Unmanned aerial vehicles (UAVs) can offer higher spatial resolution images compared to satellites. In this experiment, an L-band microwave radiometer named PoLRa was equipped on an UAV to detect surface soil moisture and a new soil moisture retrieval algorithm was developed for it. Compared with the algorithm provided by suppliers of PoLRa, the new algorithm can significantly improve the accuracy of soil moisture and enhance spatial details. A comparison with ground-based soil moisture measurements showed that the UAV's spatial resolution reached 10 meters with an accuracy of 0.08 m3/m3. This demonstrates significant advantages for monitoring soil moisture at the farmland scale, making it applicable to precision agriculture, flash flood warnings, and drought monitoring in the future.

Soil texture is a fundamental soil property and serve as a crucial input to many Land Surface Models (LSMs). However, current soil texture datasets used in LSMs are usually extrapolated from in-situ scale geological surveys, which may contain high uncertainties due to the mismatch in spatial scales. Here, we propose a method to optimize several currently existing soil texture datasets by using a long-term satellite soil moisture dataset. The optimized soil texture datasets may provide an opportunity to improve land surface simulations in LSMs.

Soil moisture drydown plays an important role in many hydrometeorological processes such as regulating surface energy budget, evapotranspiration, and infiltration. In this study, we analyzed the spatial and temporal characteristics of global soil moisture drydown using the daily-scale long-term satellite soil moisture product NNSM. We find that the time-series of τSand τLremained stable over the years. The spatial distribution of global τSand τLshows an anti-spatial correlation pattern, implying that strong land-atmosphere interaction in the short and long term occurs in different regions. τS. of NNSM is closer to the observation measurement than SMAP. The results show that NNSM can provide a long-term global reference for global soil moisture memory characterization, and for improving land surface models.

Remote sensing soil moisture (SM) products are an important source for obtaining surface soil moisture and have been widely used in large-scale hydrological, land surface, and ecological studies. The accuracy of remote sensing products determines the reliability of these applications. In this study, we evaluated three long time-series SM remote sensing products CCI, NNsm, and FY3B in China using SM measured at 732 stations. It is found that all three products tend to underestimate the surface SM. CCI has the highest correlation coefficient with ground observation, implying CCI may have an advantage in predicting long periods of drought. The differences in bias are related to the type of land cover. However, newly developed SM products need to be further validated to provide a solid reference for data selection in China.

Rapid monitoring of urban waterlogging is of great significance for disaster recovery. In order to quickly extract the inundation area and evaluate loss, based on Google Earth engine (GEE), we compared four water indexes (NDWI, MNDWI, AWEI, WI2015) and data sources (Sentinel-2 and Landsat-8) in 2021/07/20 Zhengzhou rainstorm. It is found that compared with Landsat-8, Sentinel-2 can provide richer data with higher resolution. The accuracy of monitoring disaster inundation area with MNDWI was the highest (89.7%), and especially when its threshold was around 0.15. The quantitative framework of urban rainwater and flood area in this study can provide technical support for flood disaster analysis of relevant departments.

Agricultural drought in China since the 21st century is an important but little discussed issue. In this study, we adopted NNsm, a newly developed long time series soil moisture product based on remote sensing and artificial neural networks, to identify the drought events in China from 2002 to 2019 by using the severity-area-duration (SAD) analysis. The results show there were 52 long-term drought events ($\geq 4$months) in the study period, while 83.81% of the areas in China have experienced drought. In 2015, the drought area was the largest. There was a drying trend in southern China, central Xinjiang and central Tibet, while a wetting trend in eastern Xinjiang and eastern Tibet. This study suggests the drought develop trend and reveals the areas vulnerable to drought in China, which can help with drought monitoring and mitigation, as well as scientific and technological support for early warning of agricultural drought.