxEnTES algorithm

Temperature–Emissivity Separation (TES) in the Thermal InfraRed (TIR) domain is an illposed problem, in which the number of measurements (the measured spectral radiances) is less than the number of the parameters to be determined (the values of emissivity in each channel plus the temperature). In this paper we present a new method for deriving these parameters from TIR radiance spectra. The presented procedure determines both the spectral emissivity and the temperature by looking for their maximum entropy probability density function, and computes the target temperature and emissivity as the related expectations.

An Enhanced Physical Method for Downscaling Thermal Infrared Radiance

Thermal infrared (TIR) imagery plays a critical role in characterizing land surface processes and modeling energy balances. However, due to the low TIR radiance emitted from the Earth's surface, TIR imagery acquired from satellite thermal sensors is often with limited spatial resolutions, which presents a serious obstacle to its applications in heterogeneous landscapes (e.g., the studies of urban heat island). In this letter, we developed a new method for downscaling TIR radiance by addressing the limitations of a previously developed physical downscaling method by Liu and Pu (2008). To validate our method, a 990-m TIR image was generated by upscaling a 90-m TIR image from the Advanced Spaceborne Thermal Emission and Reflection Radiometer and downscaled back to the 90-m resolution using the proposed method. The results show that the enhanced physical method not only greatly reduced the block effects and smooth effects found in the original physical method but also improved the downscaling accuracy over the original method.

Sensitive detection of aerosol effect on simulated IASI spectral radiance

Guided by radiative transfer modeling of the effects of dust (aerosol) on satellite thermal infrared radiance by many different imaging radiometers, in this article, we present the aerosol-effected satellite radiative signal changes in the top of atmosphere (TOA). The simulation of TOA radiance for Infrared Atmospheric Sounding Interferometer (IASI) is performed by using the RTTOV fast radiative transfer model. The model computation is carried out with setting representative geographical atmospheric models and typical default aerosol climatological models under clear sky condition. The radiative differences (in units of equivalent black body brightness temperature differences (BTDs)) between simulated radiances without consideration of the impact of aerosol (Aerosol-free) and with various aerosol models (Aerosol-modified) are calculated for the whole IASI spectrum between 3.62 and 15.5μm. The comparisons of BTDs are performed through 11 aerosol models in 5 classified atmospheric models. The results show that the Desert aerosol model has the most significant impact on IASI spectral simulated radiances than the other aerosol models (Continental, Urban, Maritime types and so on) in Mid-latitude Summer, contributing to the mineral aerosol components contained. The value of BTDs could reach up to 1K at peak points. The atmospheric window spectral region between 900 and 1100cm−1 (9.09–11.11μm) is concentrated after the investigation for the largest values of aerosol-affected radiance differences. BTDs in IASI spectral region between 645 and 1200cm−1 occupies the largest oscillation and the major part of the whole spectrum. The IASI highest window peak-points channels (such as 9.4 and 10.2μm) are obtained finally, which are the most sensitive ones to the simulated IASI radiance.

Use of ASTER and MODIS thermal infrared data to quantify heat flow and hydrothermal change at Yellowstone National Park

The overarching aim of this study was to use satellite thermal infrared (TIR) remote sensing to monitor geothermal activity within the Yellowstone geothermal area to meet the missions of both the U.S. Geological Survey and the Yellowstone National Park Geology Program. Specific goals were to: 1) address the challenges of monitoring the surface thermal characteristics of the >10,000 spatially and temporally dynamic thermal features in the Park (including hot springs, pools, geysers, fumaroles, and mud pots) that are spread out over ~5000km2, by using satellite TIR remote sensing tools (e.g., ASTER and MODIS), 2) to estimate the radiant geothermal heat flux (GHF) for Yellowstone's thermal areas, and 3) to identify normal, background thermal changes so that significant, abnormal changes can be recognized, should they ever occur (e.g., changes related to tectonic, hydrothermal, impending volcanic processes, or human activities, such as nearby geothermal development). ASTER TIR data (90-m pixels) were used to estimate the radiant GHF from all of Yellowstone's thermal features and update maps of thermal areas. MODIS TIR data (1-km pixels) were used to record background thermal radiance variations from March 2000 through December 2010 and establish thermal change detection limits.A lower limit for the radiant GHF estimated from ASTER TIR temperature data was established at ~2.0GW, which is ~30–45% of the heat flux estimated through geochemical thermometry. Also, about 5km2 of thermal areas was added to the geodatabase of mapped thermal areas. A decade-long time-series of MODIS TIR radiance data was dominated by seasonal cycles. A background subtraction technique was used in an attempt to isolate variations due to geothermal changes. Several statistically significant perturbations were noted in the time-series from Norris Geyser Basin, however many of these did not correspond to documented thermal disturbances. This study provides concrete examples of the strengths and limitations of current satellite TIR monitoring of geothermal areas, highlighting some specific areas that can be improved. This work provides a framework for future satellite-based thermal monitoring at Yellowstone and other volcanic and geothermal systems.

A technique for spatial sharpening of thermal imagery of coastal waters and of watercourses

Although satellite thermal infrared (TIR) remote sensing is a valuable tool for the thermal mapping of coastal waters and watercourses, it has many problematic issues, the most important of which are linked to spatial resolution. In the literature, several algorithms for sharpening thermal imagery can be found. Nevertheless, most of them are devoted to land temperature and are not applicable to water–land mixed pixels. In this article, a new algorithm for sharpening water thermal imagery (SWTI) at the water–land boundaries is presented. SWTI is based on the assumption that a relationship exists between the TIR radiance emitted by the pixels of the scene and the fractional water coverage, the fractional non-vegetated soil coverage and a variable describing the presence of vegetated soils. The algorithm works on a pixel by pixel basis and the results are accepted or refused using an analysis of variance (ANOVA) test. SWTI was applied to two Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired on areas where complex water surfaces are present: the delta of the Po river and the lagoon of Venice (Italy). The spatial resolution of ASTER TIR scenes was improved from 90 to 30 m. Different variables were tested to represent vegetated soils, and the SWTI sensitivity to them has been inspected. The performance of SWTI has been studied using visual inspection and statistical and simulation methods. Visual inspection indicated that the spatial enhancement was significant for most of the water surfaces and, in particular, for watercourses. Most of the details with dimension ≥60 m (i.e. 2 pixels at the final spatial resolution) were discernible. Quantitative analysis showed that the algorithm was successfully applicable for 94% and for 84% of the mixed pixels at the water–land boundary in the Po and in the Venice case studies, respectively. Expected and maximum errors were 1 and 1.4 K in the Po case, and 1 and 2.1 K in the Venice case. These values can be considered satisfactory when compared with the ASTER thermal accuracy (1 K). Further research is required to confirm the accuracy and performance analysis using methods based on accurate and higher resolution thermal imagery and on ground measurements.