Publications

Estimating soil moisture from space using various microwave wavelengths is essential for predicting natural disasters and analyzing the Earth's water cycle. This study examines how well space-based technology can measure soil moisture (SM) and how it performs in different environments. It found that AMSR2 C-band products work better in areas with more vegetation, while X-band products are less effective. In areas with little vegetation, all AMSR2 products have weaker performance because of their limitations in detecting moisture in dry soil. The study also found that daytime measurements work better in areas with less vegetation, while nighttime measurements are more effective in densely vegetated areas. By using different products based on their strengths and weaknesses, researchers can improve the accuracy of soil moisture measurements, but this may result in reduced coverage of the area being studied.

Estimating precipitation from space using microwave satellite systems is essential for managing water resources, predicting natural disasters, and analyzing the Earth's water cycle. This study compares two algorithms, SM2RAIN and SM2RAIN-NWF, for estimating rainfall using soil moisture data. The newer SM2RAIN-NWF algorithm offers improved results by combining SM2RAIN with a net water flux model. We found that SM2RAIN-NWF performed better than SM2RAIN, especially in arid and semi-arid regions. The study also discovered that drainage played a crucial role in improving rainfall estimates, while evapotranspiration had a minimal impact.

Rainfall estimation using remote sensing technology offers a more accurate alternative to traditional measurement methods due to its high resolution in both time and space. The SMA2RAIN-NWF algorithm, an improved version of the original SM2RAIN algorithm, uses satellite soil moisture data to estimate rainfall. This study aims to evaluate the effectiveness of SMA2RAIN-NWF using multiple soil moisture products and different aggregation periods. The results show that the algorithm performs better as the aggregation levels increase and that it is more effective in urban areas. Overall, the SMA2RAIN-NWF algorithm demonstrates improved performance compared to the original SM2RAIN algorithm.

Predicting water cycling in agricultural watersheds is challenging due to factors like farming practices. This study looks at using remote sensing evapotranspiration (ET) data and crop yield information to improve the accuracy of the Soil and Water Assessment Tool (SWAT) model. By adding more constraints to the model, such as crop yield, the number of acceptable parameter sets was reduced, and the model's performance improved. The results suggest that using crop yield data as an additional constraint can help reduce uncertainty and increase the accuracy of ET predictions in agricultural watersheds.

Soil moisture is important for understanding the global water cycle, but current satellite measurements are not continuous in time or space. This study combines data from NASA's Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active Passive (SMAP) to improve soil moisture estimates in a land surface model (LSM). The results show a 61.3% improvement in LSM soil moisture accuracy when combining the two satellite systems. However, using satellite data in areas with dense vegetation can lead to less accurate results. This research is the first to use global GNSS-based soil moisture observations in LSMs, which can help fill gaps in soil moisture measurements and improve our understanding of the water cycle.

As the intensity and frequency of storm events are projected to increase due to climate change, local agencies urgently need a timely and reliable framework for flood forecasting, downscale from watershed to street level in urban areas. Integrated with property data with various hydrometeorological data, the flood prediction model can also provide further insight into environmental justice, which will aid households and government agencies’ decision-making. This study uses deep learning (DL) methods and radar-based rainfall data to predict the inundated areas and analyze the property quickly and demographic data concerning stream proximity to provide a way to quantify socioeconomic impacts. We expect that our DL-based models will improve the accuracy of forecasting floods and provide a better picture of which communities bear the worst burdens of flooding, and encourage city officials to address the underlying causes of flood risk.