(Tomlinson et al., 2011), satellite-derived LST products used for long-term Global LST trend investigation (Jin, 2004 Jin and Dickinson, 2002 Yan etĪccording to the satellite onboard duration and spatiotemporal resolution Monitoring (Karnieli et al., 2010 Mildrexler et al., 2017), vegetationĬhange analysis (Julien and Sobrino, 2009 Julien et al., 2006 Still etĪl., 2019), permafrost thawing monitoring (Westermann et al., 2011), and Simulation (Alcântara et al., 2010 Anderson et al., 2007), drought The archived long-term satellite-derived LSTĭatasets have been widely used in various fields such as land cover changeĭetection (Lambin and Ehrlich, 1997 Muro et al., 2018), radiation flux Thermal remote sensing provides the only way to obtain long-term and regular Land–atmosphere interaction (Jin and Dickinson, 2010). Land surface temperature (LST) is one of the most important variables of Generated GADTC products should be valuable in various applications such as We consider the IADTC framework can guide theįurther optimization of T dm estimation across the globe, and the Trend do occur – the pixel-based MAE in LST trend between these two Although the global-mean LST trend (2003 to 2019)Ĭalculated with the traditional method and the IADTC framework is relativelyĬlose (both between 0.025 to 0.029 K yr −1), regional discrepancies in LST ![]() In low-latitude and midlatitude regions while of a relatively small value in Method yields a positive systematic Δ T sb of greater than 2.0 K By taking the GADTC products as references, furtherĪnalysis reveals that the T dm estimated with the traditional averaging Respectively, and the mean biases are −1.6 and −1.5 K for these twoĭatasets, respectively. That the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, Validated only with in situ data, the assessments show that the mean absoluteĮrrors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD andįLUXNET data, respectively, and the mean biases are both close to zero.ĭirect comparisons between the GADTC products and in situ measurements indicate The validations show that the IADTCįramework reduces the systematic Δ T sb significantly. Generate global spatiotemporally seamless T dm products ranging from 2003 Several methods haveīeen proposed for the estimation of the T dm, yet they are becoming lessĬapable of generating spatiotemporally seamless T dm across the globe.īased on MODIS and reanalysis data, here we propose an improved annual andĭiurnal temperature cycle-based framework (termed the IADTC framework) to ( T dm) estimated with the traditional method, which uses the averages ofĬlear-sky LST observations directly as the T dm. Systematic sampling bias ( Δ T sb) on the daily mean LST ![]() Times per day under cloud-free conditions. Polar orbiters can only sample the surface effectively with very limited ![]() Polar orbiters are crucial for various applications such as global and Daily mean land surface temperatures (LSTs) acquired from
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