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Fine spatial resolution (i.e., ≤ 100 m) land surface temperature (LST) data are crucial to study heterogeneous landscapes (e.g., agricultural and urban). Some well-known spatiotemporal fusion methods like the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), which were originally developed to fuse surface reflectance data, may not be suitable for direct application in LST studies due to the high sub-diurnal dynamics of LST. Furthermore, the effectiveness of spatiotemporal fusion methods for LST data has not been thoroughly evaluated in previous studies that only focused on relatively small spatiotemporal extents. To address these limitations, we proposed a variant of ESTARFM, referred to as the unbiased ESTARFM (ubESTARFM), specifically designed to accommodate the high temporal dynamics of LST to generate fine-resolution LST estimates. We evaluated ubESTARFM and ESTARFM against in-situ LST and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST across 12 regions throughout Australia, encompassing various land covers and environments. Independent validation showed that ubESTARFM had a bias of 2.55 K, unbiased root mean squared error (ubRMSE) of 2.57 K, and Pearson correlation coefficient (R) of 0.95 against the in-situ LST over 11,290 observations at the 12 sites, all of which were considerably better than those calculated for ESTARFM, being a bias of 4.73 K, ubRMSE of 3.80 K and R of 0.92. When compared to ECOSTRESS data, ubESTARFM LST had a bias of −1.69 K, ubRMSE of 2.00 K, and R of 0.70 over 43 near clear-sky scenes, while ESTARFM LST had a bias of 1.79 K, ubRMSE of 2.68 K, and R of 0.59. Overall, our results demonstrated that ubESTARFM can avoid systematic bias accumulation, substantially reduce uncertainty deviation, and maintain a good level of correlation with validation datasets when compared to ESTARFM. A further assessment underscored the potential of ubESTARFM for application using LST data acquired from geostationary platforms (e.g., Himawari-8), with a mean ubRMSE (R) of 2.22 K (0.97) against in-situ LST over 1327 observations at 3 sites from southeast Australia at the overpass time of MODIS/Terra. This promising method leverages reliable numeric values from coarse-resolution LST while borrowing spatial heterogeneity from fine-resolution LST and has the potential to be coupled with energy balance and/or radiative transfer models thus enabling better farm and/or regional-scale water management strategies to be implemented. Furthermore, both the input and generated LST data, encompassing a comprehensive spatial extent over diverse land covers and climatic conditions, are publicly available for benchmarking future algorithmic refinements. • We developed an unbiased variant of ESTARFM (ubESTARFM) to fuse MODIS and Landsat LST. • ubESTARFM uses coarse LST as reference to locally correct systematic bias of fine LST. • We evaluated ubESTARFM against in-situ LST and ECOSTRESS LST across Australia. • ubESTARFM reduces deviation of uncertainty while it maintains spatial details. • ubESTARFM shows potential for application on geostationary LST (e.g., Himawari-8). [ABSTRACT FROM AUTHOR]

Abstract: In this study the effects of exposure to two air temperatures (15 and 27.5°C) and three graders commonly used in South Australia (Rotary, Flat Bed and Inside/Out) on Pacific oysters were investigated using post-exposure or post-grading catecholamine levels as a stress indicator. During the 48h experimental period the circulating dopamine and noradrenaline levels of oysters exposed to 15°C air temperature did not differ significantly from controls sampled directly from the 15°C water tank. Conversely, in oysters exposed to 27.5°C air temperature, dopamine and noradrenaline became significantly different from controls after 8 and 12h air exposure, respectively, and then returned to the control levels at 24h. Significant increases (P <0.05) in circulating noradrenaline and dopamine levels were observed in oysters graded by the Flat Bed and the Inside/Out graders. The circulating noradrenaline concentrations in oysters subjected to the Flat Bed grader were significantly higher (P <0.05) than those observed in the Rotary graded oysters and close to being significantly higher (P =0.052) than those levels measured in the Inside/Out graded oysters. The circulating noradrenaline levels in oysters subjected to the Inside/Out grader were, in turn, significantly higher than those recorded in the Rotary graded oysters. These results suggest that in relation to the three graders used in South Australia, the Flat Bed induces the highest stress levels in oysters and the Rotary grader the lowest. [Copyright &y& Elsevier]