Soil assessment is important for mobile robot planning and navigation on natural and planetary environments. Terramechanic characteristics can be inferred from the thermal behaviour of soils under the influence of sunlight using remote sensors such as Long-Wave Infrared cameras. However, this behaviour is greatly affected by the low atmospheric pressures of planets such as Mars, so practical models are needed to relate robot remote sensing data on Earth to target planetary exploration conditions. This article proposes a general framework based on multipurpose environmental chambers to generate representative diurnal cycle dataset pairs that can be useful to relate the thermal behaviour of a soil on Earth to the corresponding behaviour under planetary pressure conditions using remote sensing. Furthermore, we present an application of the proposed framework to generate datasets using the UMA-Laserlab chamber, which can replicate the atmospheric \ch{CO2} composition of Mars. In particular, we analyze the thermal behaviour of four soil samples of different granularity by comparing replicated Martian surface conditions and their Earth's diurnal cycle equivalent. Results indicate a correlation between granularity and thermal inertia that is consistent with available Mars surface measurements recorded by rovers. The resulting dataset pairs, consisting of representative diurnal cycle thermal images with heater, air, and subsurface temperatures, have been made available for the scientific community.

翻译：土壤评估对于移动机器人在自然环境和行星环境中的规划与导航至关重要。通过长波红外相机等遥感设备，可根据阳光影响下土壤的热行为推断地形力学特性。然而，这种热行为受到火星等行星低气压条件的显著影响，因此需要建立实用模型，将地球上的机器人遥感数据与目标行星探测条件相关联。本文提出一个基于多用途环境舱的通用框架，用于生成具有代表性的昼夜循环数据集对，从而通过遥感手段关联地球土壤的热行为与行星气压条件下对应的热行为。此外，我们展示了该框架在UMA-Laserlab环境舱中的应用——该环境舱可模拟火星大气中的二氧化碳成分。具体而言，我们通过对比复现的火星表面条件及其对应的地球昼夜循环等效条件，分析了四种不同颗粒度土壤样本的热行为。结果表明，颗粒度与热惯性之间存在相关性，这与火星车获取的火星表面测量数据一致。最终生成的数据集对（包含代表性的昼夜循环热图像及加热器、空气和地下温度数据）已向科学界开放共享。