Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE

Impact of High Concentrations of Saharan Dust Aerosols on Infrared-Based Land Surface Temperature Products

An analysis of three operational satellite-based thermal-infrared land surface temperature (LST) products is presented for conditions of heavy dust aerosol loading. The LST products are compared against ERA5’s skin temperature (SKT) across the Sahara Desert and Sahel region, where high concentrations of dust aerosols are prevalent. Large anomalous differences are found between satellite LST and ERA5’s SKT during the periods of highest dust activity, and satellite–ERA5 differences are shown to be strongly related to dust aerosol optical depth (DuAOD) at 550 nm, indicating an underestimation of LST in conditions of heavy dust aerosol loading. In situ measurements from two ground stations in the Sahel region provide additional evidence of this underestimation, showing increased biases of satellite LST with DuAOD, and no significant dependence of ERA5’s SKT biases on dust aerosol concentrations. The impact of atmospheric water vapor content on LST and SKT is also examined, but dust aerosols are shown to be the primary driver of the inaccurate LSTs observed. Based on comparisons with in situ data, we estimate an aerosol-induced underestimation of LST of approximately 0.9 K for every 0.1 increase in DuAOD. Analysis of brightness temperatures (BTs) in the thermal infrared atmospheric window reveals that dust aerosols have the opposite effect on BT differences compared to water vapor, leading to an underestimation of atmospheric correction by the LST retrieval algorithms. This article highlights a shortcoming of current operational LST retrieval algorithms that must be addressed

A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms

Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo

A Methodology to Simulate LST Directional Effects Based on Parametric Models and Landscape Properties

The correction of directional effects on satellite-retrieved land surface temperature (LST) is of high relevance for a proper interpretation of spatial and temporal features contained in LST fields. This study presents a methodology to correct such directional effects in an operational setting. This methodology relies on parametric models, which are computationally efficient and require few input information, making them particularly appropriate for operational use. The models are calibrated with LST data collocated in time and space from MODIS (Aqua and Terra) and SEVIRI (Meteosat), for an area covering the entire SEVIRI disk and encompassing the full year of 2011. Past studies showed that such models are prone to overfitting, especially when there are discrepancies between the LSTs that are not related to the viewing geometry (e.g., emissivity, atmospheric correction). To reduce such effects, pixels with similar characteristics are first grouped by means of a cluster analysis. The models’ calibration is then performed on each one of the selected clusters. The derived coefficients reflect the expected impact of vegetation and topography on the anisotropy of LST. Furthermore, when tested with independent data, the calibrated models are shown to maintain the capability of representing the angular dependency of the differences between LST derived from polar-orbiter (MODIS) and geostationary (Meteosat, GOES and Himawari) satellites. The methodology presented here is currently being used to estimate the deviation of LST products with respect to what would be obtained for a reference view angle (e.g., nadir), therefore contributing to the harmonization of LST products

Quantifying the Clear-Sky Bias of Satellite Land Surface Temperature Using Microwave-Based Estimates

International audienceMost available long-term databases of land surface temperature (LST)derived from space-borne sensors rely on infrared observations and aretherefore restricted to clear-sky conditions. Hence, studies based onsuch data sets may not be representative of all-weather conditions andmay be considered as "biased" toward clear sky. An assessment of theimpact of this restriction is made using 3 years of LST derived frompassive microwave observations that are not affected by most clouds. Asystematic analysis in space and time is performed of the "clear-skybias," defined as the difference between average clear-sky and averageall-weather LSTs. The amplitude of the bias is closely related to thefraction of clear-sky days, and therefore, arid regions are associatedto very low values of bias whereas midlatitudes present the highestvalues. During daytime, the input of solar radiation for clear-skysituations leads to higher LST values, and therefore, the bias isgenerally positive (e.g., 2-8 K over the midlatitudes) whereas, duringnighttime, the bias is generally negative although with lower amplitude(around -2 K), because of the increased radiative cooling for clear-skysituations. The clear-, cloudy-, and all-sky LSTs are also compared withnear-surface air temperature. Although LST is generally higher than airtemperature, the contrast between the two may be strongly influenced bylocal weather conditions. Both the clear-sky bias and differencesbetween LST and air temperature are also analyzed at the local scaletaking into account the predominant cloud regime

Multisensor Thermal Infrared and Microwave Land Surface Temperature Algorithm Intercomparison

International audienceTo ensure optimal and consistent algorithm usage within climate studies utilizing satellite-derived Land Surface Temperature (LST) datasets, an algorithm intercomparison exercise was undertaken to assess the various operational and scientific LST retrieval algorithms in use. This study was focused on several LST products including single-sensor products for AATSR, Terra-MODIS, SEVIRI, SSM/I and SSMIS; a Climate Date Record (CDR), which is a combined dataset drawing from AATSR, SLSTR and MODIS; and finally a merged low Earth orbit/geostationary product using data from AATSR, MODIS and SEVIRI. Therefore, the analysis included 14 algorithms: seven thermal infrared algorithms and seven microwave algorithms. The thermal infrared algorithms include five split-window coefficient-based algorithms, one optimal estimation algorithm and one single-channel inversion algorithm, with the microwave focusing on linear regression and neural network methods. The algorithm intercomparison assessed the performance of the retrieval algorithms for all sensors using a benchmark database. This approach was chosen due to the lack of sufficient in situ validation sites globally and the bias this limited set engendered on the training of particular algorithms. A simulated approach has the ability to test all parameters in a consistent, fair manner at a global scale. The benchmark database was constructed from European Centre for Medium-Range Weather Forecasts Re-analysis 5 (ERA5) atmospheric data, Combined ASTER and MODIS Emissivity for Land (CAMEL) infrared emissivity data, and Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) emissivity data for the period of 2013–2015. The best-performing algorithms had biases of under 0.2 K and standard deviations of approximately 0.7 K. These results were consistent across multiple sensors. Areas of improvement, such as coefficient banding, were found for all algorithms as well as lines for further inquiry that could improve the global and regional performance

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which oers the potential to obtain a global, nearly gap-free LST product

Multisensor Thermal Infrared and Microwave Land Surface Temperature Algorithm Intercomparison

To ensure optimal and consistent algorithm usage within climate studies utilizing satellite-derived Land Surface Temperature (LST) datasets, an algorithm intercomparison exercise was undertaken to assess the various operational and scientific LST retrieval algorithms in use. This study was focused on several LST products including single-sensor products for AATSR, Terra-MODIS, SEVIRI, SSM/I and SSMIS; a Climate Date Record (CDR), which is a combined dataset drawing from AATSR, SLSTR and MODIS; and finally a merged low Earth orbit/geostationary product using data from AATSR, MODIS and SEVIRI. Therefore, the analysis included 14 algorithms: seven thermal infrared algorithms and seven microwave algorithms. The thermal infrared algorithms include five split-window coefficient-based algorithms, one optimal estimation algorithm and one single-channel inversion algorithm, with the microwave focusing on linear regression and neural network methods. The algorithm intercomparison assessed the performance of the retrieval algorithms for all sensors using a benchmark database. This approach was chosen due to the lack of sufficient in situ validation sites globally and the bias this limited set engendered on the training of particular algorithms. A simulated approach has the ability to test all parameters in a consistent, fair manner at a global scale. The benchmark database was constructed from European Centre for Medium-Range Weather Forecasts Re-analysis 5 (ERA5) atmospheric data, Combined ASTER and MODIS Emissivity for Land (CAMEL) infrared emissivity data, and Tool to Estimate Land Surface Emissivities at Microwave frequencies (TELSEM) emissivity data for the period of 2013–2015. The best-performing algorithms had biases of under 0.2 K and standard deviations of approximately 0.7 K. These results were consistent across multiple sensors. Areas of improvement, such as coefficient banding, were found for all algorithms as well as lines for further inquiry that could improve the global and regional performance