地面观测是遥感科学研究的基础，为定量遥感模型的构建、遥感信息准确高效的反演、真实性检验提供重要的数据支撑。特别是进入工智能时代以来，将地面观测与遥感数据相结合驱动深度学习的模式在一定程度上创新了陆表参数反演的范式。然而，由于地面观测与遥感像元的尺度差异以及陆表本身复杂的空间异质性，地面观测存在代表性误差，将其直接作为像元尺度地面真值使用时会给后续的地面观测数据与遥感数据的协同分析和处理带来很大不确定性。如何通过科学的地面观测获取无限接近遥感像元尺度地面真值是野外遥感观测实验关注的焦点。目前围绕像元尺度地面真值获取已经系统开展了从样区的选择、空间异质性评价、地面样点优化布设、地面观测、到尺度转换获取像元尺度“真值”等研究。虽然针对每一个环节都开展了大量的研究工作，然而各个环节中存在概念模糊和理解不充分的情况，导致像元尺度地面真值获取的不确定性很大。如何对像元尺度地面真值获取过程进行不确定性的约束和控制从而获得不确定性最小的像元尺度地面“真值”是目前亟需解决的瓶颈难题。本文围绕像元尺度地面真值获取的几个关键环节论述了目前存在的挑战以及可能的解决途径，在一定程度上为像元尺度地面真值获取的准确性和精度的提升提供一些新的认识和理论指导。

Ground observation is the foundation of remote sensing scientific research, providing important data support for the construction of quantitative remote sensing models, accurate and efficient inversion of remote sensing information, and validation of remote sensing products. In particular, with the entrance of era of artificial intelligence, ground observation has been combined with satellite data to drive deep learning models, generating remarkable research results in the field of remote sensing. However, with the combination of satellite data with ground observations, uncertainty is unavoidably introduced to the subsequent results and analysis. This is resulted from the representativeness errors of ground observation partly due to the scale differences between ground observations and satellite pixels and partly due to the complex spatial heterogeneity land surface itself. Ground observation only represents the true value of the measured object at the observation time and in the space it represents, but cannot be directly used as the true value at the scale of satellite pixels. How to improve the spatiotemporal representativeness of ground observations on satellite pixel scales and obtain the closest representation of reality has alway been the key issue in the field of remote sensing experiments. The acquisition of pixel scale ground truth involves the selection of sample areas, evaluation of spatial heterogeneity, optimization of ground sample layout, ground observation, and scale conversion. Although a large amount of research has been carried out for each aspect, there are still cases of conceptual ambiguity and insufficient understanding in each link, resulting in significant uncertainty in obtaining pixel scale ground truth. How to constrain and control the uncertainty of the pixel scale ground truth in the acquisition process and how to obtain the pixel scale ground “truth” with minimum uncertainty is currently a bottleneck problem that urgently needs to be solved. This article discusses the current challenges and possible solutions in obtaining pixel scale ground “truth”, aiming to provide new insights and theoretical guidance for remote sensing field observation experiments.