Publications

The use of satellite-based soil moisture (SM) data in Earth system science can be hindered by gaps in observations. To address this, our study introduces a method to fill in these gaps in the Soil Moisture Active Passive (SMAP) dataset. We utilized a basic water balance equation, considering factors like precipitation and soil water loss, to estimate soil water content over 12-hour periods. This led to the development of a new, continuous SM product for the contiguous United States. This product, named the SMAP-based 12-hourly SM product, showed promising results, aligning well with on-the-ground measurements and effectively capturing soil moisture variations, especially those related to heavy rainfall events. This continuous dataset, with its detailed insights into soil water dynamics, offers valuable contributions to our understanding of land-surface hydrology.

The Water Balance Equation (WBE) is like a budget for water in a specific area, tracking how much water comes in, goes out, and how much is stored over time. It's crucial in understanding water availability and the water cycle, especially on land. The WBE relates rainfall, evaporation, and runoff to changes in soil moisture.

Recently, there's been interest in using satellite data to guess some WBE parts, but this can be complex. Challenges include simplifications in the equation, missing water cycle elements, and limits of satellite data. Our study used advanced modeling to show these issues can affect the reliability of WBE estimates. We suggest treating these estimates as general guides, reflecting the satellite data used, rather than exact measurements. Better remote sensing and improved WBE will enhance our understanding of the water cycle.

This study aims to address gaps in assessing the accuracy of satellite-based soil moisture data by utilizing machine learning techniques to generate spatially complete error maps for Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) systems, and by examining the influence of environmental conditions on satellite-based soil moisture retrievals, revealing that a significant portion of missing error information from triple collocation analysis (TCA) can be reconstructed using ensemble prediction mean of machine learning models, contributing to a more comprehensive understanding of soil moisture dynamics across the three satellite missions.

Accurately estimating soil moisture from satellite data is crucial for various Earth science disciplines. This study introduces a Bayesian approach to analyze error characteristics in widely used satellite-derived soil moisture data. By applying Bayesian hierarchical modeling and triple collocation analysis, the study examines the influence of environmental factors and human activities on data accuracy. The findings highlight the adaptability and potential of Bayesian modeling for sensitivity analysis in remote sensing research. The study also identifies factors like irrigation, vegetation, and retrieval algorithm assumptions as sources of errors. It emphasizes the need to consider multiple factors when assessing data quality. Overall, the research provides a valuable framework for investigating error characteristics in satellite-based soil moisture data.

In many protected areas and rivers with non-constant flow, there is limited ground data, making it hard to get streamflow information. This study looks at using streamflow data from regions with lots of information (North America, South America, and Western Europe) to help estimate streamflow in areas with less data (South Africa and Central Asia). By using machine learning algorithms trained on climate and catchment attributes from data-rich areas, we found they could effectively estimate monthly streamflow in data-poor regions. This study helps guide the selection of input data and machine learning methods for estimating streamflow in different geographic locations.

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.