Papers published in 2014

1. Cai, W., Yuan, W., Liang, S., Liu, S., Dong, W., Chen, Y., Liu, D. & Zhang, H. (2014) Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models. Remote Sensing, 6, 8945.

2. Cai, W., Yuan, W., Liang, S., Zhang, X., Dong, W., Xia, J., Fu, Y., Chen, Y., Liu, D. & Zhang, Q. (2014) Improved estimations of gross primary production using satellite-derived photosynthetically active radiation. Journal of Geophysical Research: Biogeosciences, 119, 2013JG002456.

3. Chen, Y., Xia, J., Liang, S., Feng, J., Fisher, J.B., Li, X., Li, X., Liu, S., Ma, Z., Miyata, A., Mu, Q., Sun, L., Tang, J., Wang, K., Wen, J., Xue, Y., Yu, G., Zha, T., Zhang, L., Zhang, Q., Zhao, T., Zhao, L. & Yuan, W. (2014) Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sensing of Environment, 140, 279-293.

4. Cheng, J., Liang, S., Tzeng, Y.C., Wang, C. & Rebn, H. (2014a) Mapping global broadband emissivity from MODIS albedo product by using dynamic learning neural network. International Journal of Remote Sensing, 35, 1395-1416.

5. Cheng, J., Liang, S., Yao, Y., Ren, B., Shi, L. & Liu, H. (2014b) A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data. Remote Sensing, 6, 111-134.

6. Cheng, J. & Liang, S. (2014a) Effects of thermal-infrared emissivity directionality on surface broadband emissivity and longwave net radiation estimation. IEEE Geoscience and Remote Sensing Letters, 4, 459-463.

7. Cheng, J. & Liang, S. (2014b) Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product. Journal of Geophysical Research: Atmospheres, 119, 614-634.

8. Cheng, J., Liang, S., Dong, L., Ren, B. & Shi, L. (2014) Validation of the moderate-resolution imaging spectroradiometer land surface emissivity products over the Taklimakan Desert. Journal of Applied Remote Sensing, 8, 083675-083675.

9. He, T., Liang, S. & Song, D.-X. (2014a) Analysis of global land surface albedo climatology and spatial-temporal variation during 1981–2010 from multiple satellite products. Journal of Geophysical Research: Atmospheres, 119, 10,281-10,298.

10. He, T., Liang, S., wang, D., Shi, Q. & Tao, X. (2014b) Estimation of high-resolution land surface shortwave albedo from AVIRIS data. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 7, 4919-4928.

11. He, T., Liang, S., Wang, D.D., Shuai, Y.M. & Yu, Y.Y. (2014c) Fusion of Satellite Land Surface Albedo Products Across Scales Using a Multiresolution Tree Method in the North Central United States. IEEE Transactions on Geoscience and Remote Sensing, 52, 3428-3439.

12. Jia, K., Liang, S., Wei, X., Zhang, L., Yao, Y. & Gao, S. (2014a) Automatic land-cover update approach integrating iterative training sample selection and a Markov Random Field model. Remote Sensing Letters, 5, 148-156.

13. Jia, K., Liang, S., Zhang, L., Wei, X., Yao, Y. & Xie, X. (2014b) Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data. International Journal of Applied Earth Observation and Geoinformation, 33, 32-38.

14. Jia, K., Liang, S., Wei, X., Yao, Y., Su, Y., Jiang, B. & Wang, X. (2014c) Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data. Remote Sensing, 6, 11518-11532.

15. Jia, K., Liang, S., Zhang, N., Wei, X., Gu, X., Zhao, X., Yao, Y. & Xie, X. (2014d) Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 49-55.

16. Jiang, B., Zhang, Y., Liang, S., Zhang, X. & Xiao, Z. (2014) Surface Daytime Net Radiation Estimation Using Artificial Neural Networks. Remote Sensing, 6, 11031-11050.

17. Kim, W., Cao, C. & Liang, S. (2014) Assessment of Radiometric Degradation of FY-3A MERSI Reflective Solar Bands using TOA Reflectance of Pseudo-Invariant Calibration Sites. IEEE Geoscience and Remote Sensing Letters, 11, 793-797.

18. Kim, W., He, T., Wang, D., Cao, C. & Liang, S. (2014) Assessment of long-term sensor radiometric degradation using time series analysis. IEEE Transactions on Geoscience and Remote Sensing, 52, 2960-2976.

19. Li, X., Liang, S., Yuan, W., Yu, G., Cheng, X., Chen, Y., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Chen, J., Shao, C., Li, S., Zhang, X., Zhang, Z., Sun, G., Chen, S., Ohta, T., Varlagin, A., Miyata, A., Takagi, K., Saiqusa, N. & Kato, T. (2014) Estimation of evapotranspiration over the terrestrial ecosystems in China. Ecohydrology, 7, 139-149.

20. Li, X., Ma, J., Yao, Y., Liang, S., Zhang, G., Xu, H. & Yagi, K. (2014) Methane and nitrous oxide emissions from irrigated lowland rice paddies after wheat straw application and midseason aeration. Nutrient cycling in agroecosystems, 100, 65-76.

21. Liu, Q., Liang, S., Xiao, Z.Q. & Fang, H.L. (2014) Retrieval of leaf area index using temporal, spectral, and angular information from multiple satellite data. Remote Sensing of Environment, 145, 25-37.

22. Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N. & Liu, S. (2014) Improved direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52, 907-919.

23. Shi, Q. & Liang, S. (2014) Surface sensible and latent heat fluxes over the Tibetan Plateau from ground measurements, reanalysis, and satellite data. Atmospheric Chemistry and Physics, 14, 5659-5677.

24. Wang, D. & Liang, S. (2014) Improving LAI Mapping by Integrating MODIS and CYCLOPES LAI Products Using Optimal Interpolation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 445-457.

25. Wang, D., Liang, S. & Tao, H. (2014) Mapping High-Resolution Surface Shortwave Net Radiation From Landsat Data. IEEE Geoscience and Remote Sensing Letters, 11, 459-463.

26. Xia, J., Liu, S., Liang, S., Chen, Y., Xu, W. & Yuan, W. (2014a) Spatio-Temporal Patterns and Climate Variables Controlling of Biomass Carbon Stock of Global Grassland Ecosystems from 1982 to 2006. Remote Sensing, 6, 1783-1802.

27. Xia, J., Liang, S., Chen, J., Yuan, W., Liu, S., Li, L., Cai, W., Zhang, L., Fu, Y., Zhao, T., Feng, J., Ma, Z., Ma, M., Liu, S., Zhou, G., Asanuma, J., Chen, S., Du, M., Davaa, G., Kato, T., Liu, Q., Liu, S., Li, S., Shao, C., Tang, Y. & Zhao, X. (2014b) Satellite-Based Analysis of Evapotranspiration and Water Balance in the Grassland Ecosystems of Dryland East Asia. PLoS ONE, 9, e97295.

28. Xiao, Z.Q., Liang, S., Wang, J.D., Chen, P., Yin, X.J., Zhang, L.Q. & Song, J.L. (2014) Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. Ieee Transactions on Geoscience and Remote Sensing, 52, 209-223.

29. Xie, X., Meng, S., Liang, S. & Yao, Y. (2014) Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy. Hydrol. Earth Syst. Sci., 18, 3923-3936.

30. Xu, T., Bateni, S.M., Liang, S., Entekhabi, D. & Mao, K. (2014) Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites. Journal of Geophysical Research: Atmospheres, 119, 10,780-10,798, doi:10.1002/2014JD021814.

31. Yao, Y., Liang, S., Xie, X., Cheng, J., Jia, K., Li, Y. & Liu, R. (2014a) Estimation of the terrestrial water budget over northern China by merging multiple datasets. Journal of Hydrology, 519, Part A, 50-68.

32. Yao, Y., Liang, S., Zhao, S., Zhang, Y., Qin, Q., Cheng, J., Jia, K., Xie, X., Zhang, N. & Liu, M. (2014b) Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration. Remote Sensing, 6, 880-904.

33. Yao, Y., Liang, S., Li, X., Hong, Y., Fisher, J.B., Zhang, N., Chen, J., Cheng, J., Zhao, S., Zhang, X., Jiang, B., Sun, L., Jia, K., Wang, K., Chen, Y., Mu, Q. & Feng, F. (2014c) Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119, 2013JD020864.

34. Yao, Y., Liang, S., Cheng, J., Lin, Y., Jia, K. & Liu, M. (2014) Impacts of deforestation and climate variability on terrestrial evapotranspiration in subarctic China. Forests, 5, 2542-2560

35. Yuan, W., Li, X., Liang, S., Cui, X., Dong, W., Liu, S., Xia, J., Chen, Y., Liu, D. & Zhu, W. (2014a) Characterization of locations and extents of afforestation from the Grain for Green Project in China. Remote Sensing Letters, 5, 221-229.

36. Yuan, W., Liu, S., Dong, W., Liang, S., Zhao, S., Chen, J., Xu, W., Li, X., Barr, A., Andrew Black, T., Yan, W., Goulden, M.L., Kulmala, L., Lindroth, A., Margolis, H.A., Matsuura, Y., Moors, E., van der Molen, M., Ohta, T., Pilegaard, K., Varlagin, A. & Vesala, T. (2014b) Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat Commun, 5

37. Zhang, X., Liang, S., Zhou, G., Wu, H. & Zhao, X. (2014a) Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sensing of Environment, 152, 318-332.

38. Zhang, Y. & Liang, S. (2014a) Surface radiative forcing of forest disturbances over northeastern China Environmental Research Letters, 9, 024002,doi:10.1088/1748-9326/9/2/024002.

39. Zhang, Y. & Liang, S. (2014b) Changes in forest biomass and linkage to climate and forest disturbances over Northeastern China. Global Change Biology, 20, 2596-2606.

40. Zhang, Y., Liang, S. & Sun, G. (2014b) Mapping forest biomass with GLAS and MODIS data over Northeast China. IEEE Journal in Special Topics in Applied Earth Observations and Remote Sensing, 7, 140-152.