The Mars’ climate is cold and dry in the most recent epoch, and liquid water activities are considered extremely limited. Previous orbital data only show sporadic hydrous minerals in the northern lowlands of Mars excavated by large impacts. Using the short-wave infrared spectral data obtained by the Zhurong rover of China’s Tianwen-1 mission, which landed in southern Utopia Planitia on Mars, we identify hydrated sulfate/silica materials on the Amazonian terrain at the landing site. These hydrated minerals are associated with bright-toned rocks, interpreted to be duricrust developed locally. The lithified duricrusts suggest that formation with substantial liquid water originates by either groundwater rising or subsurface ice melting. In situ evidence for aqueous activities identified at Zhurong’s landing site indicates a more active Amazonian hydrosphere for Mars than previously thought.

Variations of total water contents were detected in situ from the lunar regolith and rock at the Chang’E-5 landing site.

The Zhurong rover of the Tianwen-1 mission landed in southern Utopia Planitia, providing a unique window into the evolutionary history of the Martian lowlands. During its first 110 sols, Zhurong investigated and categorized surface targets into igneous rocks, lithified duricrusts, cemented duricrusts, soils and sands. The lithified duricrusts, analysed by using laser-induced breakdown spectroscopy onboard Zhurong, show elevated water contents and distinct compositions from those of igneous rocks. The cemented duricrusts are likely formed via water vapor–frost cycling at the atmosphere–soil interface, as supported by the local meteorological conditions. Soils and sands contain elevated magnesium and water, attributed to both hydrated magnesium salts and adsorbed water. The compositional and meteorological evidence indicates potential Amazonian brine activities and present-day water vapor cycling at the soil–atmosphere interface. Searching for further clues to water-related activities and determining the water source by Zhurong are critical to constrain the volatile evolution history at the landing site.

Accurate assessments of surface temperatures on the Moon are important for understanding the physical properties of the lunar surface regolith and thermal effects on the reflectance spectra beyond 2 μm. The local time resolved spectral measurements of the same region were used to estimate the surface temperature at the Chang'E‐4 landing site. The results show that the surface temperatures at the landing site at lunar local time 14:28–14:41 are 346 ± 8 K. The spectral absorptions at ∼2 μm of the lunar surface are overestimated without sufficient thermal removal, and consequently may have resulted in overestimation of pyroxene and/or glasses at the landing site. The results play a foundation for correcting thermal effects of reflectance data that will be acquired by Chang'E‐5 and ‐6 and possible other future Chang'E missions.

Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.