ALBERT

Beschreibung: Recent climate change has induced widespread soil thawing and permafrost degradation in the Tibetan Plateau. Significant advances have been made in better characterizing Tibetan Plateau soil freeze/thaw dynamics, and their interaction with local-scale ecohydrological processes. However, factors such as sparse networks of in-situ sites and short observational period still limit our understanding of the Tibetan Plateau permafrost. Satellite-based optical and infrared remote sensing can provide information on land surface conditions at high spatial resolution, allowing for better representation of spatial heterogeneity in the Tibetan Plateau and further infer the related permafrost states. Being able to operate at “all-weather” conditions, microwave remote sensing has been widely used to retrieve surface soil moisture, freeze/thaw state, and surface deformation, that are critical to understand the Tibetan Plateau permafrost state and changes. However, coarse resolution (〉10 km) of current passive microwave sensors can add large uncertainties to the above retrievals in the Tibetan Plateau area with high topographic relief. In addition, current microwave remote sensing methods are limited to detections in the upper soil layer within a few centimetres. On the other hand, algorithms that can link surface properties and soil freeze/thaw indices to permafrost properties at regional scale still need improvements. For example, most methods using InSAR (interferometric synthetic aperture radar) derived surface deformation to estimate active layer thickness either ignore the effects of vertical variability of soil water content and soil properties, or use site-specific soil moisture profiles. This can introduce non-negligible errors when upscaled to the broader Tibetan Plateau area. Integrating satellite remote sensing retrievals with process models will allow for more accurate representation of Tibetan Plateau permafrost conditions. However, such applications are still limiting due to a number of factors, including large uncertainties in current satellite products in the Tibetan Plateau area, and mismatch between model input data needs and information provided by current satellite sensors. Novel approaches to combine diverse datasets with models through model initialization, parameterization and data assimilation are needed to address the above challenges. Finally, we call for expansion of local-scale observational network, to obtain more information on deep soil temperature and moisture, soil organic carbon content, and ground ice content.