Xiaoyang Zhang

Research focus: Satellite remote sensing of land surface phenology, biomass burning emissions, crop growth progress, and climate change across regional and global scales.

• Nov. 15, 2023: Algorithm allows farmers to monitor crops in real time.
• Nov. 14, 2023: Novel algorithm allows farmers to monitor crops in real time.
• Aug. 9, 2023: SDPB Radio interview regarding our air quality research: Better estimating South Dakota's air quality | SDPB;  "The legacy of ‘should’ in South Dakota" | SDPB.
• Nov. 1, 2022: "SDSU researchers’ work allow for more accurate air quality forecasts" | South Dakota State University.
• July 5, 2020: Interviewed by research writer at South Dakota State University.
• May 24, 2018: Interviewed by research writer at South Dakota State University.
• May 3, 2018: Featured news article.
• June 1, 2017: Interviewed by research writer at South Dakota State University.
• July 26, 2017: Interviewed by EnvironmentalResearchWeb Newswire.
• June 5, 2017: Interviewed by EnvironmentalResearchWeb Newswire.
• Sept. 3, 2015: Interviewed by The Philadelphia Inquirer.
• November 2014: Interviewed by research writer at SDSU and widely reported by news media including United Nations, NOAA, Science News, Live Science and SDSU.
• December 2014: Featured news article in Cooperative Institute for Climate and Satellites-Maryland Earth System Science Interdisciplinary Center, University Maryland.
• May 2010: Interviewed by EnvironmentalResearchWeb Newswire.
• February 2008: Interviewed by the Associated Press.
• November 2007: Interviewed by media including New Scientist Magazine, Wired Magazine, Natural History Magazine and Live Science, separately.
• July 2004: Interviewed by media: Science News, NASA press release, The Atlanta Journal-Constitution and The Republican, separately.

• Cochrane, M. and Zhang, X. Characterizing and monitoring changing fire regimes and the risk of extreme wildfire events in the United States using biophysical models and satellite observations. NASA, SDSU, January 2024-December 2026, $1,504,120.
• Zhang, X., Zhang, H., Maimaitijiang, M. and Yang, Z. Developing an enhanced geospatial tool for operationally monitoring crop-specific crop progress and growth condition in near real time from Geostationary Satellite Observations and Harmonized Landsat-8 and Sentinel-2 Time Series. USDA, Sept. 1, 2023-Aug. 31, 2027, $759,272.
• Zhang, X., Liu, Y., Boyte, S. and Xian, G. Detection of Species-specific Plant Phenology from PlanetScope Time Series for Rangeland Management of the Western United States, NASA, Oct. 1, 2023-Sept. 1, 2025, $299,651.
• Li, F. and Zhang, X. Expansion of RAVE Algorithm for Hourly Biomass Burning Emissions Estimation in Asia and Europe for Air Quality Forecast Applications. NOAA, Sept. 1, 2023-Aug. 31, 2025, $250,000.
• Maimaitijiang, M., Zeng, D. and Zhang, X. AI-powered near real-time crop damage detection using satellite remote sensing. NASA EPSCoR, September 2022-August 2024, $75,000.
• Li, F. and Zhang, X. Enhancement of RAVE emissions algorithm and transition to operations. NOAA, October 2022-June 2024, $235,405.
• Li, F. and Zhang, X. Fire Emissions Reprocessing Activities. NOAA, October 2022-June 2024, $188,324.
• Henebry, G. and Zhang X. Maintenance, Evolution, and Validation of the Global Land Surface Phenology Product from Suomi NPP and JPSS VIIRS Observations. NASA, September 2021-August 2024, $664,845.
• Zhang, X. Investigation Of PlanetScope Time Series Observations for Detecting Land Surface Phenology In The Semiarid Western United States. NASA, January 2022-July 2024, $198,495.
• Gray, G., Zhang, H. and Zhang X. Development of Near Real-Time Land Surface Phenology Product by Fusing Geostationary Satellite and VIIRS Observations in Support of Agriculture and Land Management. NASA, August 2020-July 2023, $521,777.
• Byamukama, E., Yang, Z. and Zhang X. Developing a new geospatial tool for USDA NASS monitoring of near real time crop progress and condition by fusing observations from both polar-orbiting and geostationary satellites. USDA, June 2019-May 2024, $474,000.
• Zhang, X. Global Biomass Burning Emissions Product Maintenance and Refinement – from VIIRS I-Band Fire Detections. NOAA, August 2021-March 2024, $155,870.
• Zhang, X. Reprocess of Global Biomass Burning Emissions Product in Support of Air Quality and Smoke Predictions. NOAA, Aug. 1, 2021-Sept. 30, 2023, $97,418.
• Zhang, X. WF-3 Development and readiness of satellite products for fire and smoke forecasting – A Unified Multi-Scale Biomass Burning Emissions Product. NOAA, May 2020-April 2023, $310,000.
• Cochrane, M., Eshleman, K. and Zhang, X. Effectiveness and monitoring of large-scale carbon-loss mitigation activities in Indonesia’s peatlands. NASA, January 2020-December 2022, $1,442,946.
• Zhang, X. Global Biomass Burning Emissions Product Maintenance and Refinement –Migrating to Cloud System. NOAA, August 2021-March 2023, $41,667.
• Ragi, S. and Zhang, X. Near real-time wildfire smoke detection and monitoring from satellite imagery using artificial intelligence. South Dakota NASA EPSCoR RIG Program, October 2020-September 2022, $75,000.
• Zhang, X. Global Biomass Burning Emissions Product -Maintenance and Refinement. NOAA, August 2020-July 2021, $35,203.
• Henebry, G., Liu, L. and Zhang, X. Maintenance and Refinement of a Global Land Surface Phenology Product from NPP VIIRS for EOS-MODIS Continuity. NASA, April 2018-March 2021, $692,000.
• Cochrane, M. and Zhang, X. Filling A Critical Gap in Indonesia's National Carbon Monitoring, Reporting, and Verification Capabilities for Supporting REDD+ Activities: Incorporating, Quantifying and Locating Fire Emissions from Within Tropical Peat-Swamp Forests. NASA, July 2017-December 2020, $1,497,000.
• Zhang, X. Global Biomass Burning Emissions Product and JPSS-1 Blended Biomass Burning. NOAA, July 1, 2016-June 30, 2020, $230,000.
• Zhang, X. Investigation of GOES-16 Active Wildfire Detections and FRP Measurement for Estimating Biomass Burning Emissions. NOAA/IMSG, Dec. 1, 2018-Sept. 30, 2019, $95,000.
• Gray, J. and Zhang, X. A Multi-Scale Satellite-Based Indicator of Climate Change Impacts on Land-Surface Phenology. NASA, July 2016-June 2019, $434,000.
• Friedl, M. A., Henebry, G. and Zhang, X. Development and Validation of a Global Land Surface Phenology Product from NPP VIIRS for EOS-MODIS Continuity. NASA, November 2014-December 2018, $685,000.
• Zhang, X. Global Biomass Burning Emissions Product (BG-133E-15-SE-1613). NOAA, Sept. 11, 2015-March 31, 2017, $80,000.
• Schaaf, C. B. and Zhang, X. Suomi NPP VIIRS BRDF/Albedo/NBAR Products to Extend the Long Term Consistent MODIS Standard Data Record. NASA, August 2014-July 2017, $592,000.
• Yu, Y. and Zhang, X. Monitoring land surface vegetation phenology from VIIRS. NOAA JPSS Risk Reduction Programs, July 2013-June 2016, $380,000.
• Yu, Y. and Zhang, X. Real-Time Monitoring and Short-term Forecasting of Phenology from GOES-R ABI for the Use in Numerical Weather Prediction Models. NOAA, July 2014-June 2017, $346,000.
• De Beurs K. M., Henebry, G. and Zhang, X. Change in our MIDST: Detection and Analysis of Land Surface Dynamics in North and South America Using Multiple Sensor Data streams. NASA, July 1, 2014-June 30, 2018, $1,100,000.
• Kondragunta, S. and Zhang, X. Develop Near Real Time Biomass Burning Emissions Product Covering the Whole Globe from Polar and Geostationary Satellites for NEMS-GFS-GOCART. NASA-NOAA Joint Center for Satellite Data Assimilation, August 2011-June 2014, $610,000.
• Didan, K. and Zhang, X. Vegetation phenology and enhanced vegetation index products from multiple long term satellite data records. NASA, Aug. 1, 2008-July 31, 2013, $3,441,131.
• Kondragunta S. and Zhang X. Biomass Burning Emissions Product from GOES-R ABI. NOAA Contract No. /Task No. DG133R-07-NC-1616, Mod 12/8407-003, August 2011-July 2012, $53,000.
• Kondragunta, S. and Zhang, X. Global Biomass Burning Emissions Product (GBBEP) from a Constellation of Geostationary Satellites for Operational Use in NWS/NCEP GFS-GOCART. NOAA Contract No. / Task No. DG133R-07-NC-1616, Mod 12/8407-001, August 2011-July 2012, $50,000.
• Kondragunta, S. and Zhang X. Global Biomass Burning Emissions Product (GBBEP) from Multiple Geostationary Satellites. NOAA Contract No./Task No. DG133E-06-CQ-0030/N154-001, July 2010-December 2011, $200,000.
• Kondragunta, S. and Zhang X. Derive Biomass Burning Emissions from GOES WildFire Automated Biomass Burning (WF_ABBA) Fire Products. NOAA Contract No./Task No.  DG133E-06-CQ-0030/T102, April 2005-December 2011, $550,000.
• Goldberg, M. and Zhang X. POES/METOP Product Validation – Assess AVHRR NDVI product through generation and validation of phenology applications. NOAA Contract No./Task No.  DG133E-06-CQ-0030/T140, August 2009-October 2011, $40,000.
• Kondragunta, S. and Zhang, X. Develop GOES-R ABI Aerosol and Trace Gas Emissions Algorithm. NOAA Contract No./Task No. DG133E-06-CQ-0030/T124A, June 2009-December 2011.
• Kondragunta, S. and Zhang, X. Adaptation of the GOES Emissions Algorithm to GOES-R ABI. NOAA SciTech Contract: DG133E-06-CQ-0030/8003-080, June 2007-May 2009.
• Tarpley, D. and Zhang, X. Analysis of AVHRR Climate-Quality Land Products. NOAA Contract No./Task No. DG133E-06-CQ-0030/8003-034, April 2005-December 2007, $150,000.
• Friedl, M.A. and Zhang, X. Global land cover and land cover dynamics from MODIS: algorithm refinement in support of global change research. NASA, January 2004-December 2006, $698,049.
• Friedl, M.A. and Zhang, X. Real Time Estimation and Assimilation of Remotely Sensed Surface Properties for Numerical Weather Prediction Models. NOAA, June 2004-May 2007, $454,987.
• Friedl, M.A. and Zhang, X. Retrieval of Time-Varying Land Cover and Vegetation Properties from MODIS in Support of the NCEP-WRF Land Surface Mode. NOAA, August 2003-July 2004, $100,000.
• Strahler, A.H. and Zhang, X. Land Cover/Land-Cover Change, Albedo, BRDF/Directional Reflectance and Spatial Structure Products from MODIS-N and MODIS-T. NASA, January 1992-December 2003, $9,464,991.
• Zhang, X. Remote Sensing for Estimating Rice Yield in Central China. Supported by Chinese National Eighth Five-Year Plan, 1990-1995.
• Cai, S. and Zhang, X. Studies on Fishery Ecology in the Shallow Lake of Middle and Low Reaches of the Yangtze River. Supported by Eighth Five-Year Plan of the Chinese Academy of Science, 1992-1995.
• Cai, S. and Zhang, X. Remotely Sensed Investigation of Natural Resources and Environment in China and their Dynamics. Supported by Five-Year Plan of the Chinese Academy of Science, 1992-1995.
• Cai, S. and Zhang, X. Studies of Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu. Supported by Seventh Five-Year Plan of the Chinese Academy of Science, 1987-1990.
• Cai, S. and Zhang, X. Relationship between Human Activities and Environmental Changes in Honghu Area. An International Joint Research with Liverpool University (UK), supported by the Chinese National Foundation of Natural Science, 1991-1994.
• Cai, S. and Zhang, X. Studies of Natural Resources & Environment and Adjustment of Ecological Agriculture in Sihui District. Supported by Seventh Five-Year Plan of the Hubei Province, 1984-1990.
• Cai, S. and Zhang, X. Effects of the Three Gorge Project on Lake Environmental Evolution, Potential Gleization, and Creation of Marshes in the North and South of Jingjiang River (Four Lake District). Supported by Chinese National Seventh Five-Year Plan, 1985-1991.
• Cai, S. and Zhang, X. Planning of Agriculture and Ecological Economy in Four Lake District. Supported by Seventh Five-Year Plan of the Hubei Province, 1986-1990.
• Cai, S. and Zhang, X. The Evolution of Jianghan-Dongting Lakes. Supported by Chinese National Foundation of Natural Science, 1984-1987.

Books

1. Zhang, X. (Ed.), 2012. Phenology and Climate Change, ISBN: 978-953-51-0336-3, InTech.

Refereed Journal Papers (English)

1. *Shen, Y., Zhang, X., Gao S., Zhang, H.K.,  Schaaf, C.,  Wang W., Ye, Y., Liu, Y., Tran, K.H., 2024, Analyzing GOES-R ABI BRDF-adjusted EVI2 time series by comparing with VIIRS observations over the CONUS, Remote Sensing of Environment, 302: 113972, DOI:10.1016/j.rse.2023.113972.
2. Román, M.O., Justice, C., Paynter, I., Boucher, P.B., a, Devadiga, S., Endsley, A., Erb, A., Friedl, M.,  Gao, H.,  Giglio, L., Gray, J.M., Hall, D., Hulley, G., Kimball, J., Knyazikhin, Y., Lyapustin, A., Myneni, R.B., Noojipady, P.,  Pu, J., Riggs, G., Sarkar, S., Schaaf, C, Shah, D., Tran, K.H., Vermote, E.,  Wang, D., Wang, Z.,  Wu, A., Ye, Y., Shen, Y., Zhang S., Zhang  S., Zhang, X., Zhao, M., Davidson, C.,  Wolfe, R., 2024, Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products, Remote Sensing of Environment, 302: 113963, DOI:10.1016/j.rse.2023.113963.
3. Pan, L., Bhattacharjee, P.S., Zhang, L., Montuoro, R., Baker, B., McQueen, J., 5, Grell, G.A., McKeen, S.A., Kondragunta, S., Zhang, X., Frost, G.J., Yang, F., and Stajner, I., 2024, Analysis of the GEFS-Aerosols annual budget to better understand  aerosol predictions simulated in the model, Geoscientific Model Development, 17: 431–447, DOI: 10.5194/gmd-17-431-2024.
4. Lou, Z., Wang, F., Peng, D., Zhang, X., Xu, J., Zhu, X., Wang, Y., Shi, Z., Yu, L., Liu, G., Xie, Q., Dou, D., 2023, Combining shape and crop models to detect soybean growth stages, Remote Sensing of Environment, 298: 113827, DOI:10.1016/j.rse.2023.113827.
5. *Tran, K. H., Zhang, X., Ye, Y., Shen, Y., Gao, S., Liu, Y. and Richardson, A. 2023. HP-LSP: a reference of land surface phenology from fused Harmonized Landsat and Sentinel-2 with PhenoCam data. Scientific Data, 10:691, DOI: 10.1038/s41597-023-02605-1.
6. *Shen, Y., Zhang, X., Yang, Z., Ye, Y., Wang, J., Gao, S., Liu, Y., Wang, W., Tran, K.H., Ju, J., 2023, Developing an operational algorithm for near-real-time monitoring of crop progress at field scales by fusing harmonized Landsat and Sentinel-2 time series with geostationary satellite observations,  Remote Sensing of Environment, 296: 113729,  DOI:10.1016/j.rse.2023.113729.
7. Yang, J., Dong, J., Liu, L., Zhao, M., Zhang, X., Li, X., Dai, J., Wang, H., Wu, C., You, N., Fang, S., Pang, Y., He, Y., Zhao, G., Xiao, X., Ge, G., 2023, A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 202:610-636, DOI:10.1016/j.isprsjprs.2023.07.017.
8. *Oliveira, P. VC., Zhang, X., Peterson, B., Ometto, J.P., 2023, Using simulated GEDI waveforms to evaluate the effects of beam sensitivity and terrain slope on GEDI L2A relative height metrics over the Brazilian Amazon Forest, Science of Remote Sensing, 7,100083,  DOI:10.1016/j.srs.2023.100083.
9. Ingty, T., Erb, A., Zhang, X., Schaaf, C., Bawa, K.S., 2023, Climate change is leading to rapid shifts in seasonality in the Himalaya, International Journal of Biometeorology, 67(5):913-925, DOI:10.1007/s00484-023-02465-9.
10. Li, Y., Tong, D., Ma, S., Freitas, S.R., Ahmadov, R., Sofiev, M., Zhang, X., Kondragunta, S., Kahn, R., Tang, Y., Baker, B., Campbell, P., Saylor, R., Grell, G., Li, F., 2023, Impacts of estimated plume rise on PM2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes (Briggs, Freitas, and Sofiev), Atmospheric Chemistry and Physics, 23 (5): 3083-3101. DOI:10.5194/acp-23-3083-2023.
11. Pan, Y., Peng, D., Chen, J.M., Myneni, R. B., Zhang, X., Huete, A.R., Fu, Y.H., Zheng, S., Yan, K., Yu, L., Zhu, P., Shen, M., Ju, W., Zhu, W., Xie, Q., Huang, W., Chen, Z., Huang, J., Wu, C., 2023, Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes, Environmental Research Letters, 18(4):044045. DOI:10.1088/1748-9326/acca34.
12. Yang, F., He, B., Zhou, Y., Li, W., Zhang, X., Feng Q., 2023, Trophic status observations for Honghu Lake in China from 2000 to 2021 using Landsat Satellites, Ecological Indicators, 146:109898. DOI:10.1016/j.ecolind.2023.109898.
13. Rodman,K.C., Andrus, R.A., Carlson, A.R., Carter, T.A., Chapman, T.B., Coop, J.D., Fornwalt, P.J., Gill, N.S., Harvey, B.J., Hoffman, A.E., Kelsey, K.C., Kulakowski, D., Laughlin, D.C., Morris, J.E., Negrón, J.F., Nigro, K.M., Pappas, G.S., Redmond, M.D., Rhoades, C.C., Rocca, M.E., Schapira, Z.H., Sibold, J.S., Stevens‐Rumann, C.S., Veblen, T.T., Wang, J., Zhang, X., Hart, S.J., 2022, Rocky Mountain forests are poised to recover following bark beetle outbreaks, but with altered composition, Journal of Ecology,110(12): 292902949, DOI:10.1111/1365-2745.13999.
14. Tang, Y., Campbell, P. C., Lee, P., Saylor, R., Yang, F., Baker, B., Tong, D., Stein, A., Huang, J., Huang, H.-C., Pan, L., McQueen, J., Stajner, I., Tirado-Delgado, J., Jung, Y., Yang, M., Bourgeois, I., Peischl, J., Ryerson, T., Blake, D., Schwarz, J., Jimenez, J.-L., Crawford, J., Diskin, G., Moore, R., Hair, J., Huey, G., Rollins, A., Dibb, J. and Zhang, X., 2022, Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign, Geoscientific Model Development, 15, 7977–7999, DOI:10.5194/gmd-15-7977-2022.
15. Zhang, X., Shen, Y., Gao, S., Wang, W. and Schaaf, C., 2022, Diverse responses of multiple satellite-derived vegetation greenup onsets to dry periods in the Amazon, Geophysical Research Letters, 49, e2022GL098662. DOI:10.1029/2022GL098662.
16. *Ye, Y., Zhang, X., Shen, Y., Wang, J., Crimmins, T., Scheifinger, H., 2022, An optimal method for validating satellite-derived land surface phenology using in-situ observations from national phenology networks, ISPRS Journal of Photogrammetry and Remote Sensing, 194: 74-90, DOI:10.1016/j.isprsjprs.2022.09.018.
17. *Tran, K.H., Zhang, X., Ketchpaw, A.R., Wang, J., Ye, Y., Shen, Y., 2022, A novel algorithm for the generation of gap-free time series by fusing harmonized Landsat 8 and Sentinel-2 observations with PhenoCam time series for detecting land surface phenology, Remote Sensing of Environment, 282, 113275, DOI:10.1016/j.rse.2022.113275.
18. *Lu, X., Zhang, X., Li, F., Cochrane, M.A., 2022, Improved estimation of fire particulate emissions using a combination of VIIRS and AHI data for Indonesia during 2015–2020, Remote Sensing of Environment, 281,113238, DOI:10.1016/j.rse.2022.113238.
19. *Li, F., Zhang, X., Kondragunta, S., Lu, X., Csiszar, I., Schmidt, C.C., 2022, Hourly biomass burning emissions product from blended geostationary and polar-orbiting satellites for air quality forecasting applications, Remote Sensing of Environment, 281, 113237, DOI: 10.1016/j.rse.2022.113237.
20. Liu, Y., Wu, C., Tian, F., Wang, X., Gamon, J.A., Wong, C.  Zhang, X., Gonsamo, A., Jassal R.S., 2022,  Modeling plant phenology by MODIS derived photochemical reflectance index (PRI), Agricultural and Forest Meteorology, 324, 109095, DOI:10.1016/j.agrformet.2022.109095.
21. Campbell, P.C., Tong, D., Saylor, R., Li, Y., Ma, S., Zhang, X., Kondragunta, S., Li, F., 2022, Pronounced increases in nitrogen emissions and deposition due to the historic 2020 wildfires in the western US, Science of The Total Environment, 839, 156130, DOI:10.1016/j.scitotenv.2022.156130.
22. Wu, C., Peng, J., Ciais, P.,  Peñuelas, J., Wang, H.,  Beguería, S., Black, T.A., Jassal, R.S., Zhang, X., Yuan, W., Liang, E., Wang, X., Hua, H., Liu, R., Ju, W., Fu, Y.H., Ge, Q., 2022, Increased drought effects on the phenology of autumn leaf senescence, Nature Climate Change, 1-7, DOI:10.1038/s41558-022-01464-9.
23. Li, Y., Tong, D., Ma, S., Freitas, S. R., Ahmadov, R., Sofiev, M., Zhang, X., Kondragunta, S., Kahn, R., Tang, Y., Baker, B., Campbell, P., Saylor, R., Grell, G., and Li, F., 2022, Impacts of estimated plume rise on PM2.5 exceedance prediction during extreme wildfire events: A comparison of three schemes (Briggs, Freitas and Sofiev), EGUsphere, DOI:10.5194/egusphere-2022-713.
24. Zhang, L., Montuoro, R., McKeen, S. A., Baker, B., Bhattacharjee, P. S., Grell, G. A., Henderson, J., Pan, L., Frost, G. J., McQueen, J., Saylor, R., Li, H., Ahmadov, R., Wang, J., Stajner, I., Kondragunta, S., Zhang, X., and Li, F., 2022, Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1), Geoscientific Model Development, 15, 5337–5369, DOI:10.5194/gmd-15-5337-2022.
25. Wu, J., Kong, S., Yan, Y., Yao, L., Yan, Q., Liu, D., Shen, G., Zhang, X., Qi, S., 2022, Neglected biomass burning emissions of air pollutants in China-views from the corncob burning test, emission estimation, and simulations, Atmospheric Environment, 278, 119082, DOI:10.1016/j.atmosenv.2022.119082.
26. Wu, J., Kong, S., Yan, Y., Yao, L., Yan, Q., Liu, D., Shen, G., Zhang, X., Qi, S., 2022, The toxicity emissions and spatialized health risks of heavy metals in PM2. 5 from biomass fuels burning, Atmospheric Environment, 284, 119178, DOI: 10.1016/j.atmosenv.2022.119178.
27. Pan, Y., Wang, Y., Zheng, S., Huete, A.R., Shen, M., Zhang, X., Huang, J., He, G., Yu, L., Xu, X., Xie, Q., Peng, D., 2022, Characteristics of Greening along Altitudinal Gradients on the Qinghai–Tibet Plateau Based on Time-Series Landsat Images, Remote Sensing, 14(10), 2408, DOI:10.3390/rs14102408.
28. Lou, Z., Peng, D., Zhang, X., Yu, L., Wang, F., Pan, Y., Zheng, S., Hu, J., Yang, S., Chen, Y., Liu, S., 2022,  Soybean EOS Spatiotemporal Characteristics and Their Climate Drivers in Global Major Regions, Remote Sensing, 14 (8), 1867, DOI:10.3390/rs14081867.
29. An, S., Chen, X.Q., Shen, M.G., Zhang, X., Lang, W.G. and Liu, G.H., 2022, Increasing Interspecific Difference of Alpine Herb Phenology on the Eastern Qinghai-Tibet Plateau. Frontiers in Plant Science. 13:844971, DOI:10.3389/fpls.2022.844971.
30. An, S., Zhang, X., Ren, S., 2022, Spatial Difference between Temperature and Snowfall Driven Spring Phenology of Alpine Grassland Land Surface Based on Process-Based Modeling on the Qinghai–Tibet Plateau, Remote Sensing, 14(5), 1273, DOI: 10.3390/rs14051273.
31. Bela, M.M., Kille, N., McKeen, S.A., Romero‐Alvarez, J., Ahmadov, R., James, E., Pereira, G., Schmidt, C., Pierce, R.B., O’Neill,  S.M., Zhang, X., Kondragunta, S., Wiedinmyer, C., Volkamer, R., 2022, Quantifying carbon monoxide emissions on the scale of large wildfires, Geophysical Research Letters, 49 (3), DOI:10.1029/2021GL095831.
32. Donnelly, A., Yu, R., Jones, K., Belitz, M., Li, B., Duffy, K., Zhang, X., Wang, J., Seyednasrollah, B., Gerst, K.L., Li, D., Kaddoura, Y., Zhu, K., Morisette, J., Ramey, C., Smith, K., 2022, Exploring discrepancies between in situ phenology and remotely derived phenometrics at NEON sites, Ecosphere 13 (1), e3912, DOI:10.1002/ecs2.3912.
33. *Shen, Y., Zhang, X., Yang, Z., 2022, Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data, ISPRS Journal of Photogrammetry and Remote Sensing, 186, 55-69, DOI:10.1016/j.isprsjprs.2022.01.023.
34. Zhang, X., Gao, F., Wang, J., Ye, Y., 2021, Evaluating a spatiotemporal shape-matching model for the generation of synthetic high spatiotemporal resolution time series of multiple satellite data, International Journal of Applied Earth Observation and Geoinformation, 104, DOI:10.1016/j.jag.2021.102545.
35. Li, Y., Tong, D., Ma, S., Zhang, X., Kondragunta, S., Li, F., Saylor, R. 2021, Dominance of Wildfires Impact on Air Quality Exceedances During the 2020 Record‐Breaking Wildfire Season in the United States, Geophysical Research Letters, 48 (21), e2021GL094908, DOI:10.1029/2021GL094908.
36. *Shen, Y., Zhang, X.,  Wang, W., Nemani, R., Ye, Y., and Wang, J., 2021, Fusing Geostationary Satellite Observations with Harmonized Landsat-8 and Sentinel-2 Time Series for Monitoring Field-Scale Land Surface Phenology, Remote Sensing, 13(21), 4465; DOI: 10.3390/rs13214465.
37. *Ye, Y., Zhang, X., 2021, Exploration of global spatiotemporal changes of fall foliage coloration in deciduous forests and shrubs using the VIIRS land surface phenology product, Science of Remote Sensing, 4, 100030, DOI:10.1016/j.srs.2021.100030.
38. Lu, X., Zhang, X., Li, F., Gao, L., Graham, L., Vetrita, Y., Saharjo, B., Cochran, M., 2021, Drainage canal impacts on smoke aerosol emissions for Indonesian peatland and non-peatland fires, Environmental Research Letters, 16(9), 095008, DOI: 10.1088/1748-9326/ac2011.
39. Liu, Y, Mackenzie, C.M., Primack, R.B., Hill, M.J., Zhang, X., Wang, Z., and Schaaf, C.B., 2021, Using remote sensing to monitor the spring phenology of Acadia National Park across elevational gradients, Ecosphere, 12(12), DOI:10.1002/ecs2.3888.
40. Peng, J., Wu, C., Zhang, X., Ju, W., Wang, X., Lu, L., Liu, Y., 2021, Incorporating water availability into autumn phenological model improved China’s terrestrial gross primary productivity (GPP) simulation, Environmental Research Letters,16(9), DOI: 10.1088/1748-9326/ac1a3b.
41. Liu, H., He, B., Zhou, Y., Yang, X., Zhang, X., Xiao, F., Feng, Q., Liang, S., Zhou, X., Fu, C., 2021, Eutrophication monitoring of lakes in Wuhan based on Sentinel-2 data, GIScience and Remote Sensing, 58(5):1-23, DOI:10.1080/15481603.2021.1940738.
42. Gao, F., Zhang, X., 2021, Mapping crop phenology in near real-time using satellite remote sensing: Challenges and opportunities, Journal of Remote Sensing, 8379391, DOI:10.34133/2021/8379391.
43. Lu, X., Zhang, X., Li, F., Cochrane, M.A., Ciren, P., 2021, Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions, Remote Sensing, 13 (2), 196, DOI:10.3390/rs13020196.
44. *Wang, J., Zhang, X., Rodman, K., 2021, Land cover composition, climate, and topography drive land surface phenology in a recently burned landscape: An application of machine learning in phenological modeling, Agricultural and Forest Meteorology, DOI:10.1016/j.agrformet.2021.108432.
45. Wu, C., Wang, J., Ciais, P., Peñuelas, J., Zhang, X., Sonnentag, O.,Tian, F., Wang, X., Wang, H., Liu, R., Fu, Y. and  Ge, Q., 2021, Widespread decline in winds delayed autumn foliar senescence over high latitudes, PNAS, 118 (16), DOI:10.1073/pnas.2015821118.
46. Jia, W., Zhao, S., Zhang, X., S Liu, S., Henebry, G.M., Liu, L., 2021, Urbanization imprint on land surface phenology: The urban–rural gradient analysis for Chinese cities, Global Change Biology, DOI:10.1111/gcb.15602.
47. Liang, L., Henebry, G.M., Liu, L., Zhang, X., Hsu,  LC, 2021, Trends in land surface phenology across the conterminous United States (1982–2016) analyzed by NEON domains, Ecological Applications, DOI:10.1002/eap.2323.
48. Wu, J., Kong, S., Zeng, X., Cheng, Y., Yan, Q., Zheng, H., Yan, Y.,  Zheng, S., Liu, D., Zhang, X., Fu, P., Wang, S., Qi, S.,  2021, First High-Resolution Emission Inventory of Levoglucosan for Biomass Burning and Non-Biomass Burning Sources in China, Environmental Science Technology, DOI:10.1021/acs.est.0c06675.
49. *Peng, D., Wang, Y., Xian, G., Huete, A.R., Huang, W., Shen, M., Wang, F., Yu, L., Liu., L., Xie, Q., Liu, L., Zhang, X., 2021, Investigation of land surface phenology detections in shrublands using multiple scale satellite data, Remote Sensing of Environment, 252, DOI:10.1016/j.rse.2020.112133.
50. *Liu, L., Zhang, X., 2020, Effects of temperature variability and extremes on spring phenology across the contiguous United States from 1982 to 2016, Scientific Reports (10), 17952, DOI:10.1038/s41598-020-74804-4.
51. Wang, X., Wu, C., Zhang, X., Li, Z., Liu, Z., Gonsamo, A. and Ge, Q., 2020, Satellite-observed decrease in the sensitivity of spring phenology to climate change under high nitrogen deposition, Environmental Research Letters, 15, 094055, DOI: 10.1088/1748-9326/aba57f.
52. Li, Y., Tong, D.Q., Ngan, F., Cohen, M.D., Stein, A.F., Kondragunta, S., Zhang, X., Ichoku, C., Hyer, E.J., Kahn, R.A., 2020, Ensemble PM2.5 Forecasting During the 2018 Camp Fire Event Using the HYSPLIT Transport and Dispersion Model, Journal of Geophysical Research: Atmospheres, 125 (15), e2020JD032768, DOI:10.1029/2020JD032768.
53. *Li, F ., Zhang, X., Kondragunta, S., Lu, X., 2020, An evaluation of advanced baseline imager fire radiative power based wildfire emissions using carbon monoxide observed by the Tropospheric Monitoring Instrument across the conterminous United States, Environmental Research Letters, 15, 094049, DOI:10.1088/1748-9326/ab9d3a.
54. *Mfuka, C., Byamukama, E., Zhang, X., 2020, Spatiotemporal characteristics of white mold and impacts on yield in soybean fields in South Dakota, Geo-Spatial Information Science, 23 (2), 182-193, DOI:10.1080/10095020.2020.1712265.
55. Zhang, X., Wang, J., Henebry, G.M., Gao, F., 2020, Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series, ISPRS Journal of Photogrammetry and Remote Sensing, 161, 37-51; DOI:10.1016/j.isprsjprs.2020.01.012.
56. *Li, F ., Zhang, X., Kondragunta, S., Schmidt, C.C., Holmes,  C.D., 2020, A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records, Remote Sensing of Environment, 237: 111600; DOI:10.1016/j.rse.2019.111600.
57. *Wang, J. and Zhang, X., 2020, Investigation of wildfire impacts on land surface phenology from MODIS time series in the western US forests, ISPRS Journal of Photogrammetry and Remote Sensing, 159, 281-295; DOI:10.1016/j.isprsjprs.2019.11.027.
58. Qian, Y., Yang, Z., Di, L., Rahman, Md. S., Tan, Z., Xue, L., Gao, F., Yu, E.G., and Zhang, X., 2019, Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages, Remote Sens. 2019, 11(20), 2439; DOI:10.3390/rs11202439.
59. Peng, D., Zhang, H., Liu, L., Huang, W., Huete, A., Zhang, X., Wang, F., Yu, L., Xie, Q., Wang, C., Luo, S., Li, C., and Zhang, B., 2019, Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables, Remote Sensing 11, 2270; DOI:10.3390/rs11192270.
60. Xiao, J., Chevallier, F., Gomez, C., Guanter, L., Hicke, J.A., Huete, A.R., Ichii, K., Ni, W., Pang, Y., Rahman, A.F., Sun, G., Yuan, W., Zhang, L., Zhang,  X., 2019, Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years, Remote Sensing of Environment, 233, DOI: 10.1016/j.rse.2019.111383.
61. Liu, L., Cao, R., Shen, M., Chen, J., Wang, J., Zhang, X. 2019, How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes? Remote Sensing 11 (18), 2137; DOI:10.3390/rs11182137.
62. *Mfuka, C., Zhang, X., E Byamukama, E., 2019, Mapping and Quantifying White Mold in Soybean across South Dakota Using Landsat Images, Journal of Geographic Information System, 11, 331-346, DOI:10.4236/jgis.2019.113020.
63. *Li, F., Zhang, X., Roy, D.P., Kondragunta, S. 2019, Estimation of biomass-burning emissions by fusing the fire radiative power retrievals from polar-orbiting and geostationary satellites across the conterminous United States, Atmospheric Environment, 211, 274-287, DOI:10.1016/j.atmosenv.2019.05.017.
64. *Lu, X., Zhang, X., Li, F., Cochrane, M.A., 2019,  Investigating Smoke Aerosol Emission Coefficients using MODIS Active Fire and Aerosol Products — A Case Study in the CONUS and Indonesia, Journal of Geophysical Research: Biogeosciences, 124 (6): 1413-1429, DOI:10.1029/2018JG004974.
65. Moon, M., Zhang, X., Henebry, G.M., Liu, L., Gray, J.M., Melaas, E.K., Friedl, M.A., 2019, Long-term continuity in land surface phenology measurements: A comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products, Remote Sensing of Environment, 226, 74-92, DOI:10.1016/j.rse.2019.03.034.
66. Peng, J., Wu, C., Zhang, X., Wang, X., Gonsamo, A., 2019, Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere, Global change biology, 25(6): 2174-2188. DOI:10.1111/gcb.14627.
67. Zhang, X., Liu, L., Henebry, G., 2019, Impacts of land cover and land use change on long-term trend of land surface phenology: a case study in agricultural ecosystems, Environmental Research Letters, 14: 044020.
68. *Yan, D., Zhang, X., Nagai, S., Yu, Y., Akitsu, T., Nasahara, K., Ide, R., Maeda, T., 2019, Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network, International Journal of Applied Earth Observation and Geoinformation, 79: 71-83. DOI:10.1016/j.jag.2019.02.011.
69. Wang, J., Wu, C., Wang, X.,  Zhang, X., 2019, A new algorithm for the estimation of leaf unfolding date using MODIS data over China’s terrestrial ecosystems, ISPRS Journal of Photogrammetry and Remote Sensing, 149:77-90. DOI:10.1016/j.isprsjprs.2019.01.017.
70. *Liu, L., Zhang, X., Yu, Y., Gao, F., Yang, Z., 2018, Real-time Monitoring of Crop Phenology in the Midwestern United States using VIIRS Observations, Remote Sensing, 10(10), 1540; DOI:10.3390/rs10101540.
71. Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon, M., Henebry, G.M., Friedl, M.A., Schaaf, C.B., 2018, Generation and evaluation of the VIIRS land surface phenology product, Remote Sensing of Environment, 216, 212-229, DOI:10.1016/j.rse.2018.06.047.
72. Wang, J., Bhattacharjee, P.S., Tallapragada, V., Lu, C., Kondragunta, S., da Silva, A., Zhang, X., Chen, S., 2018, The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP –Part 1: Model descriptions, Geoscientific Model Development, 11, 2315–2332, DOI:10.5194/gmd-11-2315-2018.
73. Donnelly, A., Liu, L., Zhang, X. and Wingler, A., 2018, Autumn leaf phenology: discrepancies between in situ observations and satellite data at urban and rural sites, International Journal of Remote Sensing, 39 (22), 8129-8150. DOI:10.1080/01431161.2018.1482021.
74. *An, S., Zhang, X., Chen, X., Dong Yan, D. and Henebry, G.M., 2018, An exploration of terrain effects on land surface phenology across the Qinghai–Tibet Plateau using Landsat ETM+ and OLI data, Remote Sensing, 10, 1069; DOI:10.3390/rs10071069.
75. *Li, F., Zhang, X., Kondragunta, S., Csiszar, I., 2018, Comparison of fire radiative power estimates from VIIRS and MODIS observations. Journal of Geophysical Research-Atmosphere, 123(9): 4545-4563. DOI:10.1029/2017JD027823.
76. *Li, F., Zhang, X., Kondragunta, S., Roy, D.P., 2018, Investigation of the fire radiative energy biomass combustion coefficient - a comparison of polar and geostationary satellite retrievals over the Conterminous United States. Journal of Geophysical Research-Biogeoscience, 132, 722-739. DOI:10.1002/2017JG004279.
77. Huang, R., Zhang, X., Chan, D., Kondragunta, S., Russell, A.G., Odman, M.T., 2018, Burned Area Comparisons between Prescribed Burning Permits in Southeastern USA and two Satellite‐derived Products. Journal of Geophysical Research-Atmosphere, 123(9): 4746-4757. DOI:10.1029/2017JD028217.
78. Peng, D., Wu, C., Zhang, X., Yu, L., Huete, A.R., Wang, F., Luo, S., Liu, X., Zhang, H., 2018, Scaling up spring phenology derived from remote sensing images. Agricultural and Forest Meteorology, 256, 207-219. DOI:10.1016/j.agrformet.2018.03.010.
79. Zhang, X., Jayavelu, S., Liu, L., Friedl, M.A., Henebry, G.M., Liu, Y., Schaaf, C.B., Richardson, A.D., and Gray, J., 2018, Evaluation of Land Surface Phenology from VIIRS Data using Time Series of PhenoCam Imagery, Agricultural and Forest Meteorology, 256–257, 137-149. DOI:10.1016/j.agrformet.2018.03.003.
80. *Liu, Y., Wang, Z., Sun, Q., Erb, A.M., Li, Z., Schaaf, C.B., Zhang, X., Román, M.O., Scott, R.L., Zhang, Q., Novick, K.A., Bret-Harte, S., Petroy, S., SanClements, M., 2017, Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record, Remote Sensing of Environment, 201, 256-274. DOI:10.1016/j.rse.2017.09.020.
81. Peng, D., Zhang, X., Zhang, B., Liu, L., Liu, X., Huete, A.R., Huang, W., Wang, S., Luo, S., Zhang, X., Zhang, H., 2017, Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States, ISPRS Journal of Photogrammetry and Remote Sensing, 132:185-198. DOI:10.1016/j.isprsjprs.2017.09.002.
82. *Wang, J., Zhang, X., 2017, Impacts of wildfires on interannual trends in land surface phenology: an investigation of the Hayman Fire, Environmental Research Letters, 12: 05400. DOI:10.1088/1748-9326/aa6ad9.
83. Zhang, X., Liu, L., and Yan, D., 2017, Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data, Journal of Geophysical Research-Biogeoscience, 122, DOI:10.1002/2017JG003811.
84. *Krehbiel, C., Zhang, X. and Henebry, G.M., 2017, Impacts of Thermal Time on Land Surface Phenology in Urban Areas, Remote Sensing, 9, 499, DOI:10.3390/rs9050499.
85. *Yan, D., Zhang, X., Yu, Y. and Guo, W., 2017, Characterizing land cover impacts on the responses of land surface phenology to the rainy season in the Congo Basin, Remote Sensing, 9(5), 461; DOI: 103390/rs9050461.
86. Peng, D., Zhang, X., Wu, C., Huang, W., Gonsamo, A., Huete, A.R. Didan, K., Tang, B.,Liu, X., Zhang, B., 2017, Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States, Agricultural and Forest Meteorology, 242: 33–46. DOI:10.1016/j.agrformet.2017.04.009.
87. Wang, Z., Schaaf, C.B., Sun, Q., Kim, J., Erb, A.M., Gao, F., Román, M.O., Yang, Y., Petroy, S., Taylor, J.R., Masek, J.G., Morisette, J.T., Zhang, X., Papuga, S.A., 2017, Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product, International Journal of Applied Earth Observation and Geoinformation, 59, 104–117. DOI:10.1016/j.jag.2017.03.008.
88. *Liu, L., Zhang, X., Yu, Y., Guo, W., 2017, Real-time and short-term predictions of spring phenology in North America from VIIRS data, Remote Sensing of Environment, 194, 89–99, DOI:10.1016/j.rse.2017.03.009.
89. Peng, D., Wu, C., Li, C., Zhang, X., Liu, Z., Ye, H., Luo, S., Liu, X., Hu, Y. Fang, B., 2017, Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations, Ecological Indictors, 77, 323-336, DOI:10.1016/j.ecolind.2017.02.024.
90. Liu , Y., Hill, M.J., Zhang, X., Wang, Z., Richardson, A., Hufkens, K., Filippa, G., Baldocchi, D. D., Ma, S., Verfaillie, J., Schaaf, C.B., 2017, Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales, Agricultural and Forest Meteorology, 237–238, 311–325, DOI:10.1016/j.agrformet.2017.02.026.
91. Zhang, X., Wang, J., Gao, F., Liu, Y., Schaaf, C.B., Friedl, M.A., Yu, Y., Jayavelu, S., Gray, J., Liu, L., Yan, D. and Henebry, G.M., 2017, Exploration of Scaling Effects on Coarse Resolution Land Surface Phenology, Remote Sensing of Environment, 190, 318-330, DOI:10.1016/j.rse.2017.01.001.
92. *Liu, L. Zhang, X., Yu, Y. and Donnelly, A., 2017. Detecting spatiotemporal changes of peak foliage coloration in deciduous and mixedforests across the Central and Eastern United States, Environmental Research Letters, 12 024013, DOI:10.1088/1748-9326/aa5b3a.
93. Gao F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D.M., Prueger, J.H., 2017, Toward mapping crop progress at field scales using Landsat and MODIS imagery, Remote Sensing of Environment,  188, 9–25, DOI:10.1016/j.rse.2016.11.004.
94. *Yan, D., Zhang, X., Yu, Y., Guo, W. and Hanan, N. P., 2016, Characterizing land surface phenology and responses to rainfall in the Sahara Desert, Journal of Geophysical Research- Biogeosciences, 121, DOI:10.1002/2016JG003441.
95. Peng, D., Wu, C., Zhang, B., Huete, A., Zhang, X., Sun, R., Lei, L., Huang, W., Liu, L., Liu, X., Li, J., Luo, S., Fang, B., 2016, The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data. PLOS ONE, 11(6), DOI:10.1371/journal.pone.0158173.
96. Liang, L., Schwartz, M., Zhang, X., 2016, Mapping Temperate Vegetation Climate Adaptation Variability Using Normalized Land Surface Phenology, Climate, 4(2), 24, DOI:10.3390/cli4020024.
97. *Yan, D., Zhang, X., Yu, Y. and Guo, W., 2016, A comparison of tropical rainforest phenology retrieved from geostationary (SEVIRI) and polar-orbiting (MODIS) sensors across the Congo Basin, IEEE Transactions On Geoscience and Remote Sensing, 54(8): 4867 – 4881, DOI:10.1109/TGRS.2016.2552462.
98. Zhang, X., and Zhang, Q. 2016, Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations, ISPRS Journal of Photogrammetry and Remote Sensing 114, 191-205, DOI:10.1016/j.isprsjprs.2016.02.010.
99. *Liu, L., Zhang, X., Donnelly, A. and Liu, X. ,2016, Interannual variations in spring phenology and their response to climate change across the Tibetan Plateau from 1982 to 2013, International Journal of Biometeorology, DOI:10.1007/s00484-016-1147-6.
100. Wu, M., Zhang, X, Huang, W., Niu, Z., Wang,C., Li, W. and Hao, P., 2015, Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring, Remote Sensing, DOI:10.3390/rs71215826.
101. Liang, L., Zhang, X., 2015. Coupled Spatiotemporal Variability of Temperature and Spring Phenology in the Eastern U.S., International Journal of Climatology, DOI:10.1002/joc.4456.
102. Yue, X., Unger, N., Keenan, T. F., Zhang, X., and Vogel, C. S. 2015. Probing the past 30-year phenology trend of US deciduous forests. Biogeosciences, 12, 4693–4709, DOI:10.5194/bg-12-4693-2015.
103. Senthilnath, J., Kumar, D., Benediktsson, J.A., Zhang, X., 2015. A novel hierarchical clustering technique based on splitting and merging. International Journal of Image and Data Fusion, DOI:10.1080/19479832.2015.1053995.
104. Zhang, X., 2015. Reconstruction of a Complete Global Time Series of Daily Vegetation Index Trajectory from Long-term AVHRR Data. Remote Sensing of Environment, 156, 457-472, DOI:10.1016/j.rse.2014.10.012.
105. Zhang, Q., Cheng, Y.B., Lyapustin, A.I., Wang, Y., Zhang, X., Suyker, A., Verma, S., Shuai, Y., Middleton, E.M., 2015. Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance? Agricultural and Forest Meteorology, 200, 1–8, DOI:10.1016/j.agrformet.2014.09.003.
106. Fan, B., Guo, L., Li, N., Chen, J., Lin, H., Zhang, X., Shen, M., Rao, Y., Wang, C., Ma, L., 2014. Earlier vegetation green-up has reduced spring dust storms. Scientific Reports, 4: 6749, DOI:10.1038/srep06749.
107. Xiao J., Ollinger, S.V., Frolking, S., Hurtt, G.C., Hollinger, D.Y., Davis, K.J., Pan, Y., Zhang, X., Deng, F., Chen, J., Baldocchi, D.D., Law, B.E., Arain, M.A., Desai, A.R., Richardson, A.D., Sun, G., Amiro, B., Margolis, H., Gu, L., Scott, R.L., Blanken, P.S., Suyker, A.E., 2014. Data-driven diagnostics of terrestrial carbon dynamics over North America. Agricultural and Forest Meteorology, 197, 142–157, DOI:10.1016/j.agrformet.2014.06.013.
108. Zhang, X., Kondragunta, S. and Roy, D.P., 2014. Interannual variation in biomass burning and fire seasonality derived from geostationary satellite data across the contiguous United States from 1995 to 2011. Journal of Geophysical Research-Biogeosciences, DOI:10.1002/2013JG002518.
109. Zhang, F., Wang, J. Ichoku, C., Hyer, E., Yang, Z., Ge, C., Su, S., Zhang, X., Kondragunta, S., Kaiser, J., Wiedinmyer, C. and da Silva, A., 2014. Sensitivity of mesoscale modeling of smoke direct radiative effect to the emission inventory:  A case study in northern sub-Saharan African region. Environmental Research Letters, 9, 075002, DOI:10.1088/1748-9326/9/7/075002.
110. Zhang, X., Tan, B. and Yu, Y. 2014. Interannual variation and trends in global land surface phenology derived from enhanced vegetation index during 1982-2010. International Journal of Biometeorology, 58(4), 547-564, DOI:10.1007/s00484-014-0802-z.
111. Liang, L., Schwartz, M.D., Wang, Z., Gao, F., Schaaf, C.B., Tan, B., Morisette, J.T. and Zhang, X., 2014. A cross comparison of spatiotemporally enhanced springtime phenological measurements from satellites and ground in a northern U.S. mixed forest.  IEEE Transactions On Geoscience and Remote Sensing, DOI:10.1109/TGRS.2014.2313558.
112. Shuai, Y., Schaaf, C., Zhang, X., Strahler, A., Roy, D., Morisette, J., Wang, Z., Nightingale, J., Nickeson, J., Richardson, A.D., Xie, D., Wang, J., Li, X., Strabala, K., Davies, J.E., 2013. Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics. International Journal of Remote Sensing, 34(16): 5997-6016, DOI:10.1080/01431161.2013.803169.
113. Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., Huang,H-C, 2012.  Near Real Time Global Biomass Burning Emissions Product from Geostationary Satellite Constellation. Journal of Geophysical Research-Atmosphere, DOI: 10.1029/2012JD017459.
114. Zhang, X., 2012. Impacts of global climate change on the plant seasonality of our planet. Overseas Scholars, 1:35-45.
115. Zhang, X., Goldberg, M.D., Yu, Y., 2012. Prototype for monitoring and forecasting fall foliage coloration in real time from satellite data. Agricultural and Forest Meteorology, 158: 21-29, DOI:10.1016/j.agrformet.2012.01.013.
116. Kovalskyy, V., Roy, D. P., Zhang, X., Ju, J., 2012. The suitability of multi-temporal Web-Enabled Landsat Data (WELD) NDVI for phenological monitoring – a comparison with flux tower and MODIS NDVI. Remote Sensing Letters, 3(4): 325–334, DOI:10.1080/01431161.2011.593581.
117. Zhang, X. Kondragunta, S., and Quayle, B., 2011. Estimation of biomass burned areas using multiple-satellite-observed active fires. IEEE Transactions on Geosciences and Remote Sensing, 49: 4469-4482, DOI:10.1109/TGRS.2011.2149535.
118. Zhang, X. and Goldberg, M., 2011. Monitoring Fall Foliage Coloration Dynamics Using Time-Series Satellite Data. Remote Sensing of Environment, 115 (2): 382-391, DOI:10.1016/j.rse.2010.09.009.
119. Yang, E.S., Christopher, S.A., Kondragunta, S. and Zhang, X., 2010. Use of hourly GOES fire emissions in a Community Multiscale Air Quality (CMAQ) model for improving surface particulate matter predictions. Journal of Geophysical Research, 116, D04303, DOI:10.1029/2010JD014482.
120. Zhang, X., Goldberg, M., Tarpley, D., Friedl, M., Morisette, J., Kogan, F., Yu, Y., 2010. Drought-induced Vegetation Reduction in Southwestern North America. Environmental Research Letters, 5 (2010) 024008, DOI:10.1088/1748-9326/5/2/024008.
121. Ganguly, S., Friedl, M.A., Tan, B., Zhang, X., and Verma, M., 2010. Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product. Remote Sensing of Environment, 114(8), 1805-1816, DOI:10.1016/j.rse.2010.04.005.
122. Christopher, S.A., Gupta, P., Nair, U., Jones, T.A., Kondragunta, S., Wu, Y. L., Hand, J., Zhang, X, 2009. Satellite Remote Sensing and Mesoscale Modeling of the 2007 Georgia/Florida Fires. Journal of Selected Topics in Earth Observations and Remote Sensing, 2:163 – 175, DOI:10.1109/JSTARS.2009.2026626.
123. Zhang, X., Friedl, M.A., Schaaf, C.B., 2009. Sensitivity of vegetation phenology detection to the temporal resolution of satellite data. International Journal of Remote Sensing, 30(8): 2061 – 2074, DOI:10.1080/01431160802549237.
124. Zhang, X., Kondragunta, S., Schmidt, C., Kogan, F., 2008. Near real-time monitoring of biomass burning particulate emissions (PM2.5) using multiple satellite instruments. Atmospheric Environment, 42 (29), 6959-6972, DOI: 10.1016/j.atmosenv.2008.04.060.
125. Al-Saadi, J., Soja, A., Pierce, B., Kittaka, C., Emmons, L., Kondragunta, S., Zhang, X., Wiedinmyer, C., Schaack, T. Szykman, J., 2008. Evaluation of Near-Real-Time Biomass Burning Emissions Estimates Constrained by Satellite Active Fire Detections. Journal of Applied Remote Sensing, version 2, DOI:10.1117/1.2948785.
126. Zhang, X., Kondragunta, S., 2008. Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product. Remote Sensing of Environment, 112 (6), 2886-2897. DOI:10.1016/j.rse.2008.02.006.
127. Zhang, X., Tarpley, D., Sullivan, J. 2007. Diverse responses of vegetation phenology to a warming climate. Geophysical Research Letters, 34, L19405, DOI:10.1029/2007GL031447.
128. Zhang, X., Friedl, M.A., Schaaf, C.B., 2006. Global vegetation phenology from MODIS: evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research, Volume 111, G04017, DOI:10.1029/2006JG000217.
129. Zhang X., Kondragunta, S., 2006. Estimating forest biomass in the USA using generalized allometric model and MODIS product data. Geophysical Research Letters, 33: L09402, DOI: 101029/2006GL025879.
130. Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, Zhang, X., O’Neil, S. and Wynne, K.K., 2006. Estimating emissions from fires in North America for air quality modeling. Atmospheric Environment, 40: 3419-3432, DOI:10.1016/j.atmosenv.2006.02.010.
131. Zhang, X., Friedl, M.A., Schaaf, C.B. and Strahler, A.H., Liu, Z., 2005. Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research-Atmospheres, 110, D12103. DOI:10.1029/2004JD005263.
132. Zhang, X., Friedl, M.A., Schaaf, C. B., Strahler, A.H. and Schneider, A., 2004. The footprint of urban climates on vegetation phenology. Geophysical Research Letter, Vol. 31, L12209, DOI:10.1029/2004GL020137.
133. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., 2004. Climate controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data. Global Change Biology, 10:1133-1145, DOI:10.1111/j.1529-8817.2003.00784.x.
134. Tian Y, Dickinson, R.E., Zhou, L., Zeng, X., Dai, Y., Myneni, R.B., Knyazikhin, Y., Zhang, X., Friedl, M., Yu, II., Wu, W., Shaikh, M. 2004. Comparison of seasonal and spatial variations of leaf area index and fraction of absorbed photosynthetically active radiation from Moderate Resolution Imaging Spectroradiometer (MODIS) and Common Land Model. Journal of Geophysical Research-Atmospheres, 109 (D1): Art. No. D01103, DOI:10.1029/2003JD003777.
135. Penuelas, J., Filella, I., Zhang, X., LLorens, L., Ogaya, R., Lloret, F., Comas, P., Estiarte, M., Terradas, J., 2004. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytologist, 161(3): 837-846, DOI:10.1111/j.1469-8137.2004.01003.x.
136. Zhang, X., Schaaf, C. B., Friedl, M. A., Strahler, A. H., Gao F., Hodges, J. F., Reed, B. C., Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3), 471-475, DOI:10.1016/S0034-4257(02)00135-9.
137. Zhang, X., Drake, N. A., and Wainwright, J. 2002. Scaling land-surface parameters for global scale soil-erosion estimation. Water Resources Research, 38(9), 191-199, DOI:10.1029/2001WR000356.
138. Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J. P. et al. 2002. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing of Environment, 83(1-2), 135-148, DOI:10.1016/S0034-4257(02)00091-3.
139. Friedl, M. A, McIver, D. K, Hodges, J. C., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schnieder, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C. B. 2003. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1-2), 287-302, DOI:10.1016/S0034-4257(02)00078-0.
140. Yun Du, Y.,  Cai, S., Zhang, X. and Zhao, Y. 2001. Interpretation of the environmental change of Dongting Lake, middle reach of Yangtze River, China, by 210Pb measurement and satellite image analysis. Geomorphology, 41(2-3), 171-181, DOI:10.1016/S0169-555X(01)00114-3.
141. Zhang, X., Drake, N. A., Wainwright, J. and Mulligan, M. 1999. Comparison of slope estimates from low resolution DEMs: scaling issues and a fractal method for their solution. Earth Surface Processes and Landforms, 24(9), 763-779, DOI:10.1002/(SICI)1096-9837(199908)24:9<763::AID-ESP9>3.0.CO;2-J.
142. Zhang, X. 1998. On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: case study of the Honghu Lake, PR China. International Journal of Remote Sensing, 19(1), 11-20, DOI:10.1080/014311698216396.

Refereed Book Chapters (English)

1. Zhang, X., 2018. Land Surface Phenology: Climate Data Record and Real-Time Monitoring. In Liang, S. (ed.), Comprehensive Remote sensing: Terrestrial ecosystems, ELSE, Vol 3: 35-52. DOI: 10.1016/B978-0-12-409548-9.10351-3.
2. Zhang, X., Ni-meister, W., 2014. Remote sensing of Forest biomass. In Hanes, J. (ed), Biophysical Applications of Satellite Remote Sensing, Springer, New York, pp 63-98. DOI: 10.1007/978-3-642-25047-7_3.
3. Zhang, X., Friedl, M.A., Tan, B., Goldberg, M.D. and Yu, Y., 2012. Long-Term Detection of Global Vegetation Phenology from Satellite Instruments. In X. Zhang (Ed.), Phenology and Climate Change, ISBN: 978-953-51-0336-3, InTech.
4. Zhang, X., Drake, N. A., Wainwright, J., 2013. Spatial Modelling and Scaling Issues. In Wainwright, J. and Mulligan, M. (eds.), Environmental Modeling: Finding Simplicity in Complexity (Second Edition), John Wiley and Sons, Chichester.
5. Friedl, M.A., Zhang, X., Strahler, A.H, 2011. Characterizing global land cover type and seasonal land cover dynamics at moderate spatial resolution using MODIS. In Ramachandran, B., Justice, C. and Abrams, M. (Eds), Land Remote Sensing and Global Environmental Change: NASA’s Earth Observing System and the Science of ASTER and MODIS, Springer, New York, pp. 709-721.
6. Zhang, X., Drake, N. A. and Wainwright, J. 2004. Scaling issues in environmental modeling. In Wainwright, J. and Mulligan, M. (eds.), Environmental Modeling: Finding Simplicity in Complexity, John Wiley and Sons, Chichester, pp. 319-334.
7. Drake, N.A., Zhang, X., Symeonakis, E., Patterson, G., Bryant, A.R. 2004. Near Real-time Modeling of Regional scale soil erosion using AVHRR and METEOSAT data: a tool for monitoring the impact of sediment yield on the biodiversity of Lake Tanganyika. In Kelly, R., Drake, N., and Barr, S. (eds.), Spatial Modelling of the Terrestrial Environment. John Wiley and Sons, Chichester, pp. 157-174.
8. Drake, N. A., Zhang, X., Berkhout, E., Bonifacio, R., Grimes, D., Wainwright, J. and Mulligan, M. 1999. Modeling soil erosion at global and regional scales using remote sensing and GIS techniques. In Atkinson, P. M. and Tate, N. J. (eds.), Advances in Remote Sensing and GIS Analysis, John Wiley and Sons, Chichester, pp. 241-261.
9. Zhang, X. 1992. Study on the swamping of lakes and lowland in Jianghan and Dongting plain by using remote sensing techniques. In Embleton, C. (ed.), Geo-hazards and their Reduction, Science Press, Beijing, pp. 61-69.
10. Zhang, X. and Cai, S. 1994. Study on wetland and its dynamic changes in Jianhan plain by using remote sensing. In Wetland Environment and Peatland Utilization: Wetland Environment and Peatland Utilization, Changchun, China, Jilin People's Publishing House, Changchun, China, pp. 296-302.

Refereed Journal Papers (Chinese)

1. Liu, L., Pang, Y., Zhang, X., Solberg, S., Fan,W., Li, Z., Li, M., 2012. Monitoring Forest Growth Disturbance Using Time Series MODIS EVI Data. Forest Science, China, 28: 54-62.
2. Zhang, X., Li, J., 1995. The derivation of a reflectance model for the estimation of leaf area index using perpendicular vegetation index. Remote Sensing Technology and Application, 10(3):13-18.
3. Zhang, X., Du, Y. and Cai, S. 1995. An analysis on evolutional tendency of Dongting Lake. Resources and Environment in the Yangtze Valley, China, 4(1), 64-69.
4. Zhang, X., Cai, S. and Sun, S. 1994. Evolution of Dongting Lake since Holocene, Limnology Science, China, 16(1).
5. Yu, L., Xu, Y, Cai, S. and Zhang, X. 1993. The application of GIS to a lake environmental change study. Limnology Science, China, 15(4).

Refereed Journal Book Chapters (Chinese)

1. Zhang, X., Huang, J., Li, J., Chen, S. and Liu, K. 1995. Remote sensing for modeling rice yield in Hubei province, PRC. In Zhou, R. et al (ed.), Rice Yield Estimation Using Remote Sensing in China, Science Press, Beijing.
2. Zhang, X. 1995. The relationship between biomass of submerged vegetation and spectral properties. In Chen, Y. and Xu Y. (eds.), Hydrobiology and Resource Exploitation in the Honghu Lake, Sciences Press, Beijing.
3. Zhang, X. 1995. Investigating biomass of submerged vegetation using PCA analysis.  In Chen, Y. and Xu Y. (eds.), Hydrobiology and Resource Exploitation in the Honghu Lake, Sciences Press, Beijing.
4. Zhang, X. and Cai, S. 1994. The effect of the Three Gorge Project on Dongting Lake. In Pu, P. (ed.), The Effect of Three Gorge Project on The Environment of Lakes And Wetland In The Middle Reaches of Yangtze River, Sciences Press, Beijing.
5. Zhang, X., Li, R., Chen, S. and Liu, K. 1993. Exploring a remote sensing model of rice yield estimation. In Chen, S. (ed.), Estimation of Wheat, Maize and Rice Yield Using Remote Sensing Techniques, Chinese Science and Technology Press, Beijing.
6. Zhang, X., Li, R. and Du, Y. 1993. Sampling frame for rice yield estimation based on the remote sensing techniques in Jiangli County. In Chen, S. (ed.), Estimation of Wheat, Maize and Rice Yield Using Remote Sensing Techniques, Chinese Science and Technology Press, Beijing.
7. Liu, K., Yang, B. and Zhang, X. 1993. Numerical simulation of rice yield. In Chen, S. (ed.), Estimation of Wheat, Maize and Rice Yield Using Remote Sensing Techniques, Chinese Science and Technology Press, Beijing.
8. Zhang, X. and Cai, S. 1991. Recent change of Dongting Lake. In Chinese Association of Geomorphology and Quaternary (ed.), Research Progress of Geomorphology and Quaternary, Survey and Drawing Press, Beijing.
9. Zhang, X. and Cai, S. 1991. Analysis of the swamping process and the dynamic change in emergent vegetation on the basis of remote sensing data. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing.
10. Zhang, X. and Cai, S. 1991. Estimation of the emergent vegetation biomass in Lake Honghu by means of remote sensing. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing.
11. Cai, S. and Zhang, X. 1991. Co-ordinate development of fishery and agriculture in the Honghu basin. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing.
12. Cai, S., Yi, C., and Zhang, X. 1991. Process of swamping and pedogenesis in Honghu Lake and utilization. In Honghu Research Group, Institute of Hydrobiology, Academia Sinica (ed.), Studies on Comprehensive Exploitation of Aquatic Biological Productivity and Improvement of Ecological Environment in Lake Honghu, China Ocean Press, Beijing.
13. Cai, S., Zhang, X., Zhou, S. and Wang, K. 1989. Map of the change of lakes in Sihu district. In Atlas of Ecosystems and Environments in the Three Gorges of the Yangtze River, Science Press, Beijing.
14. Cai, S. and Zhang, X. 1989. Map of the change of Dongting Lake. In Atlas of Ecosystems and Environments in the Three Gorges of the Yangtze River, Science Press, Beijing.
15. Cai, S., Guan, Z, Zou, J. Zhang, X., Yi, L. and Yang H. 1987. Effects of the Three Gorge project on lake environmental evolution and potential gleization and creation of marshes in the north and south of Jingjiang River. In Impacts of the Three Gorges Project on Ecosystems and Environment and Possible Countermeasures, Science Press, Beijing.