Thermal sharpening expands the application of land surface temperature due to the trade-off between spatial and temporal resolutions. Fixed kernels (FK) are widely applied in two window strategies: local window strategies (LWS) and object-based window strategies (OWS). However, the fixed regression kernel effect on LWS and OWS has rarely been considered in existing studies. Due to the heterogeneity among different windows, it is important to select suitable kernels for each window either in OWS or LWS. This article presents an auto-adjusted kernel (AAK) method to resolve this issue and examines certain simple kernel selection rules in thermal sharpening aiming to balance accuracy and efficiency. We test the AAK method with Landsat 8 data and compare it to the FK method with both OWS and LWS. The results reveal that the AAK method generally performs better than the FK method. Compared to the FK method, the AAK method improves the OWS accuracy by 0.283 K on average at three downscaling ratios, and the accuracy improvement increases with increasing downscaling ratio (from 3 to 9). Especially when the downscaling ratio reaches to 9, there is an evident improvement with 0.425 K of AAK. Moreover, the AAK method enhances the mean LWS accuracy by 0.179 K overall and decreases the difference between OWS and LWS. Furthermore, the AAK method increases the accuracy in specific areas and reduces extreme-value points. These findings indicate the potential of the AAK method in thermal sharpening with OWS and LWS, which resolves kernel selection problems.

中文翻译：

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利用局部和基于对象的窗口策略进行热锐化的自动调整内核方法

由于空间和时间分辨率之间的权衡，热锐化扩展了地表温度的应用。固定内核（FK）广泛应用于两种窗口策略：本地窗口策略（LWS）和基于对象的窗口策略（OWS）。但是，现有研究很少考虑固定回归核对LWS和OWS的影响。由于不同窗口之间的异构性，在OWS或LWS中为每个窗口选择合适的内核非常重要。本文提出了一种自动调整内核（AAK）方法来解决此问题，并研究了一些在热锐化中简单的内核选择规则，旨在平衡精度和效率。我们使用Landsat 8数据测试AAK方法，并将其与OWS和LWS的FK方法进行比较。结果表明，AAK方法通常比FK方法具有更好的性能。与FK方法相比，AAK方法在三种缩小比例下平均将OWS精度提高了0.283 K，并且随着缩小比例的增加（从3到9），精度提高也随之增加。特别是当缩小比例达到9时，AAK为0.425 K时有明显的改善。此外，AAK方法总体上将LWS的平均精度提高了0.179 K，并减少了OWS和LWS之间的差异。此外，AAK方法可提高特定区域的精度并减少极值点。这些发现表明AAK方法在OWS和LWS热锐化中的潜力，它可以解决内核选择问题。AAK方法在三个缩小比例下平均将OWS精度提高了0.283 K，并且随着缩小比例的增加（从3到9），精度提高也随之增加。特别是当缩小比例达到9时，AAK为0.425 K时有明显的改善。此外，AAK方法总体上将LWS的平均精度提高了0.179 K，并减少了OWS和LWS之间的差异。此外，AAK方法可提高特定区域的精度并减少极值点。这些发现表明AAK方法在OWS和LWS热锐化中的潜力，它可以解决内核选择问题。AAK方法在三个缩小比例下平均将OWS精度提高了0.283 K，并且随着缩小比例的增加（从3到9），精度提高也随之增加。特别是当缩小比例达到9时，AAK为0.425 K时有明显的改善。此外，AAK方法总体上将LWS的平均精度提高了0.179 K，并减少了OWS和LWS之间的差异。此外，AAK方法可提高特定区域的精度并减少极值点。这些发现表明AAK方法在OWS和LWS热锐化中的潜力，它可以解决内核选择问题。0.425 K的AAK有明显的改善。此外，AAK方法总体上将LWS的平均精度提高了0.179 K，并减少了OWS和LWS之间的差异。此外，AAK方法可提高特定区域的精度并减少极值点。这些发现表明AAK方法在OWS和LWS热锐化中的潜力，它可以解决内核选择问题。0.425 K的AAK有明显的改善。此外，AAK方法总体上将LWS的平均精度提高了0.179 K，并减少了OWS和LWS之间的差异。此外，AAK方法可提高特定区域的精度并减少极值点。这些发现表明AAK方法在OWS和LWS热锐化中的潜力，它可以解决内核选择问题。