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We present an improved water vapor scaling (WVS) model for atmospherically correcting MODIS thermal infrared (TIR) bands in the temperature emissivity separation (TES) algorithm. TES is used to retrieve the land surface temperature and emissivity (LST&E) from MODIS TIR bands 29, 31, and 32. The WVS model improves the accuracy of the atmospheric correction parameters in TES on a band-by-band and pixel-by-pixel basis. We used global atmospheric radiosondes profiles to generate view angle and day–night-dependent WVS coefficients that are valid for all MODIS scan angles up to 65°. We demonstrate the effects of applying the improved WVS model on the retrieval accuracy of MODIS-TES (MODTES) LST&E using a case study for a granule over the southwest USA during very warm and moist monsoonal atmospheric conditions. Furthermore, a comprehensive validation of the MODTES LST&E retrieval was performed over two sites at the quartz-rich Algodones Dunes in California and a grassland site in Texas, USA using three full years of MODIS Aqua data. Results from the case study showed that absolute errors in the emissivity retrieval for the three MODIS TIR bands were reduced on average from 1.4% to 0.4% when applying the WVS method. A Radiance-based method was used to validate the MODTES LST retrievals for and the results showed that application of the WVS method with the MODTES algorithm led to significant reduction in both bias and root mean square error (RMSE) of the LST retrievals at both sites. When the WVS model was applied, LST RMSE's were reduced on average from 1.3 K to 1.0 K at the Algodones Dunes site, and from 1.2 K to 0.7 K at the Texas Grassland site. This study demonstrated that the WVS atmospheric correction model is critical for retrieving MODTES LST with < 1 K accuracy and emissivity with < 1% consistently for a wide range of challenging atmospheric conditions and land surface types. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an important parameter in many ecological studies. The current Root Mean Square Error (RMSE) in standard MODIS and ASTER LST products is greater than 1 K, and for ASTER can be as large as 4 K for graybody pixels such as vegetation. Errors of 3 to 8 K have been observed for ASTER in humid conditions, making knowledge of atmospheric water vapor content critical in retrieving accurate LST. For this reason improved accuracy in LST measurements through the synthesis of visible-to-shortwave-infrared (VSWIR) derived water vapor maps and Thermal-Infrared (TIR) data is one goal of the Hyperspectral Infrared Imager, or HyspIRI, mission. The 2011 ER-2 Delano/Lost Hills flights acquired data with both the MODIS/ASTER Simulator (MASTER) and Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) instruments flown concurrently. This study compares LST retrieval accuracies from the standard JPL MASTER temperature products produced using the temperature–emissivity separation (TES) algorithm, and the water vapor scaling (WVS) atmospheric correction method proposed for HyspIRI. The two retrieval methods are run both with and without high spatial resolution AVIRIS-derived water vapor maps to assess the improvement from VSWIR synthesis. We find improvement using VSWIR derived water vapor maps, with the WVS method being most accurate overall. For closed canopy agricultural vegetation we observed temperature retrieval RMSEs of 0.49 K and 0.70 K using the WVS method on MASTER data with and without AVIRIS derived water vapor, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This study compares six satellite-retrieved land surface emissivity (LSE) products over gravel plains and sand dunes of the hyper-arid Namib desert in Namibia and validates them with in-situ measurements performed with the ‘emissivity box method’. The following products are compared: LSE derived by the Land Surface Analysis — Satellite Application Facility (LSA-SAF) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), LSE products MOD11A2.C5, MOD11B1.C4.1, and MOD11B1.C5 derived for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS-Terra, LSE derived with the Temperature Emissivity Separation (TES) algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard EOS-Terra, and LSE derived with the TES algorithm for EOS-Terra/MODIS data. The LSA-SAF, MOD11A2.C5, and MOD11B1.C5 algorithms directly or indirectly utilize land cover classification and vegetation cover fraction data with the result that for arid regions their LSE are practically identical to the bare ground emissivities assigned to those classes. Over the gravel plains, mean LSA-SAF, ASTER-TES, and MODTES LSE are about 0.950 in the 11μm range, whereas mean MOD11A2.C5 and MOD11B1.C5 are about 1.5% (~1K) higher. The LSA-SAF algorithm misclassifies the sand dunes as ‘open & closed shrubland’, which results in an overestimated mean LSE (0.969). Since MOD11A2.C5 and MOD11B1.C5 utilize a similar classification and similar emissivity library data, their LSE estimates for the sand dunes are also too high (mean of 0.972 and 0.980, respectively). In contrast, the physics-based ASTER-TES and MODTES algorithms estimate mean sand dune LSE as 0.952 and 0.948, respectively. The physics-based MOD11B1.C4.1 algorithm produced noisy LSE estimates with frequent outliers at 5km resolution: spatial averaging yielded mean LSE of 0.950 and 0.954 for the gravel plains and the sand dunes, respectively. Based on a combined analysis of in-situ LSE and TES retrieved LSE, and also accounting for uncertainty in the fraction of dry grass (only gravel plains), for future work it is recommended to use SEVIRI ch10.8 emissivities of 0.941±0.004 for the sand dunes and 0.944±0.015 for the gravel plains, respectively. The results suggest that split window algorithms would benefit significantly from using physically based MODTES LSE. [Copyright &y& Elsevier]

Abstract: 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 0.4K when compared to the operational MODIS product. For the AATSR data, the IWbST RMSE was reduced by 0.36K at Tahoe and 0.29K 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 (<0.5K), and will enable the derivation of long-term, accurate LSWTs consistently across multiple sensors for climate studies. [Copyright &y& Elsevier]

Abstract: This study investigates the effects of soil moisture (SM) on thermal infrared (TIR) land surface emissivity (LSE) using field- and satellite-measurements. Laboratory measurements were used to simulate the effects of rainfall and subsequent surface evaporation on the LSE for two different sand types. The results showed that the LSE returned to the dry equilibrium state within an hour after initial wetting, and during the drying process the SM changes were uncorrelated with changes in LSE. Satellite retrievals of LSE from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) were examined for an anomalous rainfall event over the Namib Desert in Namibia during April, 2006. The results showed that increases in Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture and Tropical Rainfall Measuring Mission (TRMM) rainfall estimates corresponded closely with LSE increases of between 0.08–0.3 at 8.6µm and up to 0.03 at 11µm for MODIS v4 and AIRS products. This dependence was lost in the more recent MODIS v5 product which artificially removed the correlation due to a stronger coupling with the split-window algorithm, and is lost in any algorithms that force the LSE to a pre-determined constant as in split-window type algorithms like those planned for use with the NPOESS Visible Infrared Imager Radiometer Suite (VIIRS). Good agreement was found between MODIS land surface temperatures (LSTs) derived from the Temperature Emissivity Separation (TES) and day/night v4 algorithm (MOD11B1 v4), while the split-window dependent products (MOD11B1 v5 and MOD11A1) had cooler mean temperatures on the order of 1–2K over the Namib Desert for the month of April 2006. [Copyright &y& Elsevier]

Abstract: Knowledge of the Land Surface Emissivity (LSE) in the Thermal Infrared (TIR: 8–12µm) part of the electromagnetic spectrum is essential to derive accurate Land Surface Temperatures (LSTs) from spaceborne TIR measurements. This study focuses on validation of the emissivity product in the North American ASTER Land Surface Emissivity Database (NAALSED) v2.0 — a mean seasonal, gridded emissivity product produced at 100m spatial resolution using all Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes from 2000 to 2008 over North America (http://emissivity.jpl.nasa.gov). The NAALSED emissivity product was validated over bare surfaces with laboratory measurements of sand samples collected at nine pseudo-invariant sand dune sites located in the western/southwestern USA. The nine sand dune sites cover a broad range of surface emissivities in the TIR. Results show that the absolute mean emissivity difference between NAALSED and the laboratory results for the nine validation sites and all five ASTER TIR bands was 0.016 (1.6%). This emissivity difference is equivalent to approximately a 1K error in the land surface temperature for a material at 300K in the TIR. [Copyright &y& Elsevier]

Abstract: Thermal Infrared (TIR) data are supplied by instruments on several satellite platforms including the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), which was launched on the Terra satellite in 1999. ASTER has five bands in the TIR and a spatial resolution of 90 m. A mean seasonal, gridded, Land Surface Temperature and Emissivity (LST&E) database has been produced at 100 m spatial resolution using all the ASTER scenes acquired for the months of Jan–Mar (winter) and Jul–Sep (summer) over North America. Version 2.0 of the North American ASTER Land Surface Database (NAALSED) (http://emissivity.jpl.nasa.gov) has now been released and includes two key refinements designed to improve the accuracy of emissivities over water bodies and account for the effects of fractional vegetation cover. The water adjustment replaces ASTER emissivity values over inland water bodies with a measured library emissivity spectrum of distilled water, and then re-calculates the surface temperatures using a split-window algorithm. The accuracy of ASTER emissivities over vegetated surfaces is improved by applying a fractional vegetation cover adjustment (TES_Pv) to the ASTER Temperature Emissivity Separation (TES) calibration curve. Comparisons of NAALSED emissivity spectra with in-situ data measured over a grassland in Northern Texas resulted in a combined absolute difference for all five ASTER bands of 1.0% for the summer emissivity data, and 0.1% for the winter data—a 33–50% improvement over the original TES results. [Copyright &y& Elsevier]

Eight new refinements were implemented in the MODIS Land Surface Temperature and Emissivity (LST&E) product suite when transitioning from version 4 (V4) to version 5 (V5). The refinements were designed to improve the spatial coverage, stability, and accuracy of the product suite. Version 4.1 (V4.1) is an interim collection which uses V5 input products (MOD02, MOD03, MOD07, MOD10, and MOD35), but the LST&E retrieval algorithm is unchanged from V4 in which the split-window and day/night temperature retrieval algorithms are only partially incorporated, and not fully incorporated as in V5. A test dataset for the V4.1 product was produced by MODAPS for a 3-month period from July through September 2004, and after an initial evaluation period, it was decided to generate the V4.1 product from mission period 2007001 onwards as a continuation of previous years of V4 data. This paper compares MODIS retrieved surface emissivities between V4, V4.1 and V5 using the level-3 MODIS daily LST&E product, MOD11B1.Comparisons of MOD11B1 retrieved surface emissivity during the Jul–Sep 2004 test period with lab measurements of sand samples collected at the Namib desert, Namibia result in a combined mean absolute emissivity difference for bands 29 (8.55 µm), 31 (11 µm) and 32 (12 µm) of 1.06%, 0.65% and 1.93% for V4, V4.1 and V5 respectively. Maximum band 29 emissivity differences with the lab results were 4.10%, 2.96% and 8.64% for V4, V4.1 and V5 respectively. These results indicate that over arid and semi-arid areas, users should consider using MODIS V4 or V4.1 data instead of V5. Furthermore, users should be careful not to develop time series from a mixture of product versions that could introduce artifacts at version boundaries. [Copyright &y& Elsevier]

High spatial resolution land surface temperature (LST, <100 m) is crucial for agricultural water management, crop water stress monitoring, fire mapping, urban heat island study and volcano eruption detection. LST retrievals from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) launched in June 2018, together with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, launched in 1999) and the Landsat series (since 1972), comprise the state-of-the-art high spatial resolution LST datasets publicly accessible. Recently, we generated the ECOSTRESS LST product over Europe and Africa using both the temperature and emissivity separation (TES) and split-window (SW) algorithms under the European ECOSTRESS Hub (EEH). Here, we validated the official Jet Propulsion Laboratory (JPL) TES (Collection 1), EEH TES and EEH SW ECOSTRESS LST products over Europe and Africa between August 1, 2018 and December 31, 2021 by comparing against the in-situ measurements at 9 sites over a wide variety of land cover types. Meanwhile, the validation results were compared with those obtained for ASTER and Landsat LST at the same sites for a thorough understanding of the consistency among these high spatial resolution LST products. The results reveal that the three ECOSTRESS LST products have consistent performances, with an overall RMSE around 2 K. A cold bias around 1 K exists for all three ECOSTRESS LST, which is presumably originated from the radiometric calibration of the sensor in Collection 1 data. The Landsat LST shows a similar accuracy, with an RMSE of 2.20 K and bias of 0.54 K. The EEHSW LST show the highest consistency with Landsat LST, possibly due to the identical emissivity correction process. The performance of ASTER LST is also similar, with an RMSE of 1.98 K and bias of 0.9 K. The precisions of all the LST products are around 1.5 K. Future recalibration of the ECOSTRESS Level 1 radiance data in Collection 2 is expected to further improve the accuracy of ECOSTRESS LST. Overall, this study supports the adaptation of LST retrieval algorithms for the future thermal missions. • Difference types of ECOSTRESS LST were validated using in-situ measurements. • ECOSTRESS LST were compared with ASTER and Landsat LST. • The three ECOSTRESS LST performed similarly with RMSE ∼2 K and bias ∼1 K. • ECOSTRESS, ASTER and Landsat had consistent performances. [ABSTRACT FROM AUTHOR]

Thermal infrared satellite observations of the Earth's surface are widely used to retrieve Land Surface Temperature (LST) and monitor LST changes around the world. Since January 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) has provided daily observations of LST with a spatial resolution of 750 m at nadir. Comparison of the standard VIIRS LST product with the equivalent daily standard product from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and with ground-based measurements over vegetated and inland water surfaces showed good agreement. Analysis indicated the accuracy and precision of the VIIRS product over these cover types was 0.2 K and 2.0 K respectively provided the analyses included appropriate compensation for any spatial heterogeneity in LST within the validation site. However, comparisons between in situ LST and the VIIRS and MODIS LST over arid and semi-arid regions indicate both satellite products significantly underestimate the LST, and the VIIRS algorithm can have large errors in the retrieved LST over areas of high atmospheric water vapor. Errors of up to 4 K were observed over semi-arid and arid areas due to incorrect characterization of emissivity, and differences of up to 15 K were observed over areas with high atmospheric water content between the VIIRS LST and matching MODIS LST. [ABSTRACT FROM AUTHOR]

Abstract: This study provides an introduction to the HyspIRI mission a National Research Council “Decadal Survey” mission that combines a 213 channel visible, near−infrared and shortwave infrared (VSWIR) imaging spectrometer with an 8 channel multispectral thermal infrared (TIR) instrument and evaluates some of its potential in urban science. Potential synergies between VSWIR and TIR data are explored using analogous airborne data acquired over the Santa Barbara metropolitan region in June, 2008. These data were analyzed at both their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60m spatial resolution similar to HyspIRI. A spectral library of dominant urban materials (e.g., grass, trees, soil, roof types, roads) was developed from field and airborne-measured spectra using Multiple-Endmember Spectral Mixture Analysis (MESMA) and used to map fractions of impervious, soil, green vegetation (GV, e.g., trees, lawn) and non-photosynthetic vegetation (NPV). Land Surface Temperature (LST) and emissivity were also retrieved from the airborne data. Co-located pixels from the VSWIR and TIR airborne data were used to generate reflectance/emissivity spectra for a subset of urban materials. MESMA was used to map GV, NPV, soil and impervious fractions at the different spatial resolutions and compare the fractional estimates across spatial scales. Important surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and surface temperature were also determined for and evaluated for 14 urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography. Fractions for GV and NPV were highly correlated with validation fractions at all spatial scales, producing a near 1:1 relationship but with a <10% overestimate of GV from MESMA. Similar, high correlations were observed for impervious surfaces, although impervious was significantly underestimated in most urban areas and soil overestimated. Comparison of fractions across scales showed high correlation between GV and NPV at 7.5 and 60m resolution, suggesting that HyspIRI will provide accurate measures of these two measures in urban areas. An inverse relationship between vegetation cover and LST was observed. Albedo proved to be highly variable and poorly correlated with LST. Broadband emissivity was far less variable with high emissivity surfaces (~0.95) including vegetation, water and asphalt, and low emissivity surfaces (<0.95) including selected roof types, beach sands and senesced grasslands. Residential and commercial areas showed a general pattern of increasing LST with increasing impervious fraction with the highest impervious fractions mapped in commercial areas, roads and roofs. Fine scale spatial structure in cover fractions and LST demonstrated important departures from a simple inverse relationship between GV and LST, even at 60m. The results demonstrate the utility of HyspIRI data for urban studies and provide an insight of what will be possible on a global scale when HyspIRI data become available. [Copyright &y& Elsevier]

The National Aeronautics and Space Administration (NASA) has proposed the launch of a new space-borne sensor called HyspIRI (Hyperspectral and Infrared Imager) which will cover the spectral range from 0.4–12 μm. Two instruments will be mounted on HyspIRI platform: 1) a hyperspectral instrument which can sense earth surface between 0.4 and 2.5 μm at 10 nm intervals and 2) a multispectral infrared sensor will acquire images between 3 and 12 μm in eight spectral bands (one in Mid infrared (MIR) and seven in Thermal Infrared (TIR)). The TIR spectral wavebands will be positioned based on their importance in various applications. This study aimed to identify HyspIRI optimal TIR wavebands position for earth compositional mapping. A Genetic Algorithm coupled with the Spectral Angle Mapper (GA-SAM) was used as a spectral bands selector. High dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) emissivity spectra comprised of 202 spectral bands of Cuprite and Death Valley regions were used to select meaningful subsets of bands for earth compositional mapping. The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 50 times. High calibration (> 95%) and validation (> 90%) accuracies were achieved with a limited number (seven) of spectral bands selected by GA-SAM. The knowledge of important band positions will help the scientists of the HyspIRI group to place spectral bands in regions where accuracies of earth compositional mapping can be enhanced. [ABSTRACT FROM AUTHOR]

The Hyperspectral Infrared Imager (HyspIRI) mission is proposed to be the first satellite system with the capability to provide global, repeat coverage across the visible and shortwave infrared spectrum, as well as eight channels in the midwave and thermal infrared. HyspIRI has stated objectives to address a host of pressing earth science questions, from radiation budgets to ecosystem functions. A sizable science community has grown to support the mission, and their ongoing research demonstrates HyspIRI's potential to greatly expand our knowledge of the earth system. This special issue features a collection of papers, some reviews and others novel science, that cover the wide array of topics relevant to HyspIRI's mission and reaffirm the necessity for HyspIRI. [ABSTRACT FROM AUTHOR]