Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data

Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA\u27s Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products

Supplement of the radiance-based method to validate satellite-derived land surface temperature products over heterogeneous land surfaces

Land surface temperature (LST) retrieved from satellite remote sensing data has become a key parameter in research on global environmental change; therefore, the acquisition of accurate satellite-derived LST information is crucial for the diagnosis and analysis of global change. However, it is relatively difficult to obtain the true value of a pixel due to the scale mismatch between in situ measurements and satellite-based observations, especially for commonly heterogeneous and nonisothermal land areas, which greatly increases the difficulty in estimating pixel-representative LST values from in situ measurements for validation of satellite-based LST products. In this study, a supplemented radiance-based (SR-based) validation method was developed to evaluate the latest moderate resolution imaging spectroradiometer (MODIS) Collection 6 Level 2 daily LST/land surface emissivity (LSE) products over a heterogeneous and nonisothermal region of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, West China. In the SR-based framework, pixel-representative LST values are simulated by the MODTRAN model from the corresponding in situ measurements, such as LSE and atmospheric profile measurements, to evaluate the MODIS LST products. The validation results show that the MODIS daytime LST products from the Aqua satellite (MYD11_L2) have a greater accuracy than those from the Terra satellite (MOD11_L2). Analyses of the effect factors indicate a strong correlation between the errors in the MOD11_L2 LST product and the corresponding difference in the MODIS brightness temperature between bands 31 and 32. Although the requirement of synchronous or quasisynchronous in situ measurements for the validated LST products may limit the applicability of the SR-based method, it is still an effective and simple method for validating satellite-derived LST products over mixed pixels. Our method is an indispensable supplement for the validation methods of satellite-derived LST products, and it can be applied in West China and other areas with heterogeneous land surfaces

Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes

Validation of Collection 6 MODIS land surface temperature product using in situ measurements

Land surface temperature (LST) is an important physical quantity at the land-atmosphere interface. Since 2016 the Collection 6 (C6) MODIS LST product is publicly available, which includes three refinements over bare soil surfaces compared to the Collection 5 (C5) MODIS LST product. To encourage the use of the C6 MODIS LST product in a wide range of applications, it is necessary to evaluate the accuracy of the C6 MODIS LST product. In this study, we validated the C6 MODIS LST product using temperature-based method over various land cover types, including grasslands, croplands, cropland/natural vegetation mosaic, open shrublands, woody savannas, and barren/sparsely vegetated. In situ measurements were collected from various sites under different atmospheric and surface conditions, including seven SURFRAD sites (BND, TBL, DRA, FPK, GCM, PSU, and SXF) in the United States, three KIT sites (EVO, KAL, and GBB) in Portugal and Namibia, and three HiWATER sites (GBZ, HZZ, and HMZ) in China. The spatial representativeness of the in situ measurements at each site was separately evaluated during daytime and nighttime using all available clear-sky ASTER LST products at 90m spatial resolution. Only six sites during daytime are selected as sufficiently homogeneous sites despite the usually high spatial thermal heterogeneity, whereas during nighttime most sites can be considered to be thermally homogeneous and have similar LST and air temperature. The C6 MODIS LST product was validated using in situ measurements from the selected homogeneous sites during daytime and nighttime: except for the GBB site, large RMSE values (> 2 K) were obtained during daytime. However, if only satellite LST with a high spatial thermal homogeneity on the MODIS pixel scale are used for LST validation, the best daytime accuracy (RMSE<1.3 K) for the C6 MODIS LST product is achieved over the BND and DRA sites. Except for the DRA site, the RMSE values during nighttime are<2 K at the selected homogeneous sites. Furthermore, the accuracy of the C6 MODIS LST product was compared with that of the C5 MODIS LST product during nighttime at the selected homogeneous sites. Except for the GBB site, there are only small differences (< 0.4 K) between the RMSE values for the C5 and C6 MODIS LST products

Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming

Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed

Creating new near-surface air temperature datasets to understand elevation-dependent warming in the Tibetan Plateau

© 2020 by the authors. The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002-present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9-0.95 and 1-2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000-3000 m and 4000-5000 m, whereas the elevation interval at 6000-7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming

Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed