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Xiaoyang Zhang

Xiaoyang Zhang

Title

Distinguished Professor/ Co-Director GSCE/ Senior Research Scientist

Office Building

Wecota Hall

Office

115E

Mailing Address

Wecota Hall 115E
Geospatial Science Center of Excellence-Box 0506B
University Station
Brookings, SD 57007

Biography

I am a distinguished professor at Department of Geography & Geospatial Sciences and Co-Director/senior scientist at the Geospatial Sciences Center of Excellence. Prior to joining SDSU in 2013, I was a Research Assistant Professor with the Institute of Hydrobiology, Chinese Academy of Sciences (CAS), China (1984-1988); a Research Associate Professor with the Institute of Geodesy and Geophysics, CAS, China (1988-1995); a Research Associate and Research Assistant Professor with the Department of Geography, Boston University, Boston, MA, USA (1999 to 2005). As a Senior Research Scientist in the Earth Resources Technology (2005-2012) and a visiting Associate Research Scientist in the University of Maryland (2012-2013), I worked at the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data, and Information Service, Center for Satellite Applications and Research (STAR), Camp Spring, MD, USA. Moreover, I am a journal editor of “Earth Interaction" and "International Journal of Applied Earth observation and Geoinformation", as well as a member of Editorial Board of “Remote Sensing of Environment” and "Remote Sensing Applications: Society and Environment".

Education

Ph.D. Department of Geography, King's College London, University of London, London, 02/1995-05/1999.

M.Sc. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 09/1988-08/1991.

B.Sc. Department of Geography, Peking University, Beijing, 09/1980-07/1984.

Academic Interests

Research interests include the developments of remote sensing algorithms and global products for investigating biomass burning emissions, land surface phenology, land cover and land use change, and climate-terrestrial ecosystem interaction.

Academic Responsibilities

80% research, 10% service, 10% teaching

Current courses taught:
• GEOG 484-484L/584-584L Remote Sensing
• GEOG 485-485L/585-585L Quantitative Remote Sensing
• GSE/GEOG 760–S01 Advanced Methods in Geospatial Modeling: Computation for Remote Sensing Analysis and Product Generation
• GSE 898D Dissertation Course
• GSE 790 Geospatial Science and Engineering Seminar Course

Academic Advising
• Research scientist/Postdoctoral Monitoring
Current:
1) Dr. Fanjun Li (08/2018 --), after gaining his PhD degree from GSCE at SDSU, promoted to be Research Assistant Professor in June 2022
2) Dr. Yongchang Ye (06/2019--), after obtaining his PhD degree from Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences
3) Dr. Shuai Gao (12/2021--), who was a research scientist at Aerospace Information Research Institute, Chinese Academy of Sciences
4) Dr. Yuxia Liu (03/2022--), after obtaining her PhD degree from University of Technology Sydney
5) Dr. Shuai An (1/2023--), who was a lecture at Beijing Union University

Past:
1) Dr. Xiaoman Lu (May-June 2022) after obtaining his PhD degree at SDSU. She is a postdoc research scientist at University of Illinois, Urbana-Champaign
2) Dr. Jianmin Wang worked from November 2020-October 2022 after obtaining his PhD degree at SDSU. He is a research scientist at Purdue University
3) Dr. Linling Liu Worked from August 2014-January 2019 after gaining her PhD degree from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences. She is a Remote Sensing Specialist at Woods Institute for the Environment, Stanford University
4) Dr. Dong Yan worked from June 2014-June 2017 and he is now a research associate at University of Arizona
5) Dr. J. Senthilnath worked from February 2015-November 2016 and he is now a research fellow at Nanyang Technological University, Singapore

• PhD Student Advising and Mentoring
Current:
1) Ruixuan Li, enrolled in GSE PhD program in Fall 2023 (in progress), support with GRA
2) Yu Shen, enrolled in GSE PhD program in Fall 2019 (in progress), support with GRA
3) Khuong Tran, enrolled in GSE PhD program in June 2021 (in progress), support with GRA
4) Naeem Abbas Malik, enrolled in GSE PhD program in Fall 2021 (in progress), support with GRA
5) Pedro Valle De Carvalho E Oliveira, enrolled in GSE PhD program in Fall 2017 (in progress), support with GRA
6) Juliana Fajardo Rueda, enrolled in GSE PhD program in Fall 2021 (in progress) , support with GRA

Graduated:
1) Xiaoman Lu, enrolled in GSE PhD program in Fall 2017 and graduated in May 2022
2) Jianmin Wang, enrolled in GSE PhD program in Fall 2015 and graduated in October 2020
3) Confiance Mfuka, Plant Science, and graduated in Summer 2019
4) Fangjun Li, enrolled GSE PhD program in Spring 2014 and graduated in Summer 2018

Professional Memberships

- American Geophysical Union (AGU)
- Association of American Geographers (AAG)
- International Association of Wildland Fire (IAWF)
- American Meteorological Society (AMS)
- International Association for Landscape Ecology (IALE)

Awards and Honors

Media Coverage of Research
• 11/15/2023: Algorithm allows farmers to monitor crops in real time: https://www.lifetechnology.com/blogs/life-technology-science-news/algorithm-allows-farmers-to-monitor-crops-in-real-time#:~:text=The%20algorithm%20uses%20data%20from%20various%20sources%20such,can%20identify%20patterns%20and%20trends%20in%20the%20data.
• 11/14/2023: Novel algorithm allows farmers to monitor crops in real time https://www.sdstate.edu/news/2023/09/novel-algorithm-allows-farmers-monitor-crops-real-time
• 08/09/2023: SDPB Radio interview regarding our air quality research: Better estimating South Dakota's air quality | SDPB; The legacy of ‘should’ in South Dakota | SDPB
• 11/01/2022: SDSU researchers’ work allow for more accurate air quality forecasts: SDSU researchers’ work allow for more accurate air quality forecasts | South Dakota State University (sdstate.edu)
• 07/05/2020: Interviewed by Research Writer in South Dakota State University
(https://www.sdstate.edu/news/2020/07/new-satellite-data-improve-burning-emissions-model )
• 05/24/2018: Interviewed by Research Writer in South Dakota State University
(http://www.newswise.com/articles/satellite-sensors-track-spring-greenup%2C-fall-leaf-off;
https://www.sdstate.edu/news/2018/05/satellite-sensors-track-spring-greenup-growing-season)
• 05/03/2018: featured news article (https://sciencetrends.com/comparing-viirs-and-phenocam-land-surface-phrenology/)
• 06/01/2017: Interviewed by Research Writer in South Dakota State University
(https://www.sdstatefoundation.org/news/satellite-sensors-track-spring-greenup-growing-season)
• 07/26/2017: Interviewed by EnvironmentalResearchWeb Newswire (http://environmentalresearchweb.org/cws/article/news/69988)
• 06/05/2017: Interviewed by EnvironmentalResearchWeb Newswire (http://environmentalresearchweb.org/cws/article/news/68993)
• 09/03/2015: Interviewed by The Philadelphia Inquirer
• 11/2014: Interviewed by Research Writer in SDSU and widely reported by new media including United Nation, NOAA, Science News, Live Science, and SDSU.
• 12/2014: Featured news article in Cooperative Institute for Climate & Satellites-Maryland Earth System Science Interdisciplinary Center, University Maryland
• 5/2010: Interviewed by EnvironmentalResearchWeb Newswire.
• 2/2008: Interviewed by the Associated Press.
• 11/2007: Interviewed by media including New Scientist Magazine, Wired Magazine, Natural History Magazine, and Live Science, separately.
• 7/2004 Interviewed by media: Science News (newsmagazine), NASA press release, The Atlanta Journal-Constitution, and The Republican, separately.

Grants

(1) Pending Projects
1. **

(2) Current projects

1. Characterizing and monitoring changing fire regimes and the risk of extreme wildfire events in the United States using biophysical models and satellite observations, NASA, $1,504,120, 01/2024-12/2026, PI at SDSU: X. Zhang ($297,156), Project PI: Mark Cochrane (UMD Appalachian Laboratory).
2. 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, $759,272, 09/01/2023-08/31/2027, PI X. Zhang (SDSU), CoI, H. Zhang (SDSU), M. Maimaitijiang (SDSU), Z. Yang (USDA)
3. Detection of Species-specific Plant Phenology from PlanetScope Time Series for Rangeland Management of the Western United States, NASA, $299,651, 10/01/2023-09/01/2025, PI X. Zhang (SDSU), CoI Y. Liu (SDSU), Stephen Boyte (USGS), G. Xian (USGS).
4. Expansion of RAVE Algorithm for Hourly Biomass Burning Emissions Estimation in Asia and Europe for Air Quality Forecast Applications, NOAA, 09/01/2023-08/31/2025, $250,000, PI F. Li (SDSU), CoI X. Zhang (SDSU)
5. AI-powered near real-time crop damage detection using satellite remote sensing, NASA EPSCoR, 09/2022-08/2024, $75,000, PI M. Maimaitijiang (SDSU), CoI D. Zeng (DSU), X. Zhang (SDSU)
6. Enhancement of RAVE emissions algorithm and transition to operations, NOAA, 10/2022-6/2024, $235,405, PI F. Li (SDSU), CoI X. Zhang (SDSU)
7. Fire Emissions Reprocessing Activities, NOAA, 10/2022-6/2024, $188,324, PI X. Zhang (SDSU), CoI F. Li (SDSU)
8. Maintenance, Evolution, and Validation of the Global Land Surface Phenology Product from Suomi NPP and JPSS VIIRS Observations, NASA, 09/2021-08/2024, $664,845, PI: X. Zhang (SDSU), CoIs: G. Henebry (SDSU)
9. Investigation Of Planetscope Time Series Observations for Detecting Land Surface Phenology In The Semiarid Western United States, NASA, 01/2022 - 07/2024, $198,495, PI: X. Zhang (SDSU)
10. Development of Near Real-Time Land Surface Phenology Product by Fusing Geostationary Satellite and VIIRS Observations in Support of Agriculture and Land Management, NASA, 08/2020-07/2023, $521,777, PI: X. Zhang (SDSU), CoIs: G. Gray (NCSU) and H. Zhang (SDSU)
11. 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, 06/2019-5/2024, $474K, PI: X. Zhang (SDSU), CoIs: E. Byamukama (SDSU), Z. Yang (USDA).
12. Global Biomass Burning Emissions Product Maintenance and Refinement – from VIIRS I-Band Fire Detections, NOAA, 08/20221-3/2024, $155,870, PI: X. Zhang (SDSU)
13. Reprocess of Global Biomass Burning Emissions Product in Support of Air Quality and Smoke Predictions, NOAA, 08/01/2021-09/30/2023, $97,418, PI: X. Zhang (SDSU)

(3) Completed projects

1. WF-3 Development and readiness of satellite products for fire and smoke forecasting -- A Unified Multi-Scale Biomass Burning Emissions Product, NOAA, 05/2020-04/2023, $310,000, PI: X. Zhang (SDSU)
2. Effectiveness and monitoring of large-scale carbon-loss mitigation activities in Indonesia’s peatlands, NASA, 01/2020-12/2022, $1,442,946, PI at SDSU: X. Zhang, Project PI: Mark A. Cochrane (UMCES), other Co-I: Keith Eshleman.
3. Global Biomass Burning Emissions Product Maintenance and Refinement –Migrating to Cloud System, NOAA, 08/20221-3/2023, $41,667, PI: X. Zhang (SDSU)
4. Near real-time wildfire smoke detection and monitoring from satellite imagery using artificial intelligence, South Dakota NASA EPSCoR RIG Program, 10/2020 –9/2022, $75,000, PI at SDSU: X. Zhang, Project PI: Shankarachary Ragi (South Dakota School of Mines & Technology
5. Global Biomass Burning Emissions Product -Maintenance and Refinement, NOAA, 08/2020-07/2021, $35,203, PI at SDSU: X. Zhang (SDSU).
6. Maintenance and Refinement of a Global Land Surface Phenology Product from NPP VIIRS for EOS-MODIS Continuity. NASA, 04/2018-03/2021, $692K, PI: X. Zhang (SDSU), CoIs: G. Henebry (SDSU) and L. Liu (SDSU).
7. 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, 07/2017-12/2020, $1,497K, PI at SDSU: X. Zhang, Project PI: Mark Cochrane (UMD Appalachian Laboratory).
8. Global Biomass Burning Emissions (GBBEP) Product and JPSS-1 Blended Biomass Burning. NOAA, 07/01/2016-06/30/2020, $230K. PI: X. Zhang (SDSU).
9. Investigation of GOES-16 Active Wildfire Detections and FRP Measurement for Estimating Biomass Burning Emissions. NOAA/IMSG, 12/1/2018-9/30/2019, $95K, PI: X. Zhang (SDSU).
10. A Multi-Scale Satellite-Based Indicator of Climate Change Impacts on Land-Surface Phenology. NASA, 07/2016-06/2019, $434K, PI at SDSU: X. Zhang, Project PI: J. Gray (NCSU), NASA.
11. Development and Validation of a Global Land Surface Phenology Product from NPP VIIRS for EOS-MODIS Continuity. NASA, 11/2014-12/2018, $685K, PI: X. Zhang (SDSU), CoIs: G. Henebry (SDSU) and M.A. Friedl (BU).
12. Global Biomass Burning Emissions (GBBEP) Product (BG-133E-15-SE-1613). NOAA, 09/11/2015-03/31/2017, $80K. PI: X. Zhang (SDSU).
13. Suomi NPP VIIRS BRDF/Albedo/NBAR Products to Extend the Long Term Consistent MODIS Standard Data Record. NASA, 08/2014-07/2017, $592K, PI at SDSU: X. Zhang, Project PI: C.B. Schaaf (UMass-Boston).
14. Monitoring land surface vegetation phenology from VIIRS. NOAA JPSS Risk Reduction Programs, 07/2013-06/2016, $380K, PI at SDSU: X. Zhang, Project Administrative PI at NOAA: Y. Yu.
15. Real-Time Monitoring and Short-term Forecasting of Phenology from GOES-R ABI for the Use in Numerical Weather Prediction Models. NOAA, 07/2014-06/2017, $346K, PI at SDSU: X. Zhang (SDSU), Project Administrative PI at NOAA: Y. Yu.
16. Change in our MIDST: Detection and Analysis of Land Surface Dynamics in North and South America Using Multiple Sensor Data streams. NASA, 07/01/2014-06/30/2018, $1.1M, G. Henebry (PI), X. Zhang (Co-I), K. M. de Beurs (Co-I).
17. 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, 08/2011-6/2014, $610K, PI at SDSU: X. Zhang, Project Administrative PI at NOAA: S. Kondragunta.
18. Vegetation phenology and enhanced vegetation index products from multiple long term satellite data records. NASA, 08/01/08-07/31/13, $3,441,131, PI at ERT/NOAA: X. Zhang, Project PI: K. Didan (UA).
19. Biomass Burning Emissions Product from GOES-R ABI. NOAA Contract No. /Task No. DG133R-07-NC-1616, Mod 12/8407-003, 8/2011-7/2012, $53,000, X. Zhang (task leader), S. Kondragunta (task monitor).
20. 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, 8/2011-7/2012, $50,000, X. Zhang (task leader), S. Kondragunta (task monitor).
21. Global Biomass Burning Emissions Product (GBBEP) from Multiple Geostationary Satellites. NOAA Contract No./Task No. DG133E-06-CQ-0030/N154-001, 7/2010-12/2011, $200,000, X. Zhang (task leader), S. Kondragunta (task monitor).
22. Derive Biomass Burning Emissions from GOES WildFire Automated Biomass Burning (WF_ABBA) Fire Products. NOAA Contract No./Task No. DG133E-06-CQ-0030/T102, 04/2005-12/2011, $550,000, X. Zhang (task leader), S. Kondragunta (task monitor).
23. 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, 08/2009-10/2011, $40,000, X. Zhang (task leader), M. Goldberg (task monitor).
24. Develop GOES-R ABI Aerosol & Trace Gas Emissions Algorithm. NOAA Contract No./Task No. DG133E-06-CQ-0030/T124A, 6/2009-12/2011, X. Zhang (task leader), S.Kondragunta (task monitor).
25. Adaptation of the GOES Emissions Algorithm to GOES-R ABI. NOAA SciTech Contract: DG133E-06-CQ-0030/8003-080, 6/2007-5/2009, X. Zhang (task leader), S. Kondragunta (task monitor).
26. Analysis of AVHRR Climate-Quality Land Products. NOAA Contract No./Task No. DG133E-06-CQ-0030/8003-034, 04/2005—12/2007, $150,000, X. Zhang (task leader), D. Tarpley (task monitor).
27. Global land cover and land cover dynamics from MODIS: algorithm refinement in support of global change research. NASA, 1/2004-12/2006, $698,049, PI: M.A. Friedl (BU), CO-I, X. Zhang (BU).
28. Real Time Estimation and Assimilation of Remotely Sensed Surface Properties for Numerical Weather Prediction Models. NOAA, 6/2004-5/2007, $454,987, PI: M.A. Friedl (BU), Co-I: X. Zhang (BU).
29. Retrieval of Time-Varying Land Cover and Vegetation Properties from MODIS in Support of the NCEP-WRF Land Surface Mode. NOAA, 8/2003-7/2004, $100,000, PI: M.A. Friedl (BU), Co-I: X. Zhang (BU).
30. Land Cover/Land-Cover Change, Albedo, BRDF/Directional Reflectance and Spatial Structure Products from MODIS-N and MODIS-T. NASA, 1/1992 - 12/2003, $9,464,991, PI: A.H. Strahler (BU), Research Associate: X. Zhang (BU).

31. Remote Sensing for Estimating Rice Yield in Central China. Supported by Chinese National Eighth Five-Year Plan, 1990-1995, X. Zhang (PI).
32. 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, S. Cai (PI), X. Zhang (Co-I).
33. Remotely Sensed Investigation of Natural Resources & Environment in China and their Dynamics. Supported by Five-Year Plan of the Chinese Academy of Science, 1992-1995, S. Cai (PI), X. Zhang (Co-I).
34. 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, S. Cai (PI), X. Zhang (Co-I).
35. 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, S. Cai (PI), X. Zhang (Co-I).
36. 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, S. Cai (PI), X. Zhang (Co-I).
37. 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. S. Cai (PI), X. Zhang (Co-I).
38. Planning of Agriculture and Ecological Economy in Four Lake District. Supported by Seventh Five-Year Plan of the Hubei Province, 1986-1990, S. Cai (PI), X. Zhang (Co-I).
39. The Evolution of Jianghan-Dongting Lakes. Supported by Chinese National Foundation of Natural Science, 1984-1987, S. Cai (PI), X. Zhang (Co-I).

Work Experience

6/2018-present: Full Professor of Geography & Senior Research Scientist at the Geospatial Sciences Centers of Excellence (GSCE), South Dakota State University (SDSU), Brooking, SD. USA
8/2013-5/2018: Associate Professor of Geography & Senior Research Scientist at the Geospatial Sciences Centers of Excellence (GSCE), South Dakota State University (SDSU), Brooking, SD. USA
6/2012-8/2013: Visiting Associate Research Scientist, University of Maryland at NOAA/NESDIS/STAR, College Park, MD, USA
4/2005-5/2012: Senior research scientist, Earth Resources Technology (ERT) at NOAA/NESDIS/STAR, Camps Springs, MD, USA
6/1999-3/2005: Research Associate and made as Research Assistant Professor in 2003, Department of Geography, Boston University
10/1988-2/1995: Research Assistant Professor (1988-1992) and Research Associate Professor (1992-1995), Deputy of Department of Natural Resources and Land Use, Institute of Geodesy & Geophysics, Chinese Academy of Science, Wuhan, China
7/1984-9/1988: Research Assistant, Institute of Hydrobiology, Chinese Academy of Science, Wuhan, China

Creative Activities

Operational Products
• Global near real time biomass burning emissions product from polar and geostationary satellites (GBBEPx) (NOAA)
• NOAA Blended Polar Geo Biomass Burning Emissions Product (Blended-BBEP) (NOAA)
• Geostationary Operational Environmental Satellite Biomass Burning Emission Product (GBBEP) (NOAA)
• AVHRR-MODIS long-term global land surface phenology product (NASA)
• VIIRS global land surface phenology product (NASA)

Edited Books

1. Zhang, X. (Ed.), 2012. Phenology and Climate Change, ISBN: 978-953-51-0336-3, InTech, Available from: http://www.intechopen.com/books/phenology-and-climate-change.

Refereed Journal Papers (English) (* first author is my PhD students or Postdocs, or I am the corresponding author but not the first author)
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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/10.1016/j.rse.2023.113827
5. *Tran, K. H., Zhang, X., Ye, Y., Shen, Y., Gao, S., Liu, Y. & 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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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, https://doi.org/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, https://doi.org/10.5194/gmd-15-7977-2022.
15. Zhang, X., Shen, Y., Gao, S., Wang, W., & Schaaf, C., 2022, Diverse responses of multiple satellite-derived vegetation greenup onsets to dry periods in the Amazon, Geophysical Research Letters, 49, e2022GL098662. https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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. Front. Plant Sci. 13:844971, https://doi.org/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 Sens., 14(5), 1273, https://doi.org/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), https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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; https://doi.org/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, https://doi.org/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, https://doi.org/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), http://doi.org/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), https://doi.org/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 & Remote Sensing, 58(5):1-23, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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), https://doi.org/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, https://doi.org/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, https://doi.org/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, Environ Sci Technol, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/10.1029/2020JD032768
53. *Li, F ., Zhang, X., Kondragunta, S., Lu, X., 2020, An evaluation of advanced baseline imager fire radiative power basedwildfire emissions using carbon monoxide observed by the TroposphericMonitoring Instrument across the conterminous United States, Environmental Research Letters, 15, 094049, https://doi.org/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, https://doi.org/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; https://doi.org/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; https://doi.org/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; https://doi.org/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; https://doi.org/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; https://doi.org/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, https://doi.org/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; https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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. https://doi.org/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, https://iopscience.iop.org/article/10.1088/1748-9326/ab04d2/meta.
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. https://doi.org/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. https://doi.org/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; https://doi.org/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, https://doi.org/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, https://doi.org/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. https://doi.org/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; https://doi.org/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. https://doi.org/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. https://doi.org/10.1002/2017JG004279.
77. Huang, R., Zhang, X., Chan, D., Kondragunta, S., Russell, A.G., Odman, M.T., 2018, urned Area Comparisons between Prescribed Burning Permits in Southeastern USA and two Satellite‐derived Products. Journal of Geophysical Research-Atmosphere, 123(9): 4746-4757. https://doi.org/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. https://doi.org/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. https://doi.org/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. http://dx.doi.org/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. https://doi.org/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. https://doi.org/10.1088/1748-9326/aa6ad9 (news http://environmentalresearchweb.org/cws/article/news/69988)
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, https://doi.org/10.1002/2017JG003811. (Highlighted by the Journal)
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, https://doi.org/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; https://doi.org/10.3390/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. https://doi.org/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 Landsatand the MODIS BRDF/NBAR/albedo product, International Journal of Applied Earth Observation and Geoinformation, 59, 104–117. http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, https://doi.org/10.1088/1748-9326/aa5b3a (news http://environmentalresearchweb.org/cws/article/news/68993)
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, http://dx.doi.org/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, http://dx.doi.org/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), http://dx.doi.org/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, http://dx.doi.org/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, http://doi.org/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, http://dx.doi.org/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, Int. J. Biometeorol., http://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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi. org/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, http://dx.doi.org/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, http://dx.doi.org/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, v2, http://dx.doi.org/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. http://dx.doi.org/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, http://dx.doi.org/10.1029/2007GL031447. (This paper was reported in more than 60 different news sites worldwide, including New Scientist Magazine, Wired Magazine, Natural History Magazine, AGU EOS, and Live Science).
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, Vol. 111, G04017, http://dx.doi.org/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, http://dx.doi.org/10.1029/2006GL025879.
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, http://dx.doi.org/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. http://dx.doi.org/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, http://dx.doi.org/10.1029/2004GL020137. (This paper was reported in more than 100 different news sites, such as Science News (newsmagazine), NASA press release, and The Associated Press).
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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/10.1029/2001WR000356. (This paper was highlighted by EOS, 83(43), Oct., 2002).
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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/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, http://dx.doi.org/10.1080/014311698216396.

Refereed Book Chapters (English)

143. 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. https://doi.org/10.1016/B978-0-12-409548-9.10351-3
144. 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. https://doi.org/10.1007/978-3-642-25047-7_3
145. 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.
146. 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.
147. 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,pp709-721.
148. 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.
149. 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.
150. 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.
151. 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.
152. 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) (5)

153. 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.
154. 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.
155. 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.
156. Zhang, X., Cai, S. and Sun, S. 1994. Evolution of Dongting Lake since Holocene, Limnology Science, China, 16(1).
157. 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) (15)

158. 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.
159. 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.
160. 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.
161. 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.
162. 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.
163. 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.
164. 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.
165. 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.
166. 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.
167. 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.
168. 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.
169. 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.
170. 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.
171. 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.
172. 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.

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