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.

Validation of satellite land surface temperature (LST) products is usually performed through direct comparison with ground-measured LSTs (Temperature-based or T-b validation) and with the Radiance-based (R-b) method where reference ground LSTs are obtained from at-sensor radiances, atmospheric temperature and water vapor profiles, and emissivity measurements. While T-b is the preferred method, it is only applicable to a few, single-component covers that are thermally homogeneous from the ground measurement scale (few m) to the satellite scale (usually 1 km). The indirect R-b method is an alternative to extend the LST validation at global scale over cover types where the T-b method is not feasible. In this study, we used ground LST measurements taken in a thermally homogeneous site coincident with MODIS Terra and Aqua overpasses (86 matchups) to assess the accuracy of the R-b LSTs derived from MODIS data. Mean bias (ground minus R-b LSTs) of -0.1 K and root mean square error (RMSE) of 0.8 K were obtained, showing the good performance against ground measurements. Then, we apply operationally the R-b method for validating MODIS Level 2 LST products M*D11 and M*D21 over six test sites comprising a varied range of surfaces (4267 cases). The emissivity values necessary for the R-b calculations were obtained from (1) the M*D11 and M*D21 emissivity product, and (2) an independently modelled emissivity using ground measurements and vegetation cover fraction estimates. For the M*D11 product, we obtained an overall bias (product minus R-b LST) of -0.5 K and RMSE of 0.9 K with either the product or the modelled emissivities. However, a significant difference was found between daytime and nighttime bias (-1.0 K and 0.0 K respectively). The daytime cold bias of M*D11 was attributed to an ill tuning of the algorithm for high surface temperature and atmospheric humidity conditions. For the M*D21 product, the overall bias (RMSE) was 0.2 K (0.6 K) with the product emissivity, and 0.6 K (1.1 K) with the modelled emissivity, both in nighttime and daytime. These results compare well with recent studies and contribute to the global assessment of MODIS LST uncertainty.

Land surface temperature (LST) is a fundamental physical quantity in a range of different studies, for example in climatological analyses and surface–atmosphere heat flux assessments, especially in heterogeneous and complex surfaces such as vegetated canopies. To obtain accurate LST values, it is important to measure accurately the land surface emissivity (LSE) in the thermal infrared spectrum. In the past decades, different directional emissivity canopy models have been proposed. This paper evaluates six radiative transfer models (FR97, Mod3, Rmod3, 4SAIL, REN15, and CE-P models) through a comparison with in situ emissivity measurements performed using the temperature-emissivity separation (TES) method. The evaluation is done using a single set of rose plants over two different soils with very different spectral behavior. First, using an organic soil, the measurements were done for seven different observation angles, from 0° to 60° in steps of 10°, and for six different values of leaf area index (LAI). Taking into account all LAIs, the bias (and root mean square error, RMSE) obtained were 0.003 (±0.006), −0.004 (±0.005), −0.009 (±0.011), 0.005 (±0.007), 0.004 (±0.007), and 0.005 (±0.007) for FR97, Mod3, Rmod3, 4SAIL, REN 15, and CE-P models, respectively. Second, using an inorganic soil, the measurements were done for six different LAIs but for two different observation angles: 0° and 55°. The bias (and RMSE) obtained were 0.012 (±0.014), 0.004 (±0.007), −0.020 (±0.035), 0.016 (±0.017), 0.013 (±0.015), 0.013 (±0.015) and for FR97, Mod3, Rmod3, 4SAIL, REN15, and CE-P models, respectively. Overall, the Mod3 model appears as the best model in comparison to the TES emissivity reference measurements.

Current land surface temperature (LST) products, estimated by sensors on board satellites, show a trade-off between their spatial and temporal resolution. If the spatial resolution is high (i.e., around 100 m), the LST product is delivered every 2 weeks, and for those LST products estimated daily, its spatial resolution is 1 km. Current spatial and temporal resolutions are not adequate for disciplines such as high-precision agriculture, urban decision making, and planning how to mitigate the overheating of cities, for which LST maps at 50–100 m resolution every few days are desirable. This situation has led to the development of disaggregation techniques in order to enhance the spatial resolution of daily LST products. Unfortunately, disaggregation techniques are usually complex since they rely on a number of external inputs and computer resources and are difficult to apply in practice. To our knowledge, there are only two operative downscaled 10 m LST products available to the end user, which are implemented in the Google Earth Engine (GEE) tool. They are the Daily Ten-ST-GEE and LST-downscaling-GEE systems. This study provides a critical benchmark by performing the first direct intercomparison and rigorous in situ validation of these two operative GEE systems. The validation, conducted with reference temperature data from dedicated field campaigns over contrasting agricultural sites in Spain, showed a good correlation of both methods with a R² of 0.74 for Daily Ten-ST-GEE and 0.94 for LST-downscaling-GEE, but the poor results of the first method in a highly heterogeneous site (RMSE of 5.8 K) make the second method the most suitable (RMSE of 3.6 K) for obtaining high-spatiotemporal-resolution LST maps.

1. The World Meteorological Organization (WMO) and the Global Climate Observing System (GCOS 2022) consider land surface temperature (LST) as an essential climate variable (ECV) for Earth monitorin...