A5055719885- Precipitation Measurement and Analysis
- Meteorological Phenomena and Simulations
- Hydrology and Watershed Management Studies
- Advanced Image Fusion Techniques
- Remote-Sensing Image Classification
- Remote Sensing in Agriculture
- Image and Signal Denoising Methods
- Energy Load and Power Forecasting
- Hydrological Forecasting Using AI
- Remote Sensing and LiDAR Applications
- Flood Risk Assessment and Management
- Automated Road and Building Extraction
- Soil Moisture and Remote Sensing
- Geophysics and Gravity Measurements
University of Wisconsin–Madison
2023-2025
Tongji University
2021-2022
Machine learning methods for water depth estimation using remote sensing require accurate prior measurements. The successful operation of the ICESat-2 mission provides in shallow directly; however, its spatial coverage is limited. has been used to link optical images and data bathymetric mapping. Compared with other machine models, convolutional neural network (CNN) models utilize local correlation between adjacent pixels can thus reduce effect environmental noise. However, existing CNN rely...
Satellite-based quantitative precipitation estimates (QPE), such as NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG), provide easily accessible continental-to-global forcings flood prediction and other hydrologic applications. Nevertheless, when used in prediction, uncertainty satellite-based QPE often leads to significant bias. This forcing is further blended with error sources, including process representation, parameter values, their interactions. The identification decoupling...
Hydrological studies often depend on model simulations to analyze flood occurrences and frequency. A major challenge in this domain is quantifying reducing uncertainty simulations, particularly when dealing with complex models like WRF-Hydro, which involve extensive parameterization. To address this, we present a novel parameter estimation approach using Iterative Ensemble Smoothers (iES). While iES has been widely applied calibrating parameters for general circulation groundwater models,...
Abstract Satellite‐based precipitation observations can provide near‐global coverage with high spatiotemporal resolution in near‐realtime. Their utility, however, is hindered by oftentimes large uncertainties that vary substantially space and time. This problem particularly pronounced regions which lack dense ground‐based measurements to quantify or reduce such uncertainty. Since this uncertainty is, definition, a random process, probabilistic representations are needed advance their...
Reliable rainfall data are critical for managing hydrometeorological hazards in West Africa, yet they often sparse and temporally inconsistent. The current study assessed the accuracy of four near real-time satellite-based data, namely IMERGv7 Late, IMERGv6 Early, GSMAP-NRT PERSIANN-DIR Now, estimation hydrological modeling Ouémé basin. These datasets were compared with ground-based bias-corrected used to calibrate validate model HBV light. While demonstrated qualitative accuracy, their...
Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending different resolutions. Spatial unmixing (SU) widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance variation in land cover between pixels common issue existing SU methods. For example, all coarse neighbors local window are treated equally model, inappropriate. Moreover, determination appropriate clusters...
Satellite-based precipitation observations provide near-global coverage with high spatiotemporal resolution in near-realtime. Their utility, however, is hindered by oftentimes large errors that vary substantially space and time. Since uncertainty is, definition, a random process, probabilistic expression of satellite-based product needed to advance their operational applications. Ensemble methods, which depicted via multiple realizations fields, have been widely used other contexts such as...