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Abstract: The accuracy of land surface temperatures (LSTs) derived from the Advanced Along-Track Scanning Radiometer (AATSR) was assessed in a test site in Valencia, Spain from 2002 to 2008. AATSR LSTs were directly compared with concurrent ground measurements over homogeneous, full-vegetated rice fields in the conventional temperature-based (T-based) method. We also applied the new radiance-based (R-based) method over bare soil and water surfaces, where ground LST measurements were not available. In the R-based method, ground LSTs are simulated from AATSR brightness temperatures in the 11μm band and radiative transfer simulations using surface emissivity data and atmospheric water vapor and temperature profiles. The accuracy of the R-based ground LSTs depends on how well the profiles used in simulations represent the actual atmosphere at the time of AATSR observations. This can be checked with the difference δ(T11-T12) between the actual AATSR and the profile-based simulated difference in the 11 and 12μm brightness temperatures (T11 and T12, respectively). We found that for −0.6K<δ(T11-T12)<0.6K, the R-based LSTs were accurate within ±1.0K and can be used for LST validation. For the data analyzed here, the AATSR operational algorithm overestimated the ground LST by 2 to 5K, showing that the auxiliary data utilized within the retrieval scheme (biome classification and fractional vegetation cover maps at 0.5°×0.5° resolution) should be improved and provided at the same spatial resolution as the AATSR data (1km2). When the AATSR algorithm was optimized with biome and fractional vegetation cover selected according to the nature of each surface, LST errors showed negligible average biases and rmse=±0.5K for full vegetation and water, and ±1.1K for bare soil. Furthermore, we checked an alternative algorithm explicitly dependent on emissivity, which provided accurate LSTs for all the surfaces studied, with small biases, rmse from ±0.4 to ±0.6K and most LST errors within ±1.0K. The algorithm requires monthly emissivity maps at 1km2, which can be derived from classification and fractional vegetation cover estimated from optical AATSR data. The results of this paper show the high LST accuracy achievable with AATSR data in ideal conditions. While it is necessary to establish and maintain highly homogeneous T-based validation sites, the R-based method provides an alternative for the semi-operational, long-term evaluation of LST products at global scale, since it is applicable over surfaces with varied LST and atmospheric regimes where ground LST measurements are not feasible. [Copyright &y& Elsevier]

Knowledge of surface emissivity in the thermal infrared (TIR) region is critical for determining the land surface temperature (LST) from remote-sensing measurements. If emissivity is not well determined, it can cause a significant systematic error in obtaining the LST. The main aim of this paper is to compare different methods for measuring accurate land surface emissivity in the field, namely, the Box method and the Temperature and Emissivity Separation (TES) algorithm. Field emissivities were compared with soil spectra from laboratory measurements. Emissivities were measured for the bands of a multispectral radiometer CE312-2 with effective wavelengths at 8.4, 8.7, 9.1, 10.6, and 11.3 μm, similar to the Advanced Spaceborne Thermal Emission and Reflection Radiometer TIR bands, and a wide channel 8-13 μm. The measurements were made at two sites in New Mexico: the White Sands National Monument and an open shrub land in the Jornada Experimental Range. The measurements show that for both sites the emissivities derived with the Box method agree with those derived with the TES algorithm for the 10.6 and 11.3 jμm bands. However, the emissivities for the shorter wavelength bands are higher when derived with the Box method than those with the TES algorithm, with differences ranging from 2% to 7%. The field emissivities agree within 2% with the laboratory spectrum for the 8-13-, 11.3-, and 10.6-μm bands. However, the field and laboratory measurements in general differ from 2.4% to 9% for the shorter wavelength bands, with the larger value most likely caused by variations in soil moisture. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an essential climate variable (ECV) for monitoring the Earth climate system. To ensure accurate retrieval from satellite data, it is important to validate satellite derived LSTs and ensure that they are within the required accuracy and precision thresholds. An emissivity-dependent split-window algorithm with viewing angle dependence and two dual-angle algorithms are proposed for the Sentinel-3 SLSTR sensor. Furthermore, these algorithms are validated together with the Sentinel-3 SLSTR operational LST product as well as several emissivity-dependent split-window algorithms with in-situ data from a rice paddy site. The LST retrieval algorithms were validated over three different land covers: flooded soil, bare soil, and full vegetation cover. Ground measurements were performed with a wide band thermal infrared radiometer at a permanent station. The coefficients of the proposed split-window algorithm were estimated using the Cloudless Land Atmosphere Radiosounding (CLAR) database: for the three surface types an overall systematic uncertainty (median) of −0.4 K and a precision (robust standard deviation) 1.1 K were obtained. For the Sentinel-3A SLSTR operational LST product, a systematic uncertainty of 1.3 K and a precision of 1.3 K were obtained. A first evaluation of the Sentinel-3B SLSTR operational LST product was also performed: systematic uncertainty was 1.5 K and precision 1.2 K. The results obtained over the three land covers found at the rice paddy site show that the emissivity-dependent split-window algorithms, i.e., the ones proposed here as well as previously proposed algorithms without angular dependence, provide more accurate and precise LSTs than the current version of the operational SLSTR product. [ABSTRACT FROM AUTHOR]

Abstract: Different sources of atmospheric water vapor and temperature profiles were used with a radiative transfer model for retrieving land surface temperature (LST) from thermal infrared remote sensing data with the so-called single channel (SC) method. Retrieved LSTs were compared to concurrent ground measurements over homogeneous rice fields to assess the accuracy of the atmospheric profiles. These included radiosonde balloons launched at the test site near-concurrently to satellite overpasses, re-analysis profiles from the National Centers for Environmental Prediction (NCEP), and satellite sounder products from the Atmospheric Infrared Sounder (AIRS) and the Moderate Imaging Spectroradiometer (MODIS; MOD07 product). SC LSTs were computed for Enhanced Thematic Mapper+ (ETM+), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER), MODIS, and Advanced Along-Track Scanning Radiometer (AATSR). Results show that radiosonde profiles provided the best agreement between ground-measured and satellite-derived LSTs, with root mean square difference (RMSD) better than 1.0K and biases within ±0.5K for most of the cases. As an alternative to radiosonde profiles, NCEP and MOD07 data yielded reasonable results with RMSDs around 1.0K, although LSTs derived from MOD07 profiles showed a slight overestimation (0.5 to 1.0K) of the ground LSTs. AIRS profiles usually underestimated the ground LSTs by 1 to 2K, probably due to the large temporal gap (2–3h) with the other satellite measurements. We propose a test to assess the suitability of atmospheric profiles applicable to sensors with bands at 11 and 12μm in the split-window. So this test plus the SC method may be called split-window SC algorithm, being significantly different from the simple SC method. It implies the calculation of the difference between the LST derived from both bands (Tig −Tjg), which should be close to zero if the atmospheric profile is accurate and the surface emissivity is well known. A small range of Tig −Tjg values around zero can be set for which the LST derived at 11μm is accurate. Using only the profiles passing the test, LSTs derived from MODIS band 31 agreed with the ground data with a mean bias of −0.1K (ground minus satellite) and RMSD of 0.6K, while AATSR band at 11μm yielded a −0.1K bias and RMSD=0.5K. [Copyright &y& Elsevier]

Abstract: The performance of Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) thermal infrared (TIR) data product algorithms was evaluated for low spectral contrast surfaces (such as vegetation and water) in a test site close to Valencia, Spain. Concurrent ground measurements of surface temperature, emissivity, and atmospheric radiosonde profiles were collected at the test site, which is a thermally homogeneous area of rice crops with nearly full vegetation cover in summer. Using the ground data and the local radiosonde profiles, at-sensor radiances were simulated for the ASTER TIR channels and compared with L1B data (calibrated at-sensor radiances) showing discrepancies up to 3% in radiance for channel 10 at 8.3 μm (equivalently, 2.5 °C in temperature or 7% in emissivity), whereas channel 13 (10.7 μm) yielded a closer agreement (maximum difference of 0.5% in radiance or 0.4 °C in temperature). We also tested the ASTER standard products of land surface temperature (LST) and spectral emissivity generated with the Temperature–Emissivity Separation (TES) algorithm with standard atmospheric correction from both global data assimilation system profiles and climatology profiles. These products showed anomalous emissivity spectra with lower emissivity values and larger spectral contrast (or maximum–minimum emissivity difference, MMD) than expected, and as a result, overestimated LSTs. In this work, a scene-based procedure is proposed to obtain more accurate MMD estimates for low spectral contrast materials (vegetation and water) and therefore a better retrieval of LST and emissivity with the TES algorithm. The method uses various gray-bodies or near gray-bodies with known emissivities and assumes that the calibration and atmospheric correction performed with local radiosonde data are accurate for channel 13. Taking the channel 13 temperature (atmospherically and emissivity corrected) as the true LST, the radiances for the other channels were simulated and used to derive linear relationships between ASTER digital numbers and at-ground radiances for each channel. The TES algorithm was applied to the adjusted radiances and the resulting products showed a closer agreement with the ground measurements (differences lower than 1% in channel 13 emissivities and within ±0.3 °C in temperature for rice and sea pixels). [Copyright &y& Elsevier]

The S-NPP VIIRS Land Surface Temperature (LST) product attained the stage V1 of validation maturity (provisional validated) at the end of 2014. This paper evaluates the current VIIRS V1 LST product versus concurrent ground data acquired at a rice paddy site from December 2014 to August 2016. The experimental site has three different seasonal and homogeneous land covers through the year, which makes the site interesting for validation activities. An autonomous and multiangular system was used to record continuous ground data at the site. The data acquired at zenith angles similar to the VIIRS viewing angles were used for the validation to avoid possible differences between satellite and ground views due to angular dependences of the LSTs. Concurrently to surface data, downwelling sky radiances were measured at different incidence angles by the system, which were used to improve the cloud screening of the validation dataset, since cloud leakage was identified in previous validation studies as an important issue for further improvement. The validation results show good performance for the VIIRS V1 LST product at zenith angles ≤40°, with systematic uncertainties within ±0.5 K and accuracies around 1.2 K. These values are within the threshold requirements established for the VIIRS LST product, and they are better than the validation results published previously for the beta version of the product or using VIIRS data reprocessed with the calibrated algorithm coefficients implemented from April 2014. As the VIIRS LST algorithm has regression coefficients dependent on land cover type, the impact of land cover misclassifications on VIIRS LST data accuracy was also evaluated. It was expected that changing the surface type assigned by the VIIRS product to more appropriate types at the site pixels should improve the validation results. However, the improvement was limited, likely due to the reduced range of variability of the emissivities considered for the different land cover types in the regression process of the VIIRS LST algorithm coefficients. The results reveal the difficulties and uncertainties involved in the LST retrieval when using a LST algorithm with surface type dependent coefficients. [ABSTRACT FROM AUTHOR]

This paper proposes an angular and emissivity-dependent split-window equation that permits the determination of the sea surface temperature (SST) to a reasonable level of accuracy for any observation angle, including large viewing angles at the image edges of satellite sensors with wide swaths. This is the case of the MODIS radiometer both on EOS Terra/Aqua platforms, with observation angles of up to 65° at the surface, for which the split-window equation has been developed in this study. The algorithm takes into account the angular dependence of both the atmospheric correction (due to the increase of the atmospheric optical path with angle) and the emissivity correction (since sea surface emissivity (SSE) decreases with observation angle). Angular-dependent coefficients have been estimated for the atmospheric terms, and also an explicit dependence on the SSE has been included in the algorithm, as this parameter has values different to a blackbody surface for off-nadir angles, the SSEs also being dependent on surface wind speed. The proposed algorithm requires as input data at-sensor brightness temperatures for the split-window bands (31 and 32 of MODIS), the observation angle at each pixel, an estimate of the water vapor content (which is provided by the MODIS MOD07/MYD07 products) and accurate SSE values for both channels. The preliminary results show a good agreement between SSTs estimated by the proposed equation for off-nadir viewings of MODIS-Terra images and in situ SST measurements, with a root-mean square error (RMSE) of about ±0.3 K, for which the MODIS SST product gives an RMSE larger than ±0.7 K. [Copyright &y& Elsevier]