代表性论文1，Linjing Zhang, Xiaoxue Zhang*, Zhenfeng Shao, Wenhao Jiang, and Huimin Gao. Integrating Sentinel-1 and 2 with LiDAR data to estimate aboveground biomass of subtropical forests in northeast Guangdong, China. International Journal of Digital Earth, 2023, 16 (1): 158-182；
代表性论文2，Linjing Zhang, Huimin Gao*, Xiaoxue Zhang. Combining Radiative Transfer Model and Regression Algorithms for Estimating Aboveground Biomass of Grassland in West Ujimqin, China. Remote Sensing, 2023, 15(11): 2918；
代表性论文3，Linjing Zhang, Xinran Yin, Yaru Wang, Jing Chen. Aboveground biomass mapping in semi-arid forests by inte-grating airborne LiDAR with Sentinel-1 and Sentinel-2 time series data. Remote Sensing, 2024, 16(17), 3241；
代表性论文4，Zhenfeng Shao, Linjing Zhang*, and Lei Wang. Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-ground Biomass. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(12): 5569-5582；
代表性论文5，Linjing Zhang, Zhenfeng Shao*, Jianchen Liu, and Qimin Cheng. Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data. Remote Sensing, 2019, 11(12): 1459；
代表性论文6，Zhen Zhang, Tao Jiang, Chenxi Liu, Linjing Zhang*. An effective classification method for hyperspectral image with very high resolution based on encoder-decoder architecture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:1509-1519；
代表性论文7， Zhenfeng Shao and Linjing Zhang*. Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China.Sensors, 2016,16(6):834；
代表性论文8，Xinliang Pan, Tao Jiang, Zhen Zhang, Baikai Sui, Chenxi Liu and Linjing Zhang*. A New Method for Extracting Laver Culture Carriers Based on Inaccurate Supervised Classification with FCN-CRF. Journal of Marine Science and Engineering, 2020, 8(4):274；
代表性论文9， Linjing Zhang, Zhenfeng Shao*, and Chunyuan Diao. (2015). Synergistic retrieval model of forest biomass using the integration of optical and microwave remote sensing. Journal of Applied Remote Sensing, 2015, 9: 096069；
代表性论文10，Linjing Zhang, Qimin Cheng*, and Congmin Li. (2015). Improved model for estimating the biomass of Populus euphratica forest using the integration of spectral and textural features from the Chinese high-resolution remote sensing satellite GaoFen-1. Journal of Applied Remote Sensing, 9: 096010, 2015。
