Hankui Zhang

Assistant Professor

South Dakota State University

United States

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About

Hankui Zhang is an Assistant Professor at South Dakota State University in the United States. His research focuses on remote sensing and deep learning applications, particularly in cloud detection, land cover mapping, and land surface phenology. Recent articles include advancements in Sentinel-2 data processing and large-scale environmental monitoring techniques, demonstrating his expertise in utilizing satellite imagery for ecological research.

Articles (16)

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2025

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2025

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2024

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2024

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2023

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2022

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2022

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2020

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2020

Collaborators (3)

Lin Yan

Assistant Professor

Michigan State University

UNITED STATES

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Zutao Yang

Assistant Professor

Auburn University

UNITED STATES

View details

David Roy

Professor

Michigan State University

UNITED STATES

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Hankui Zhang

Research Interests

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About

Articles (16)

A globally applicable deep learning model for Sentinel-2 cloud and shadow detection

A global Swin-Unet Sentinel-2 surface reflectance-based cloud and cloud shadow detection algorithm for the NASA Harmonized Landsat Sentinel-2 (HLS) dataset

Large-scale mapping of plastic-mulched land from Sentinel-2 using an index-feature-spatial-attention fused deep learning model

Improved Landsat Operational Land Imager (OLI) Cloud and Shadow Detection with the Learning Attention Network Algorithm (LANA)

Estimating Brazilian Amazon Canopy Height Using Landsat Reflectance Products in a Random Forest Model with Lidar as Reference Data

Classifying raw irregular time series (CRIT) for large area land cover mapping by adapting transformer model

The Improved U-STFM: A Deep Learning-Based Nonlinear Spatial-Temporal Fusion Model for Land Surface Temperature Downscaling

A new constant scattering angle solar geometry definition for normalization of GOES-R ABI reflectance times series to support land surface phenology studies

Revealing post-megafire spectral and compositional recovery in the Siberian boreal forest using Landsat time series and regression-based unmixing approach

Reconstruction of seamless harmonized Landsat Sentinel-2 (HLS) time series via self-supervised learning

Linearly interpolating missing values in time series helps little for land cover classification using recurrent or attention networks

Utility of daily 3 m Planet Fusion Surface Reflectance data for tillage practice mapping with deep learning

MODIS Evapotranspiration Downscaling Using a Deep Neural Network Trained Using Landsat 8 Reflectance and Temperature Data

A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data

Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations

Collaborators (3)

Lin Yan

Zutao Yang

David Roy

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