The development of ground-based sampling strategies is vital for validating medium or coarse-resolution satellite-derived land surface temperature (LST) products. Conventional LST sampling at the satellite pixel scale has been limited to the homogeneous surfaces. In this study, an optimal sampling strategy called spatial and diurnal temperature cycle-constrained sampling (SDCS) is proposed to extend the feasibility of LST validation over heterogeneous surfaces with dramatic diurnal LST changes. SDCS integrates a priori information including land cover, diurnal LSTs data, and spatial distribution characteristics of samples to improve the representativeness of multi-temporal measurements over heterogeneous surfaces. SDCS was applied to four varied study areas using simulated satellite data and was compared with four existing methods including random sampling, systematic sampling, land cover-based stratified sampling, and conditioned Latin hypercube (CLH) sampling. Results showed that SDCS could significantly improve the representativeness of samples with a limited sample size. In homogeneous surfaces with an LST standard deviation (SD) of less than 2 K, the root mean square error (RMSE) of diurnal LSTs estimated by SDCS was less than 0.3 K when using 0.20% of the total pixels. In moderate heterogeneous surfaces (LST SD less than 5 K), 0.32% of the total pixels were required to achieve RMSE less than 0.5 K. In extremely heterogeneous surfaces (LST SD > 6 K), 0.96% of the pixels were needed to achieve the same accuracy. Further, the representativeness of the samples selected by SDCS was stable in diurnal space with uncertainties of LST bias less than 0.27 K. Moreover, the samples exhibited a dispersed spatial distribution with a nearest neighbor index of 1.27–1.54. SDCS can generalize to various regions and dates and can be employed in field campaigns for diurnal LST validation over heterogeneous surfaces.

地面采样策略的发展对于验证中分辨率或粗分辨率卫星衍生的地表温度（LST）产品至关重要。卫星像素级的常规LST采样仅限于均匀表面。在这项研究中，提出了一种称为空间和昼夜温度循环受限采样（SDCS）的最佳采样策略，以将LST验证的可行性扩展到昼夜LST急剧变化的异质表面上。SDCS整合了先验信息，包括土地覆盖，昼夜LST数据和样本的空间分布特征，以提高异质表面上多时相测量的代表性。SDCS使用模拟卫星数据应用于四个不同的研究区域，并与四种现有方法进行了比较，包括随机抽样，系统抽样，基于土地覆盖的分层抽样和条件拉丁超立方体（CLH）抽样。结果表明，SDCS可以显着提高有限样本量的样本的代表性。在LST标准偏差（SD）小于2 K的同质表面中，当使用总像素的0.20％时，通过SDCS估算的昼夜LST的均方根误差（RMSE）小于0.3K。在中等异构表面（LST SD小于5 K）中，需要0.32％的像素才能达到RMSE小于0.5K。在极端异构表面（LST SD> 6 K）中，需要0.96％的像素才能达到相同的精度。进一步，SDCS所选择的样本在昼夜空间中的代表性是稳定的，LST偏差的不确定度小于0.27K。此外，样本表现出分散的空间分布，最近邻居指数为1.27–1.54。SDCS可以推广到各个地区和日期，并且可以在野外活动中用于对非均质表面进行昼夜LST验证。