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An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation

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Thermal sensors onboard Landsat satellites have been underutilized due to the lack of consistent and accurate methodologies for retrieving the land surface temperature (LST) at global scales over all land cover types. We present an operational algorithm for generating Landsat LST consistently for all sensors that will be implemented by the United States Geological Survey/The National Aeronautics and Space Administration and made available at the Land Processes Distributed Active Archive Center. The LST algorithm involves three steps. The observed thermal radiance is atmospherically corrected using a radiative transfer model and reanalysis data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Data Set version 3 is spectrally adjusted and then modified to account for vegetation phenology and snow cover using Landsat visible-shortwave infrared data. The LST is retrieved by inverting the atmospherically and emissivity corrected Landsat radiances with a lookup-table approach. Landsat-derived emissivities were validated at two pseudoinvariant sand dune sites within an average absolute error of 0.54% when compared with laboratory measurements. The Landsat LST retrievals were validated with in situ observations from four surface radiation budget network (SURFRAD) sites, and two inland water bodies (Salton Sea and Lake Tahoe) in the USA. The LST retrievals for Landsat 5 and 7 had a mean bias (root mean square error) of 0.7 K (2.2 K) and 0.9 K (2.3 K) for the SURFRAD sites, and -0.3 K (0.6 K) and 0.4 K (0.7 K) for the inland water bodies, respectively. The operational algorithm will provide a consistent LST record from four decades of historical Landsat thermal data enabling the long-term monitoring of temperature and trends, land cover and land use changes, and improved utilization in models.
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... Accordingly, different LST retrieval methods have been proposed depending on the availability of LSE and atmospheric information. The methods using a priori LSE include single-band methods that require single-band LSE and atmospheric information (Sobrino et al., 2004;Malakar et al., 2018) and multi-band methods that require multi-band LSEs (e.g., Wan and Dozier, 1996;Sòria and Sobrino, 2007;Sun and Pinker, 2007). Methods without a priori LSE include the temperature and emissivity separation (TES) method (Gillespie et al., 1998;Hulley and Hook, 2011;Islam et al., 2017) and the two-temperature (TTM) method (Peres and DaCamara, 2004), which simultaneously retrieve LST and LSE using known atmospheric information. ...
... For the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat LSTs, the uncertainty in using a linear SW algorithm and the single-band method, respectively, has also been quantified based on error propagation (Jimenez-Munoz et al., 2014;Laraby and Schott, 2018;Ghent et al., 2019). In addition, their error trends under various environments have been validated extensively using in-situ measurements (Wang et al., 2008;Tan et al., 2017;Malakar et al., 2018;Duan et al., 2021;Li et al., 2014;Wan, 2014;Duan et al., 2018;Li et al., 2021). The purpose of this study was to clarify the uncertainties of AHI LSTs in operational retrieval. ...
... x.org.au/) were used to obtain in-situ LST. Longwave radiation was provided every 30 min, and the in-situ LST was obtained according to Eq. (1) based on thermal radiative transfer theory (e.g., Wang et al., 2008;Malakar et al., 2018;Duan et al., 2021): ...
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Himawari-8, a new-generation geostationary satellite, can retrieve sub-hourly land surface temperatures (LSTs) with moderate spatial resolution, providing a new scale for monitoring the thermal environment in Asia and Oceania. This study evaluated uncertainties of LSTs retrieved by three operational algorithms from Advanced Himawari Imager (AHI) data. We compared two nonlinear split-window algorithms (SOB and WAN algorithms) and one nonlinear three-band algorithm (YAM algorithm). First, the error characteristics of the retrieved LSTs caused by the input parameter errors were simulated under various land-atmospheric conditions using an atmospheric radiative transfer model. Thereafter, retrieved LSTs from actual AHI data were evaluated using in-situ observations from AsiaFlux and OzFlux networks and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LSTs. The simulated results showed that the YAM algorithm maintained the highest accuracy, whereas the WAN algorithm had the highest robustness to input errors. The YAM algorithm had the smallest total error including input errors over a wide range of retrieval conditions. Validation of the three algorithms via in-situ LSTs from 12 sites revealed nighttime mean RMSEs for all sites of ~1.7 • C, and daytime mean RMSEs for semi-arid and humid sites of approximately 3.0 • C and 2.0 • C, respectively. These are comparable to the accuracies reported for LST products with higher spatial resolutions, such as the Moderate Resolution Imaging Spectroradiometer and Landsat. Within the Himawari-8 disk, the estimation error of the YAM algorithm was ~1.0 • C lower than those of the SOB and WAN algorithms in regions with extremely high viewing angle, temperature, and humidity (e.g., northern China, Australia, and Southeast Asia). Furthermore, AHI LSTs showed closer agreement with ECOSTRESS compared to in-situ LSTs, suggesting the usefulness of ECOSTRESS for assessing the diurnal LSTs derived from geostationary satellites. The resulting LST products and the knowledge of their error characteristics have the potential to improve the collective understanding of terrestrial energy and water cycles based on improved accuracy and robustness.
... Tavares et al. [15] recommends the use of thermal data from the Landsat 7 ETM+ sensor for the observation of small lakes because at a relatively high spatial resolution the temperature estimation error is very close to the error obtained from the MODIS sensor (RMSE 1.07 and 1.05 • C, respectively). The usefulness of other medium-resolution thermal satellite sensors for water surface temperature assessment, such as Landsat TM, ETM+, TIRS [16][17][18][19][20] and Terra ASTER [21][22][23], was also investigated. Lake temperature is primarily determined by meteorological factors (insolation, cloudiness, air temperature, and wind speed), and to a lower degree by geomorphometric factors (surface area and depth) [24]. ...
... The product is available in a resolution of 30 m as georeferenced rasters (.tif format) in the Universal Transverse Mercator coordinate system. Information on the applied techniques related to atmospheric compensation, calibration methodology, and validation are widely presented and discussed by Cook [47], Cook et al. [48], Malakar et al. [18], Schaeffer et al. [49]. In the case of LST-L2, the data availability differed for all analyzed lakes due to cloud cover pattern in Poland. ...
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Changes in lake water temperature, observed with the greatest intensity during the last two decades, may significantly affect the functioning of these unique ecosystems. Currently, in situ studies in Poland are conducted only for 38 lakes using the single-point method. The aim of this study was to develop a method for remote sensing monitoring of lake water temperature in a spatio-temporal context based on Landsat 8 imagery. For this purpose, using data obtained for 28 lakes from the period 2013–2020, linear regression (LM) and random forest (RF) models were developed to estimate surface water temperature. In addition, analysis of Landsat Level-2 Surface Temperature Science Product (LST-L2) data provided by United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) was performed. The remaining 10 lakes not previously used in the model development stage were used to validate model performance. The results showed that the most accurate estimation is possible using the RF method for which RMSE = 1.83 °C and R2 = 0.89, while RMSE = 3.68 °C and R2 = 0.8 for the LST-L2 method. We found that LST-L2 contains a systematic error in the coastal zone, which can be corrected and eventually improve the quality of estimation. The satellite-based method makes it possible to determine water temperature for all lakes in Poland at different times and to understand the influence of climatic factors affecting temperature at the regional scale. On the other hand, spatial presentation of thermics within individual lakes enables understanding the influence of local factors and morphometric conditions.
... In recognition of the need for systematically generated atmospherically corrected Landsat imagery, the USGS started to provide TOA and surface reflectance Landsat Analysis Ready Data (ARD) (Dwyer et al., 2018). Now Landsat surface reflectance and surface temperature are generated systematically on a global basis using a combined radiative transfer and image content approach (Malakar et al., 2018;Vermote et al., 2015) for Collection 2 for all of the Landsat 30-m image data in the archive. Similarly, provisional aquatic reflectance products from Landsat-8 observations are also available on-demand via USGS Earth Resources Observation and Science (EROS) Science Processing Architecture (ESPA) (Franz et al., 2015;Pahlevan et al., 2017). ...
... An additional enhancement with Collection 2 is the ability to convert well-calibrated TOA brightness temperatures from single and dual thermal infrared spectral bands on Landsats 4-9 to generate atmospherically corrected surface temperature products (Cook et al., 2014;Masek et al., 2020). This algorithmic advancement leveraged decades of Landsat thermal infrared research and development regarding both radiative transfer Malakar et al., 2018) and measurement-modeling split-window techniques (Gerace et al., 2020) to compensate for atmospheric effects and incorporate surface emissivity measures available through the ASTER Global Emissivity Database (GED) (Hulley et al., 2015). ...
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Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application , and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and pro-grammatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change 2 drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.
... Evidence has suggested that deviations in emissivity would correlate with errors in temperature retrieval (Chen et al., 2016;Malakar et al., 2018). As aforesaid, the emissivity of different points of measurements might differ due to their distinctive characteristics. ...
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The estimation of land surface energy budgets and land surface temperature (LST) require accurate information on land surface emissivity (LSE). Despite the use of remote sensing or low altitude sensing data in numerous models for the computation of LSE, it is still highly challenging to precisely predict the LSE of high-heterogeneous surfaces on a micro-scale. This paper thus proposes several individual models to retrieve each surface's emissivity on the pixel level. Various emissivity and reflectance values were obtained upon spectral resampling to found individual models between emissivity and spectral indices (NDVI, DVI, MSAVI, OSAVI, RRI) for classified surfaces. Based on the smallest root mean square error (RMSE), DVI models accurately indicated the emissivity of concrete floors (0.0012), asphalt roads (0.0012), grasses (0.0062), parking lots (0.0035), pavements (0.0057), and red manhole covers (0.0089). RRI models better predicted the emissivity of tiles (0.0039), grey manhole covers (0.0092), and shrubs (0.0005). OSAVI was also the ideal index for retrieving the emissivity of rooftops (0.0009). Validation results showed their effectiveness, with accuracy within 0.001. Finally, temperature errors caused by the deviation between traditional or default emissivity and measuring emissivity fluctuated from 0.0 K to 2.9 K or 0.2 K to 3.7 K, respectively.
... The spatial representativeness is about 70 m × 70 m [28]. Observations from the SURFRAD stations have been widely used for evaluating satellite-based estimates of surface radiation, for validating hydrology, weather prediction, climate models, and satellite LST products from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), GOES, MODIS, and VI-IRS [15,16,[28][29][30][31]. The SURFRAD consists of seven stations, from which the Goodwin Creek (GWN) site was removed by the LST validation, due to the onsite thermal heterogeneity which caused the ground LST to be lower than the satellite LST in the daytime, while it was higher in the nighttime [16]. ...
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