Thermal Infrared Satellite Research Articles

Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset

AbstractAs satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

The urban heat island of Basel – seen from different perspectives

For decades thermal infrared satellite imagery has been used for climate studies of a variety of geosystems, including urban areas. Additionally, airborne thermal remotely sensed data can provide high resolution information about urban land surface temperatures (LST). Numerous studies make use of these data for the investigation of urban-rural LST differences, commonly known as the urban heat island (UHI) phenomenon. Most of these studies try to analyse the urban heat island by means of the LST distribution. It seems that the UHI is easy to measure, easy to explain, easy to find, and easy to illustrate. Due to this apparent simplicity some people seem to jump into UHI studies without fully understanding the nature of the phenomenon as far as time and spatial scales, physical processes and the numerous methodological pitfalls inherent to UHI studies are concerned. In this study the use of thermal infrared satellite data with respect to the assessment of the surface UHI is investigated. The need to clearly distinguish between different types of UHI is emphasised by recalling the (surface) temperature and the UHI terminology. The pretended simplicity of UHI effects is in reality a result of complex interactions between local radiation conditions, earth surface heat budget, the urban structure and the boundary layer atmosphere. Different methods may provide completely different results. This paper aims to bring more clearness into the subject by assessing the urban heat island of the city of Basel, Switzerland, by the use of thermal data provided by satellites (Landsat TM/ETM+), helicopter-borne infrared camera (InfraTec VarioCAM®) and ground-based measurements of air temperature profiles. It is shown that UHIs vary essentially with the choice of the respective temperature (LST, air temperature) and height (surface level, street/canopy level, roof level).

Thermal-Infrared Spectral and Angular Characterization of Crude Oil and Seawater Emissivities for Oil Slick Identification

Previous work has shown that the emissivity of crude oil is lower than that of the seawater in the thermal-infrared (TIR) spectrum. Thus, oil slicks cause an emissivity decrease relative to the seawater in that region. The aim of this paper was to carry out experimental measurements to characterize the spectral and angular variations of crude oil and seawater emissivities. The results showed that the crude oil emissivity is lower than the seawater emissivity and that it is essentially flat in the atmospheric window of 8-13 μm. The crude oil emissivity has a marked emissivity decrease with the angle (from 0.956 ± 0.005 at 15 ° to 0.873 ± 0.007 at 65 °), which is even higher than that of the seawater, and thus, the seawater-crude emissivity difference increases with the angle (from +0.030 ±0.007 at close-to-nadir angles up to +0.068 ±0.010 in average at 65 °). In addition, the experimental results were checked by using the dual-angle viewing capability of the Environmental Satellite Advanced Along-Track Scanning Radiometer (ENVISAT-AATSR) images (i.e., 0 °-22 ° and 53 °-55 ° for the nadir and forward views, respectively), with the data acquired during the BP Deepwater Horizon oil slick in 2010. The objective was to explore its applicability to satellite observations. Nadir-forward emissivity differences of +0.028 and +0.017 were obtained for the oil slick and the surrounding clean seawater, respectively. The emissivity differences between the seawater and the oil slick were +0.035 and +0.046 for the nadir and forward views, respectively, which was in agreement with the experimental data. The increase in the seawater-crude emissivity difference with the angle gives significant differences for off-nadir observation angles, showing a new chance of crude oil slick identification from satellite TIR data.

CFD simulation and validation of urban microclimate: A case study for Bergpolder Zuid, Rotterdam

Considering climate change and the rapid trend towards urbanization, the analysis of urban microclimate is gaining importance. The Urban Heat Island (UHI) effect and summer-time heat waves can significantly affect urban microclimate with negative consequences for human mortality and morbidity and building energy demand. So far, most studies on urban microclimate employed observational approaches with field measurements. However, in order to provide more information towards the design of climate adaptive urban areas, deterministic analyses are required. In this study, Computational Fluid Dynamics (CFD) simulations are performed to predict urban temperatures in the Bergpolder Zuid region in Rotterdam, which is planned to be renovated to increase its climate resilience. 3D unsteady Reynolds-averaged Navier–Stokes (URANS) simulations with the realizable k–ε turbulence model are performed on a high-resolution computational grid. The simulations include wind flow and heat transfer by conduction, convection and radiation. The resulting surface temperatures are validated using experimental data from high-resolution thermal infrared satellite imagery performed during the heat wave of July 2006. The results show that the CFD simulations are able to predict urban surface temperatures with an average deviation of 7.9% from the experimental data. It is concluded that CFD has the potential of accurately predicting urban microclimate. Results from CFD simulations can therefore be used to identify problem areas and to evaluate the effect of climate adaptation measures in these areas such as urban greening and evaporative cooling.

Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass

The land surface temperature (LST) is an important parameter when studying the interface between the atmosphere and the Earth's surface. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing is better able to overcome atmospheric influences and to estimate the LST, especially in cloudy regions. However, methods for estimating PMW LSTs at the country and continental scales are still rare. The necessity of training such methods from a temporally dynamic perspective also needs further investigations. Here, a temporally land cover based look-up table (TL-LUT) method is proposed to estimate the LSTs from AMSR-E data over the Chinese landmass. In this method, the synergies between observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), which are onboard the same Aqua satellite, are explored. Validation with the synchronous MODIS LSTs demonstrates that the TL-LUT method has better performances in retrieving LSTs with AMSR-E data than the method that uses a single brightness temperature in 36.5GHz vertical polarization channel. The accuracy of the TL-LUT method is better than 2.7K for forest and 3.2K for cropland. Its accuracy varies according to land cover type, time of day, and season. When compared with the in-situ measured LSTs at four sites without urban warming in the Tibet Plateau, the standard errors of estimation between the estimated AMSR-E LST and in-situ measured LST are from 5.1K to 6.0K in the daytime and 3.1K to 4.5K in the nighttime. Further comparison with the in-situ measured air temperatures at 24 meteorological stations confirms the good performance of the TL-LUT method. The feasibility of PMW remote sensing in estimating the LST for China can complement the TIR data and can, therefore, aid in the generation of daily LST maps for the entire country. Further study of the penetration of PMW radiation would benefit the LST estimations in barren and other sparsely vegetated environments.

Volcanic ash infrared signature: porous non-spherical ash particle shapes compared to homogeneous spherical ash particles

Abstract. The reverse absorption technique is often used to detect volcanic ash clouds from thermal infrared satellite measurements. From these measurements effective particle radius and mass loading may be estimated using radiative transfer modelling. The radiative transfer modelling usually assumes that the ash particles are spherical. We calculated thermal infrared optical properties of highly irregular and porous ash particles and compared these with mass- and volume-equivalent spherical models. Furthermore, brightness temperatures pertinent to satellite observing geometry were calculated for the different ash particle shapes. Non-spherical shapes and volume-equivalent spheres were found to produce a detectable ash signal for larger particle sizes than mass-equivalent spheres. The assumption of mass-equivalent spheres for ash mass loading estimates was found to underestimate mass loading compared to morphologically complex inhomogeneous ash particles. The underestimate increases with the mass loading. For an ash cloud recorded during the Eyjafjallajökull 2010 eruption, the mass-equivalent spheres underestimate the total mass of the ash cloud by approximately 30% compared to the morphologically complex inhomogeneous particles.

Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks

This study presents the successful application of artificial neural networks (ANNs) for downscaling Meteosat Second Generation thermal infrared satellite imagery. The scope is to examine, propose, and develop an integrated methodology to improve the spatial resolution of Meteosat satellite images. The proposed approach may contribute to the development of a general methodology for monitoring and downscaling Earth’s surface characteristics and cloud systems, where there is a clear need for contiguous, accurate, and high-spatial resolution data sets (e.g. improvement of climate model input data sets, early warning systems about extreme weather phenomena, monitoring of parameters such as solar radiation fluxes, land-surface temperature, etc.). Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to validate the downscaled Meteosat images. In terms of the ANNs, a multilayer perceptron (MLP) is used and the results are shown to compare favourably against a linear regression approach.

Lava discharge rate estimates from thermal infrared satellite data for Pacaya Volcano during 2004–2010

Pacaya is one of the most active volcanoes in Central America and has produced lava flows frequently since 1961. All effusive activity between 1961 and 2009 was confined by an arcuate collapse scarp surrounding the northern and eastern flanks. However, the recent breaching of this topographic barrier, and the eruption of a large lava flow outside of the main center of activity, have allowed lava to extend into nearby populated areas, indicating the need for assessment and monitoring of lava flow hazards. We investigated whether a commonly used satellite-based model could produce accurate lava discharge rates for the purpose of near-real-time assessment of hazards during future eruptions and to assess the dynamics of this persistently degassing system. The model assumes a linear relationship between active lava flow area and time-averaged discharge rate (TADR) via a simple conversion factor. We calculated the conversion factor via two methods: (1) best-fitting of satellite-derived flow areas to ground-based estimates of lava flow volume, and (2) theoretically via a parameterized model that takes into account the physical properties of the lava. To apply the latter method, we sampled four lava flows and measured density, vesicularity, crystal content, and major element composition. We found the best agreement of conversion factors in the eruption with the most complete satellite coverage, and used data for these flows to define the linear relationship between area and discharge rate. The physical properties of the sampled flows were essentially identical, so that any discrepancy between the two methods of calculating conversion factors must be due to modeling errors or environmental factors unaccounted for by the parameterized model. However, our best-fitting method provides a new means to set the conversion appropriately, and to obtain self-consistent TADRs. We identified two distinct types of effusive activity at Pacaya: Type 1 activity characterized by initially high TADRs of about 1–10m3·s−1, followed by a waning phase, and Type 2 activity characterized by low, scattered TADRs of about 0.1–1m3·s−1. The risk associated with Type 1 activity is much higher, so identifying the Type 1 radiance signature in satellite imagery is useful for monitoring an ongoing eruption.

MetOp-A/IASI Observed Continental Thermal IR Emissivity Variations

<?Pub Dtl=""?> Satellite thermal infrared (IR) spectral emissivity data have been shown to be significant for atmospheric research and monitoring the Earth's environment. Long-term and large-scale observations that are needed for global monitoring and research can only be supplied by satellite-based remote sensing. Presented here is the global surface IR emissivity data retrieved from the last five and half years of Infrared Atmospheric Sounding Interferometer (IASI) measurements observed from the MetOp-A satellite. Monthly mean surface properties (i.e., skin temperature <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$T_{\rm s}$</tex></formula> and spectral emissivity <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$\varepsilon_{\nu}$</tex></formula> ) with a spatial resolution of 0.5 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\times$</tex></formula> 0.5-degrees latitude-longitude are produced to monitor seasonal and inter-annual variations. Continental IR spectral emissivity derived from satellite ultraspectral IR measurements reveals its variation depending on surface weather and climate conditions. Variation behaviors of continental IR spectral emissivity, associated with the seasonal change as well as weather and climate conditions are initially captured by IASI measurements and will be continuously monitored as provided by the satellite measurements. Surface <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\varepsilon_{\nu}$</tex></formula> retrieved with IASI measurements can be used to assist in monitoring surface weather and surface climate change. Surface <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\varepsilon_{\nu}$</tex> </formula> together with <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$T_{\rm s}$</tex></formula> from current and future operational weather satellites can be utilized as a means of long-term and large-scale monitoring of Earth's surface weather environment and associated changes.

Intercomparison of polar ozone profiles by IASI/MetOp sounder with 2010 Concordiasi ozonesonde observations

Abstract. Validation of ozone profiles measured from a nadir looking satellite instrument over Antarctica is a challenging task due to differences in their vertical sensitivity with ozonesonde measurements. In this paper, ozone observations provided by the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard the polar-orbiting satellite MetOp are compared with ozone profiles collected between August and October 2010 at McMurdo Station, Antarctica, during the Concordiasi measurement campaign. The main objective of the campaign was the satellite data validation. With this aim 20 zero-pressure sounding balloons carrying ozonesondes were launched during this period when the MetOp satellite was passing above McMurdo. This makes the dataset relevant for comparison, especially because the balloons covered the entire altitude range of IASI profiles. The validation methodology and the collocation criteria vary according to the availability of global positioning system auxiliary data with each electro-chemical cell ozonesonde observation. The relative mean difference is shown to depend on the vertical range investigated. The analysis shows a good agreement in the troposphere (below 10 km) and middle stratosphere (25–40 km), where the differences are lower than 10%. However a significant positive bias of about 10–26% is estimated in the lower stratosphere at 10–25 km, depending on altitude. The positive bias in the 10–25 km range is consistent with previously reported studies comparing in situ data with thermal infrared satellite measurements. This study allows for a better characterization of IASI-retrieved ozone over the polar region during ozone depletion/recovery processes.

Precipitation from Space: Advancing Earth System Science

Of the three primary sources of spatially contiguous precipitation observations (surface networks, ground-based radar, and satellite-based radar/radiometers), only the last is a viable source over ocean and much of the Earth's land. As recently as 15 years ago, users needing quantitative detail of precipitation on anything under a monthly time scale relied upon products derived from geostationary satellite thermal infrared (IR) indices. The Special Sensor Microwave Imager (SSMI) passive microwave (PMW) imagers originated in 1987 and continue today with the SSMI sounder (SSMIS) sensor. The fortunate longevity of the joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Tropical Rainfall Measuring Mission (TRMM) is providing the environmental science community a nearly unbroken data record (as of April 2012, over 14 years) of tropical and sub-tropical precipitation processes. TRMM was originally conceived in the mid-1980s as a climate mission with relatively modest goals, including monthly averaged precipitation. TRMM data were quickly exploited for model data assimilation and, beginning in 1999 with the availability of near real time data, for tropical cyclone warnings. To overcome the intermittently spaced revisit from these and other low Earth-orbiting satellites, many methods to merge PMW-based precipitation data and geostationary satellite observations have been developed, such as the TRMM Multisatellite Precipitation Product and the Climate Prediction Center (CPC) morphing method (CMORPH. The purpose of this article is not to provide a survey or assessment of these and other satellite-based precipitation datasets, which are well summarized in several recent articles. Rather, the intent is to demonstrate how the availability and continuity of satellite-based precipitation data records is transforming the ways that scientific and societal issues related to precipitation are addressed, in ways that would not be otherwise possible. These developments have taken place in parallel with the growth of an increasingly interconnected scientific environment. Scientists from different disciplines can easily interact with each other via information and materials they encounter online, and collaborate remotely without ever meeting each other in person. Likewise, these precipitation datasets are quickly and easily available via various data portals and are widely used. Within the framework of the NASA/JAXA Global Precipitation Measurement (GPM mission, these applications will become increasingly interconnected. We emphasize that precipitation observations by themselves provide an incomplete picture of the state of the atmosphere. For example, it is unlikely that a richer understanding of the global water cycle will be possible by standalone missions and algorithms, but must also involve some component of data, where model analyses of the physical state are constrained alongside multiple observations (e.g., precipitation, evaporation, radiation). The next section provides examples extracted from the many applications that use various high-resolution precipitation products. The final section summarizes the future system for global precipitation processing.

A multi-stage tracking for mustard rot disease combining surface meteorology and satellite remote sensing

Highlights Persistent low temperature in second fortnight of January triggered Sclerotinia rot. Regular night time thermal infrared satellite observations helped issue advisories. Quadrant-based cl...

A New Technique Using Infrared Satellite Measurements to Improve the Accuracy of the CALIPSO Cloud-Aerosol Discrimination Method

In this paper, we develop a new technique called the brightness temperature difference cloud and aerosol discrimination algorithm (BTD CAD) that uses thermal infrared satellite measurements to improve the accuracy of the cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) CAD algorithm. It has been shown that the CALIPSO CAD algorithm can misclassify dense dust as cloud because the CALIPSO two-wavelength backscatter lidar operates at 532 and 1064 nm where very similar scattering properties are known to exist between dense dust and cloud. Therefore, we use the 11 and 12 μm thermal infrared channels from both the moderate resolution imaging spectroradiometer (MODIS) and the spinning enhanced visible and infrared imager (SEVIRI), which are very sensitive to dust concentration, in order to reduce the frequency of the dust misclassifications encountered by the CALIPSO CAD algorithm. For the two Saharan dust events presented in this paper, both the MODIS and SEVIRI BTD CAD techniques performed well but the MODIS BTD CAD correctly reclassified more CALIPSO CAD misclassifications as dust. After applying both techniques to all the daytime CALIPSO transects over North Africa during June 2007, the MODIS and SEVIRI BTD CAD increased the total number of detected aerosol layers by approximately 10% and 4%, respectively. Even though the Version 3 (V3) CAD algorithm is significantly more accurate in deciphering between dense dust and clouds than the Version 2 algorithm, the V3 still showed some dust misclassifications among the case studies. Thus, the BTD CAD technique can help reduce the frequency of dust misclassifications encountered by the V3 CAD algorithm.

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.

Boundary layer versus free tropospheric CO budget and variability over the United States during summertime

The regional Weather Research and Forecasting Model with Chemistry (WRF‐Chem) version 3.2 is used to analyze the carbon monoxide (CO) budget and spatiotemporal variability over the United States in summer 2008. CO tracers for different emission sources are used to separate the modeled CO fields into the contributions from individual sources (pollution inflow to the model domain, chemical production within the model domain, and local emissions by type). The implementation of tagged CO tracers into WRF‐Chem constitutes an innovative aspect of this work. We evaluate WRF‐Chem CO concentrations using aircraft, satellite, and surface observations. The model reproduces fairly well the observed CO concentrations for the entire altitude range but tends to underestimate fire emissions and overestimate anthropogenic sources and CO from pollution inflow. Evaluation results also show that the model gives a good representation of background CO mixing ratios with mean biases better than ∼15 ppbv in the free troposphere (FT) and less than 20 ppbv toward the surface. The analysis of the CO budget over the contiguous United States shows that at the surface, CO from inflow is the dominant source, with a mean relative contribution of 63 ± 19%. Anthropogenic and photochemically produced CO contribute to surface CO to a lesser extent (18 ± 14% and 14 ± 8%, respectively). The average contribution from fire emissions to surface CO during the period examined is small (2 ± 5%) but can have a large impact in certain regions and times. Similar trends are found in the planetary boundary layer (PBL). In the FT, the average CO relative contributions are estimated as 84 ± 12% for CO from inflow, 5 ± 4% for anthropogenic CO, 9 ± 7% for photochemically produced CO, and 1 ± 5% for CO from fires. Using WRF‐Chem simulations, we also examine the representation of surface and PBL CO concentration variability that would be captured by current near infrared (NIR) and thermal infrared (TIR) satellite observations. We find that CO total columns are impacted by variability in the lowermost troposphere (LMT) at the ∼10% level, indicating limited sensitivity for air quality applications. The same is generally true for the FT CO column obtained from TIR measurements, although this does provide a good measure for capturing the pollution inflow variability and is therefore valuable in providing initial and boundary conditions to constrain regional models. We further analyze the situations under which the LMT concentrations obtained from recently demonstrated multispectral (NIR + TIR) observations capture the surface CO variability.

The role of Pine Island Glacier ice shelf basal channels in deep-water upwelling, polynyas and ocean circulation in Pine Island Bay, Antarctica

AbstractSeveral hundred visible and thermal infrared satellite images of Antarctica’s southeast Amundsen Sea from 1986 to 2011, combined with aerial observations in 2009, show a strong inverse relation between prominent curvilinear surface depressions and the underlying basal morphology of the outer Pine Island Glacier ice shelf. Shipboard measurements near the calving front reveal positive temperature, salinity and current anomalies indicative of melt-laden, deep-water outflows near and above the larger channel termini. These buoyant plumes rise to the surface and are expressed as small polynyas in the sea ice and thermal signatures in the open water. The warm upwellings also trace the cyclonic surface circulation in Pine Island Bay. The satellite coverage suggests changing modes of ocean/ice interactions, dominated by leads along the ice shelf through 1999, fast ice and polynyas from 2000 to 2007, and larger areas of open water since 2008.

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.

Directional Effects on Land Surface Temperature Estimation From Meteosat Second Generation for Savanna Landscapes

Structured canopies can show pronounced directional effects which influence land surface temperature (LST) estimates from thermal infrared satellite data. The effects depend on illumination and viewing geometries, because changes in these two geometries effectively cause the sensor to “see” different fractions of the canopy and the “background” surface (bare soil or low vegetation). Furthermore, parts of these two components will be in shadow, depending on the specific geometry of the canopy and its structure. This paper investigates these directional effects for a specific savanna site in West Africa and extends the findings to areas with denser tree crown cover. This is achieved by modeling the combined effects of the structured surface with a geometric optics model. The model assumes that the surface consists of four components: shaded and sunlit tree canopies and shaded and sunlit backgrounds. The brightness temperatures of these four surface components are provided by in situ measurements at the validation site, and emissivities are taken from the Land Surface Analysis Satellite Applications Facility (LSA-SAF) project. The LST modeling is performed for the geometry of the geostationary Meteosat Second Generation and for nadir geometry. Analyses of the temperature differences between the LST estimates for the two geometries show that, in many cases, the directional effects exceed 1°C within a day and that the timing and the sign of the effects change with season. Directional errors due to structured canopies are currently not considered in error estimates of operationally available LST products, e.g., the LSA-SAF LST product or the Moderate Resolution Imaging Spectroradiometer (MODIS) LST/emissivity products.

Optimized split-window coefficients for deriving surface temperatures from inland water bodies

Large inland water bodies constituting lakes, reservoirs and inland-seas are excellent proxy indicators for climate change. Using thermal infrared satellite data, a recent study found that a global set of inland water bodies showed significant warming in seasonal nighttime Lake Surface Water Temperatures (LSWTs) between 1985 and 2009. Split-window land surface temperature (LST) retrievals are typically tuned for a broad range of land surface emissivities and global atmospheric conditions, and are not optimized for inland water body surfaces, whereas split-window sea-surface temperatures (SSTs) are only tuned for a single emissivity (water), but over ocean atmospheres. Over inland water bodies, these two approaches can lead to region dependent errors in LSWTs, spurious trends, and inconsistencies between sensors in the long-term temperature record of inland water bodies. To address this issue, the primary goal of this paper was to develop a methodology for deriving a set of optimized split-window coefficients, individually tuned for the regional atmospheric conditions of 169 globally distributed, saline and freshwater inland water bodies from multiple satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua; Along Track Scanning Radiometer (ATSR) including ATSR-1, ATSR-2, AATSR; and Advanced Very High Resolution Radiometer (AVHRR-3). The new Inland Water-body Surface Temperature (IWbST) v1.0 algorithm was applied to Terra MODIS and Advanced Along Track Scanning Radiometer (AATSR) data and validated with in situ water temperature data from sites with widely contrasting atmospheric conditions: Lake Tahoe in California/Nevada, a high-elevation cool and dry site, and the Salton Sea in California, a low-elevation warm and humid site. Analysis showed improved accuracy in LSWTs in terms of bias and RMSE when compared to the standard MODIS LST and AATSR SST products. For example, the IWbST RMSE at Salton Sea was reduced by 0.4 K when compared to the operational MODIS product. For the AATSR data, the IWbST RMSE was reduced by 0.36 K at Tahoe and 0.29 K at Salton Sea when compared to results obtained using the operational AATSR split-window coefficients. The IWbST improvements are significant in relation to the current accuracy of water temperature retrievals from space (< 0.5 K), and will enable the derivation of long-term, accurate LSWTs consistently across multiple sensors for climate studies.

Longitudinal and temporal thermal patterns of the French Rhône River using Landsat ETM+ thermal infrared images

Understanding the thermal regime of rivers is a key issue for predicting ecosystem change in the context of global warming. However, water temperature is not only influenced by air temperature. To better highlight relative contribution of factors controlling water temperature, we used satellite thermal infrared (TIR) images from Landsat ETM+ to investigate longitudinal and temporal variations in thermal patterns of the French Rhone River. Because satellite TIR remote sensing is limited to large rivers, we used an automated water extraction technique to remove pixels contaminated by terrestrial surfaces. We calculated water surface temperatures of the 500 km long reach for 83 dates between 1999 and 2009. The average accuracy and uncertainty of our data, ±1.1 and ±0.4°C for reaches with more than 3 pixels across and ±1.4 and ±0.5°C for reaches with one to 3 pixels across, are comparable to other satellite TIR studies of rivers. Our results confirmed previous studies on the thermal impacts of tributaries and nuclear power plants on the Rhone, providing an understanding of their seasonal pattern and their longitudinal impact. We showed temperature differences of 0–2°C within the largest hydroelectric bypass facilities between the bypass section and the canal, with Montelimar and Caderousse showing the most pronounced differences. Discussion points concern the potential impacts of tributaries and nuclear power plants on the spatio-temporal thermal patterns, as well as the factors responsible for thermal differences in the bypass facilities: length and minimum flow of the bypass section, and tributaries coming into this reach.