Papers published in 2019

1. Carter, C., & Liang, S. (2019). Evaluation of ten machine learning methods for estimating terrestrial evapotranspiration from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 78, 86-92

2. Gao, X., Liang, S., & He, B. (2019). Detected global agricultural greening from satellite data. Agricultural and Forest Meteorology, 276-277, 107652

3. Guo, Y., Cheng, J., & Liang, S. (2019). Comprehensive assessment of parameterization methods for estimating clear-sky surface downward longwave radiation. Theoretical and Applied Climatology, 135, 1045-1058

4. He, T., Gao, F., Liang, S., & Peng, Y. (2019a). Mapping Climatological Bare Soil Albedos over the Contiguous United States Using MODIS Data. Remote Sensing, 11, 666

5. He, T., Zhang, Y., Liang, S., Yu, Y., & Wang, D. (2019b). Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation. Remote Sensing, 11, 2655

6. Huang, G., Li, Z., Li, X., Liang, S., Yang, K., Wang, D., & Zhang, Y. (2019a). Estimating surface solar irradiance from satellites: Past, present, and future perspectives. Remote Sensing of Environment, 233, 111371

7. Huang, J., Gómez-Dans, J.L., Huang, H., Ma, H., Wu, Q., Lewis, P.E., Liang, S., Chen, Z., Xue, J.-H., Wu, Y., Zhao, F., Wang, J., & Xie, X. (2019b). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276-277, 107609

8. Huang, J., Ma, H., Sedano, F., Lewis, P., Liang, S., Wu, Q., Su, W., Zhang, X., & Zhu, D. (2019c). Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model. European Journal of Agronomy, 102, 1-13

9. Jia, K., Yang, L., Liang, S., Xiao, Z., Zhao, X., Yao, Y., Zhang, X., Jiang, B., & Liu, D. (2019). Long-Term Global Land Surface Satellite (GLASS) Fractional Vegetation Cover Product Derived From MODIS and AVHRR Data. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 508-518

10. Jiang, B., Liang, S., Jia, A., Xu, J., Zhang, X., Xiao, Z., Zhao, X., Jia, K., & Yao, Y. (2019). Validation of the Surface Daytime Net Radiation Product From Version 4.0 GLASS Product Suite. IEEE Geoscience and Remote Sensing Letters, 16, 509-513

11. Liang, S., Liu, Q., Yan, G., Shi, J., & Kerekes, J.P. (2019a). Foreword to the Special Issue on The Recent Progress in Quantitative Land Remote Sensing: Modeling and Estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 391-395

12. Liang, S., Wang, D., He, T., & Yu, Y. (2019b). Remote sensing of earth’s energy budget: synthesis and review. International Journal of Digital Earth, 12, 737-780

13. Liu, X., Tang, B.-H., Yan, G., Li, Z.-L., & Liang, S. (2019). Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data. Remote Sensing, 11, 2843

14. Roujean, J.-L., Liang, S., & He, T. (2019). Editorial for Special Issue:“Remotely Sensed Albedo”. remote sensing, 11, 1941

15. Tao, X., Huang, C., Zhao, F., Schleeweis, K., Masek, J., & Liang, S. (2019). Mapping forest disturbance intensity in North and South Carolina using annual Landsat observations and field inventory data. Remote Sensing of Environment, 221, 351-362

16. Wang, L., Wang, P., Liang, S., Qi, X., Li, L., & Xu, L. (2019a). Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index. Computers and Electronics in Agriculture, 160, 82-90

17. Wang, Y., Jiang, B., Liang, S., Wang, D., He, T., Wang, Q., Zhao, X., & Xu, J. (2019b). Surface Shortwave Net Radiation Estimation from Landsat TM/ETM+ Data Using Four Machine Learning Algorithms. Remote Sensing, 11, 2847

18. Xu, J., Yao, Y., Liang, S., Liu, S., Fisher, J.B., Jia, K., Zhang, X., Lin, Y., Zhang, L., & Chen, X. (2019). Merging the MODIS and Landsat Terrestrial Latent Heat Flux Products Using the Multiresolution Tree Method. IEEE Transactions on Geoscience and Remote Sensing, 57, 2811-2823

19. Yao, T., Xue, Y., Chen, D., Chen, F., Thompson, L., Cui, P., Koike, T., Lau, W.K.M., Lettenmaier, D., Mosbrugger, V., Zhang, R., Xu, B., Dozier, J., Gillespie, T., Gu, Y., Kang, S., Piao, S., Sugimoto, S., Ueno, K., Wang, L., Wang, W., Zhang, F., Sheng, Y., Guo, W., Ailikun, Yang, X., Ma, Y., Shen, S.S.P., Su, Z., Chen, F., Liang, S., Liu, Y., Singh, V.P., Yang, K., Yang, D., Zhao, X., Qian, Y., Zhang, Y., & Li, Q. (2019). Recent Third Pole’s Rapid Warming Accompanies Cryospheric Melt and Water Cycle Intensification and Interactions between Monsoon and Environment: Multidisciplinary Approach with Observations, Modeling, and Analysis. Bulletin of the American Meteorological Society, 100, 423-444

20. Zhan, C., Allan, R.P., Liang, S., Wang, D., & Song, Z., & (2019). Evaluation of Five Satellite Top-of-Atmosphere Albedo Products over Land. Remote Sensing, 11, 2919

21. Zhang, X., Wang, D., Liu, Q., Yao, Y., Jia, K., He, T., Jiang, B., Wei, Y., Ma, H., Zhao, X., Li, W., & Liang, S. (2019a). An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product from MODIS Data. IEEE Transactions on Geoscience and Remote Sensing, 57, 4636-4650

22. Zhang, Y., Liang, S., & Yang, L. (2019b). A Review of Regional and Global Gridded Forest Biomass Datasets. Remote Sensing, 11, 2744

23. Zhou, H., Liang, S., He, T., Wang, J., Bo, Y., & Wang, D. (2019a). Evaluating the Spatial Representativeness of the MODerate Resolution Image Spectroradiometer Albedo Product (MCD43) at AmeriFlux Sites. Remote Sensing, 11, 547

24. Zhou, J., Liang, S., Cheng, J., Wang, Y., & Ma, J. (2019b). The GLASS Land Surface Temperature Product. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 493-507