Conference Agenda

The Soil Moisture and Ocean Salinity (SMOS) mission has played a key role in monitoring global soil moisture, providing valuable measurements over the last 15 years. This research has focused on improving SMOS products by refining the algorithms, integrating data from multiple sensors, and addressing spatial and temporal gaps in soil moisture data. While SMOS, equipped with an L-band microwave radiometer, has delivered important insights, there have been ongoing challenges in improving the accuracy and consistency of SMOS soil moisture product, especially when comparing data from SMOS with other missions like SMAP. One major achievement of this research has been the development of techniques to reduce the discrepancies between SMOS and SMAP products. By correcting systematic biases in the brightness temperature (TB) measurements, particularly those arising from differences in observation angles and atmospheric effects, the research has helped improve data consistency. Additionally, the research has explored the use of multi-angular data from SMOS to enhance soil moisture retrieval through a Multi Channel Collaborative Algorithm (MCCA). This approach has effectively utilized multi-angular brightness temperature data and vegetation optical depth to improve the accuracy of soil moisture retrieval, especially in regions with varying surface characteristics. Another important outcome has been the enhancement of spatial resolution. The research combined passive and active microwave measurements with optical and infrared data to downscale SMOS and SMAP soil moisture products to finer spatial resolutions, such as 1 km. The incorporation of machine learning techniques has further improved the temporal resolution of these products, enabling the creation of long-term, high-resolution datasets spanning several decades. In addition, this research has utilized in-situ data from regions such as the Tibetan Plateau, which has been crucial in validating satellite-derived soil moisture products. These data have improved our understanding of soil moisture dynamics, particularly in challenging environments like permafrost and seasonally frozen ground. By combining in-situ data with satellite observations, this research has provided better insights into drought monitoring and agricultural forecasting, supporting more accurate assessments of global water cycles and climate variability. The work has led to the development of a harmonized soil moisture product with improved spatial and temporal resolution, filling important gaps in global change research. By improving the consistency of SMOS and SMAP products, the research has laid the groundwork for a more reliable, long-term soil moisture monitoring system, which will be valuable for research in hydrology, agriculture, and climate science.

Glaciers in High Mountain Asia (HMA) are important water sources for millions of people in arid regions. Mass balance states of the glaciers exhibit significant differences in different subregions. However, the reasons for this have been unclear due to limited accurate temperature and precipitation datasets in extremely high altitudes, bringing great uncertainty to the survival and development of the people in different subregions. In this work, the ablation index and seasonal accumulation index are proposed based on firn and snow state observation from satellite, which present the ablation and accumulation pattern of the glaciers in HMA. The results show that glaciers with the lowest ablation index, lowest summer accumulation ratio which locates in the center of the HMA region, has the most balanced state (+0.05 m w.e/a). In contrast, the type with high ablation, median summer accumulation ratio, presents a severely negative mass balance (-0.47 m w.e/a). Under similar ablation intensity, the type of glaciers with higher summer precipitation is more negative. To assess the reliability of our method, we compare the index with the summer snow line, weather station records, and precipitation data. This work provides novel methods and knowledge to understand and project the glacier mass balance heterogeneity in HMA.