Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress. - Gaoxiang Zhou; Xiangnan Liu; Shuang Zhao; Ming Liu; Ling Wu (Remote. Sens., 2017)

Sensitivity Analysis of Discrete Models and Application in Biological Networks. - Gaoxiang Zhou; Kai-Wen Liang; Natasa Miskov-Zivanov (BCB, 2018)

Combination of Crop Growth Model and Radiation Transfer Model with Remote Sensing Data Assimilation for Fapar Estimation. - Gaoxiang Zhou; Ming Liu; Xiangnan Liu; Jonathan Li 001 (IGARSS, 2018)

A New Vegetation Index Based on Multitemporal Sentinel-2 Images for Discriminating Heavy Metal Stress Levels in Rice. - Zhijiang Zhang; Meiling Liu; Xiangnan Liu; Gaoxiang Zhou (Sensors, 2018)

Dynamic system explanation: DySE, a framework that evolves to reason about complex systems - lessons learned. - Cheryl A. Telmer; Khaled Sayed; Adam A. Butchy; Kara N. Bocan; Emilee Holtzapple; Casey E. Hansen; Gaoxiang Zhou; Yasmine Ahmed; Natasa Miskov-Zivanov (AIDR, 2019)

Long-Term Trend of Ground-Level PM2.5 Concentrations Over 2012-2017 in China. - Ming Liu; Gaoxiang Zhou; Rebecca K. Saari; Jonathan Li 001 (IGARSS, 2019)

Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation. - Gaoxiang Zhou; Xiangnan Liu; Ming Liu (Remote. Sens., 2019)

Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. - Yuanyuan Meng; Xiangnan Liu; Chao Ding; Boliang Xu; Gaoxiang Zhou; Lihong Zhu (Ecol. Informatics, 2020)

An autoencoder-based model for forest disturbance detection using Landsat time series data. - Gaoxiang Zhou; Ming Liu; Xiangnan Liu (Int. J. Digit. Earth, 2021)

Spatiotemporal Disparity and Environmental Inequality in Fossil Fuel Carbon Emissions in China: 2010-2019. - Ming Liu; Ziyan Liu; Gaoxiang Zhou; Ying Wang; Xuanchen Liu; Ling Han (IGARSS, 2023)

SIFNet: A self-attention interaction fusion network for multisource satellite imagery template matching. - Ming Liu; Gaoxiang Zhou; Lingfei Ma; Liangzhi Li 002; Qiong Mei (Int. J. Appl. Earth Obs. Geoinformation, 2023)

A spatiotemporal attention-augmented ConvLSTM model for ocean remote sensing reflectance prediction. - Gaoxiang Zhou; Jun Chen; Ming Liu; Lingfei Ma (Int. J. Appl. Earth Obs. Geoinformation, 2024)

A gap-filling method for satellite-derived chlorophyll-a time series based on neighborhood spatiotemporal information. - Gaoxiang Zhou; Ming Liu; Linlin Xu; Liangzhi Li 002 (Int. J. Appl. Earth Obs. Geoinformation, 2024)

A Spatiotemporal Fusion Transformer Model for Chlorophyll-a Concentrations Prediction Over Large Areas With Satellite Time Series Data. - Gaoxiang Zhou; Ming Liu; Liangzhi Li 002 (IEEE Trans. Geosci. Remote. Sens., 2024)