(Alkama et al. 2022; Cao et al. 2022; Chen et al. 2022; Ding et al. 2022a; Ding et al. 2022b; Gao et al. 2022; Guo et al. 2022; Huang et al. 2022; Jia et al. 2022a; Jia et al. 2022b; Jiang et al. 2022; Jin et al. 2022; Li et al. 2022a; Li et al. 2022b; Liang et al. 2022a; Liang et al. 2022b; Liu et al. 2022a; Liu et al. 2022b; Lu et al. 2022; Ma and Liang 2022; Ma et al. 2022a; Ma et al. 2022b; Ma et al. 2022c; Ma et al. 2022d; Ma et al. 2022e; Ma et al. 2022f; Song et al. 2022a; Song et al. 2022b; Xiao et al. 2022a; Xiao et al. 2022b; Xie et al. 2022; Xu et al. 2022a; Xu et al. 2022b; Zhan and Liang 2022; Zhang et al. 2022a; Zhang et al. 2022b; Zhang et al. 2022c; Zhang et al. 2022d; Zhang et al. 2022e; Zhang et al. 2022f; Zhang et al. 2022g)

Papers published in 2022

1) Alkama, R., Forzieri, G., Duveiller, G., Grassi, G., Liang, S., & Cescatti, A. (2022). Vegetation-based climate mitigation in a warmer and greener World. Nature Communications, 13:606, doi:610.1038/s41467-41022-28305-41469

2) Cao, Y., Liang, S., Sun, L., Liu, J., Cheng, X., Wang, D., Chen, Y., Yu, M., & Feng, K. (2022). Trans-Arctic shipping routes expanding faster than the model projections. Global Environmental Change, 73, 102488

3) Chen, J., He, T., & Liang, S. (2022). Estimation of Daily All-wave Surface Net Radiation with Multispectral and Multitemporal Observations from GOES-16 ABI. IEEE Transactions on Geoscience and Remote Sensing

4) Ding, A., Liang, S., & et al (2022a). Improving the asymptotic radiative transfer model to better characterize the pure snow hyperspectral bidirectional reflectance. IEEE Trans. Geosci. Remote Sens., doi: 10.1109/TGRS.2022.3144831

5) Ding, A., Ma, H., Liang, S., & He, T. (2022b). Extension of the Hapke model to the spectral domain to characterize soil physical properties. Remote Sensing of Environment, 269, 112843

6) Gao, X., Liang, S., Wang, D., Li, Y., He, B., & Jia, A. (2022). Exploration of a novel geoengineering solution: lighting up tropical forests at night. Earth System Dynamics, 13,219-230

7) Guo, T., He, T., Liang, S., Roujean, J.-L., Zhou, Y., & Huang, X. (2022). Multi-decadal analysis of high-resolution albedo changes induced by urbanization over contrasted Chinese cities based on Landsat data. Remote Sensing of Environment, 269, 112832

8) Huang, X., Zheng, Y., Zhang, H., Lin, S., Liang, S., Li, X., Ma, M., & Yuan, W. (2022). High spatial resolution vegetation gross primary production product: Algorithm and validation. Science of Remote Sensing, 100049

9) Jia, A., Liang, S., & Wang, D. (2022a). Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data. Remote Sensing of Environment, 278, 113105

10) Jia, A., Wang, D., Liang, S., Peng, J., & Yu, Y. (2022b). Global Daily Actual and Snow-Free Blue-Sky Land Surface Albedo Climatology From 20-Year MODIS Products. Journal of Geophysical Research: Atmospheres, 127, e2021JD035987

11) Jiang, F., Xie, X., Wang, Y., Liang, S., Zhu, B., Meng, S., Zhang, X., Chen, Y., & Liu, Y. (2022). Vegetation greening intensified transpiration but constrained soil evaporation on the Loess Plateau. Journal of Hydrology, 614, 128514

12) Jin, H., Li, A., Liang, S., Ma, H., Xie, X., Liu, T., & He, T. (2022). Generating high spatial resolution GLASS FAPAR product from Landsat images. Science of Remote Sensing, 6, 100060

13) Li, R., Wang, D., Liang, S., Jia, A., & Wang, Z. (2022a). Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network. Remote Sensing of Environment, 274, 112999

14) Li, S., Jiang, B., Liang, S., Peng, J., Liang, H., Han, J., Yin, X., Yao, Y., Zhang, X., Cheng, J., Zhao, X., Liu, Q., & Jia, K. (2022b). Evaluation of nine machine learning methods for estimating daily land surface radiation budget from MODIS satellite data. International Journal of Digital Earth, 15, 1784-1816

15) Liang, H., Jiang, B., Liang, S., Peng, J., Li, S., Han, J., Yin, X., Cheng, J., Jia, K., Liu, Q., Yao, Y., Zhao, X., & Zhang, X. (2022a). A global long-term ocean surface daily/0.05° net radiation product from 1983–2020. Scientific Data, 9, 337

16) Liang, T., Liang, S., Zou, L., Sun, L., Li, B., Lin, H., He, T., & Tian, F. (2022b). Estimation of Aerosol Optical Depth at 30 m Resolution Using Landsat Imagery and Machine Learning. Remote Sensing, 14, 1053

17) Liu, W.H., Shi, J.C., Liang, S., Zhou, S.G., & Cheng, J. (2022a). Simultaneous retrieval of land surface temperature and emissivity from the FengYun-4A advanced geosynchronous radiation imager. International Journal of Digital Earth, 15, 198-225

18) Liu, X., He, T., Sun, L., Xiao, X., Liang, S., & Li, S. (2022b). Analysis of Daytime Cloud Fraction Spatio–Temporal Variation over the Arctic During 2000–2019 from Multiple Satellite Products. Journal of Climate, 1-53

19) Lu, J., He, T., Liang, S., & Zhang, Y. (2022). An Automatic Radiometric Cross-Calibration Method for Wide-Angle Medium-Resolution Multispectral Satellite Sensor Using Landsat Data. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11

20) Ma, H., & Liang, S. (2022). Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model. Remote Sensing of Environment, 273, 112985

21) Ma, H., Liang, S., Xiong, C., Wang, Q., & Jia, A. (2022a). Global land surface 250-m 8-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) product from 2000 to 2020. Earth System Science Data Discussions, 1-22

22) Ma, H., Liang, S., Zhu, Z., & He, T. (2022b). Developing a Land continuous Variable Estimator to generate daily land products from Landsat data. IEEE Transactions on Geoscience and Remote Sensing, 1-16

23) Ma, H., Xiong, C., Liang, S., Zhu, Z., Song, J., Zhang, Y., & He, T. (2022c). Determining the accuracy of the landsat-based land continuous Variable Estimator. Science of Remote Sensing, 100054

24) Ma, R., Xiao, J., Liang, S., Ma, H., He, T., Guo, D., Liu, X., & Lu, H. (2022d). Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data. Geoscientific Model Development, 15, 6637-6657

25) Ma, Y., He, T., Liang, S., Wen, J., Gastellu-Etchegorry, J.-P., Chen, J., Ding, A., & Feng, S. (2022e). Landsat Snow-Free Surface Albedo Estimation Over Sloping Terrain: Algorithm Development and Evaluation. IEEE Transactions on Geoscience and Remote Sensing, 60, 4408914, doi:4408910.4401109/TGRS.4402022.3149762

26) Ma, Y., He, T., Liang, S., & Xiao, X. (2022f). Quantifying the impacts of DEM uncertainty on clear-sky surface shortwave radiation estimation in typical mountainous areas. Agricultural and Forest Meteorology, 327, 109222

27) Song, D.-X., Wang, Z., He, T., Wang, H., & Liang, S. (2022a). Estimation and validation of 30 m fractional vegetation cover over China through integrated use of Landsat 8 and Gaofen 2 data. Science of Remote Sensing, 6, 100058

28) Song, Z., Liang, S., & Zhou, H. (2022b). Top-of-Atmosphere Clear-Sky Albedo Estimation Over Ocean: Preliminary Framework for MODIS. IEEE Transactions on Geoscience and Remote Sensing, 60, 4203409, doi:4203410.4201109/TGRS.4202021.3116620

29) Xiao, X., He, T., Liang, S., Liu, X., Ma, Y., Liang, S., & Chen, X. (2022a). Estimating fractional snow cover in vegetated environments using MODIS surface reflectance data. International Journal of Applied Earth Observation and Geoinformation, 114, 103030

30) Xiao, X., He, T., Liang, S., & Zhao, T. (2022b). Improving fractional snow cover retrieval from passive microwave data using a radiative transfer model and machine learning method. IEEE Transactions on Geoscience and Remote Sensing

31) Xie, Z., Yao, Y., Zhang, X., Liang, S., Fisher, J.B., Chen, J., Jia, K., Shang, K., Yang, J., Yu, R., Guo, X., Liu, L., Ning, J., & Zhang, L. (2022). The Global LAnd Surface Satellite (GLASS) evapotranspiration product Version 5.0: Algorithm development and preliminary validation. Journal of Hydrology, 610, 127990

32) Xu, J., Liang, S., & Jiang, B. (2022a). A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network. Earth System Science Data 14, doi:10.5194/ess-5114-5191-2022

33) Xu, J., Liang, S., Ma, H., & He, T. (2022b). Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks. Remote Sensing of Environment, 280, 113223

34) Zhan, C., & Liang, S. (2022). Improved estimation of the global top-of-atmosphere albedo from AVHRR data. Remote Sensing of Environment, 269, 112836

35) Zhang, G., Ma, H., & Liang, S. (2022a). Estimating 250-m Land Surface and Atmospheric Variables From MERSI Top-of-Atmosphere Reflectance. IEEE Transactions on Geoscience and Remote Sensing

36) Zhang, G., Ma, H., Liang, S., Jia, A., He, T., & Wang, D. (2022b). A machine learning method trained by radiative transfer model inversion for generating seven global land and atmospheric estimates from VIIRS top-of-atmosphere observations. Remote Sensing of Environment, 279, 113132

37) Zhang, Y., Liang, S., et al. ,(2022c). Estimation of land surface incident shortwave radiation from geostationary Advanced Himawari Imager and Advanced Baseline Imager observations using an optimization method. IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2020.3038829

38) Zhang, Y., Liang, S., & He, T. (2022d). Estimation of Land Surface Downward Shortwave Radiation Using Spectral-based Convolutional Neural Network Methods: a case study from the Visible Infrared Imaging Radiometer Suite (VIIRS) Images. IEEE Transactions on Geoscience and Remote Sensing, 10.1109/TGRS.2022.3210990

39) Zhang, Y., Liang, S., Zhu, Z., Ma, H., & He, T. (2022e). Soil moisture content retrieval from Landsat 8 data using ensemble learning. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 32-47

40) Zhang, Y., Liu, J., Liang, S., & Li, M. (2022f). A New Spatial–Temporal Depthwise Separable Convolutional Fusion Network for Generating Landsat 8-Day Surface Reflectance Time Series over Forest Regions. Remote Sensing, 14, 2199

41) Zhang, Y., Ma, J., Liang, S., Li, X., & Liu, J. (2022g). A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets. Giscience & Remote Sensing, 1-16, doi:10.1080/15481603.15482021.12023842