An assessment of global forest cover maps using regional higher-resolution reference data sets. - Xiao-Peng Song; Chengquan Huang; Joseph O. Sexton; Min Feng; Raghuram Narasimhan; Saurabh Channan; John R. Townshend (IGARSS, 2011)

Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges. - John R. Townshend; Jeffrey G. Masek; Chengquan Huang; Eric F. Vermote; Feng Gao 009; Saurabh Channan; Joseph O. Sexton; Min Feng; Raghuram Narasimhan; Do-Hyung Kim; Kuan Song; Danxia Song 001; et al. (Int. J. Digit. Earth, 2012)

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. - Joseph O. Sexton; Xiao-Peng Song; Min Feng; Praveen Noojipady; Anupam Anand; Chengquan Huang; Do-Hyung Kim; Kathrine M. Collins; Saurabh Channan; Charlene M. DiMiceli; John R. Townshend (Int. J. Digit. Earth, 2013)

Integrating global land cover products for improved forest cover characterization: an application in North America. - Xiao-Peng Song; Chengquan Huang; Min Feng; Joseph O. Sexton; Saurabh Channan; John R. Townshend (Int. J. Digit. Earth, 2014)

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover. - Xiao-Peng Song; Chengquan Huang; Joseph O. Sexton; Saurabh Channan; John R. Townshend (Remote. Sens., 2014)

A time-series model for characterizing continuous land cover change. - Xiao-Peng Song (IGARSS, 2016)

Assessment of the three factors affecting Myanmar's forest cover change using Landsat and MODIS vegetation continuous fields data. - Fujiang Liu; Chengquan Huang; Yong Pang; Mengxue Li; Dan-Xia Song 001; Xiao-Peng Song; Saurabh Channan; Joseph O. Sexton; Die Jiang; Ping Zhang; Yan Guo; Yao-Feng Li; et al. (Int. J. Digit. Earth, 2016)

Improving global land cover characterization through data fusion. - Xiao-Peng Song; Chengquan Huang; John R. Townshend (Geo spatial Inf. Sci., 2017)

Development of S-NPP VIIRS global surface type classification map using support vector machines. - Rui Zhang; Chengquan Huang; Xiwu Zhan; Huiran Jin; Xiao-Peng Song (Int. J. Digit. Earth, 2018)

Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change. - Cheng Fu; Xiao-Peng Song; Kathleen Stewart (Remote. Sens., 2019)

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016. - Jie Pei; Li Wang 055; Xiaoyue Wang 002; Zheng Niu; Maggi Kelly; Xiao-Peng Song; Ni Huang; Jing Geng; Haifeng Tian; Yang Yu; Shiguang Xu; Lei Wang 170; et al. (Remote. Sens., 2019)

Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach. - Chenxi Lin; Liheng Zhong; Xiao-Peng Song; Jinwei Dong; David B. Lobell; Zhenong Jin (CoRR, 2021)

Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia's 13 Capital Cities Using a Landsat Time Series. - Amal H. Aljaddani; Xiao-Peng Song; Zhe Zhu (Remote. Sens., 2022)

The future of global land change monitoring. - Xiao-Peng Song (Int. J. Digit. Earth, 2023)

Satellite-based soybean yield prediction in Argentina: A comparison between panel regression and deep learning methods. - Yuhao Wang; Kuishuang Feng; Laixiang Sun; Yiqun Xie; Xiao-Peng Song (Comput. Electron. Agric., 2024)

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping. - Mukti R. Subedi; Carlos Portillo-Quintero; Nancy E. McIntyre; Samantha S. Kahl; Robert D. Cox; Gad Perry; Xiao-Peng Song (Remote. Sens., 2024)