Zhang, Xiaoyang

1. The NOAA Blended Polar Geo Biomass Burning Emissions Product in North America (Blended-BBEP) (NOAA)

See http://satepsanone.nesdis.noaa.gov/pub/FIRE/BBEP-geo/

Biomass burning emissions and burned area over North America are modeled using satellite-derived vegetative fuel loading, fuel moisture, and burned area. Briefly, the MODIS vegetation property-based fuel system is developed from MODIS data including land cover type, vegetation continuous field, and monthly leaf-area index. The weekly fuel moisture category is retrieved from AVHRR Global Vegetation Index (GVIx) data for the determination of fuel combustion efficiency and emission factor. The burned area is simulated using the half-hourly fire sizes obtained from the GOES WF_ABBA fire product and the active fire hotspot detections from both MODIS and AVHRR observations. By integrating all these parameters, hourly burned area and biomass burning emissions are operationally produced every six hour in NOAA/NESDIS.

2. The Blended Global Biomass Burning Emissions Product From MODIS And Geostationary Satellites (GBBEPx) (NOAA)

See http://satepsanone.nesdis.noaa.gov/pub/FIRE/GBBEPx/

The global biomass burning emissions product (GBBEPx) is produced daily by blending scaled GBBEP-Geo and QFED fire emissions. The QFED calculates Terra and Aqua MODIS FRP in individual grid cells for four different biome types of tropical forest, extratropical forest, savanna, and grassland. The coefficient to estimate daily biomass burning emissions from MODIS FRP for four biome types is determined by comparing with GFED (Global Fire Emissions Database) and MODIS aerosol optical depth (AOT). GBBEP-Geo obtains FRP from a network of geostationary satellites consisting of two Geostationary Operation Environmental Satellites (GOES, GOES-E and GOES-W) which are operated by the National Oceanic and Atmospheric Administration (NOAA), the Meteosat Second Generation Satellites (Meteosat) operated by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the Multi-functional Transport Satellite (MTSAT) operated by the Japan Meteorological Agency (JMA). The missing fire detections and FRP calculations are simulated by reconstructing diurnal pattern of FRP. The reconstructed FRP diurnal pattern is used to estimate hourly biomass burning emissions  for individual fire pixels. GBBEP-Geo is further calibrated/scaled using QFED because QFED is specifically designed for NASA Goddard Chemistry Aerosol Radiation and Transport Model (GOCART) in aerosol forecasting.

3. Long-term Global Land Surface Phenology Product from AVHRR and MODIS (NASA)

Long-term global land surface phenology product is derived from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data since 1981. Based on a dataset of daily enhanced vegetation index at a spatial resolution of 0.05 degrees, the seasonal vegetative trajectory for each individual pixel is reconstructed using a hybrid piecewise logistic model. The temporal trajectory is then used to detect a set of phenological metrics globally. The long-term phennology product is available at GSCE and can be obtained by request.

see ftp://bruin.sdstate.edu/ftp/XYZ/AVHRR_EVI2/

4. Monitoring Land Surface Vegetation Phenology from JPSS VIIRS (NOAA)

This project is to develop and validate real-time land surface vegetation phenology from JPSS VIIRS. The VIIRS phenology will characterize the nature, magnitude, and timing of ecosystem changes. While long-term phenology data serves the investigation of climate change, the VIIRS phenology will also provide relatively realistic data in real-time monitoring and short-term forecasting of vegetation phenology across North America to assist the land modeling and the crop growth monitoring.

see http://www.star.nesdis.noaa.gov/jpss/EDRs/products_Foliage.php

5. Real-Time Monitoring and Short-term Forecasting of Vegetation Phenology from GOES-R ABI for the Use in Numerical Weather Prediction Models (NOAA)

This project is to use GOES-R ABI data to build an operational system for monitoring and forecasting vegetation phenology.  Vegetation phenology quantifies the seasonal dynamics of vegetation properties including the timing and magnitude of green vegetation fraction (GVF) development. The high frequency of diurnal observations from GOES-R ABI offers a unique opportunity to create daily cloud-free trajectories of the vegetation index. Thus this project will develop an algorithm and operational product for real-time monitoring and short-term forecasting of vegetation phenological properties, including the timing and GVF magnitude in spring leaf-out, summer growth peak, and fall foliage development.

6. Development and Validation of a Global Land Surface Phenology Product from NPP VIIRS for EOS-MODIS Continuity (NASA)

The primary objective of this project is to develop and implement an algorithm to produce continuous and well-calibrated time series of global land surface phenology from VIIRS that provides continuity with the MCD12Q2 product from MODIS. This dataset will be suitable for characterizing and understanding interannual-to-decadal scale changes in ecosystem response to climate change. The specific tasks we propose are: (1) to develop and implement a land surface phenology product with a spatial resolution of 500-m from NPP VIIRS that provides continuity with MODIS phenology products; (2) to develop a phenology product with a spatial resolution of 0.05 degree from the VIIRS surface reflectance CMG (Climate Modeling Grid) that extends phenology measurements begun by AVHRR (1981-present); (3) to conduct inter-comparisons between the VIIRS and MODIS phenology products to ensure continuity of the VIIRS product with the MODIS data record; (4) to evaluate and validate the stability, precision, uncertainty, accuracy, and spatial and temporal continuity of the proposed VIIRS phenology product.

Other Current projects

• Change in our MIDST: Detection and Analysis of Land Surface Dynamics in North and South America Using Multiple Sensor Data streams, NASA. (Henebry, M.G,  Zhang, X., de Beurs, K.M.)
• Suomi NPP VIIRS BRDF/Albedo/NBAR Products to Extend the Long Term Consistent MODIS Standard Data Record, NASA. (Schaaf, C.,  Strahler, A.H., Zhang, X., Wang, Z.)
• A multi-scale satellite-based indicator of climate change impacts on land-surface phenology, NASA. (Gray, J.,, Friedl, M., Zhang, X., Gerst, K.)
• 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. (Cochrane, M., Saharjo, B., Putra, E., Zhang, X., Yokelson, R., Hagen, S.)

(Citations >7000, h-index=27: http://scholar.google.com/citations?user=SrrF3Y0AAAAJ&hl=en)

Selected Journal Publications:

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.

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.

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

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, http://doi.org/10.1109/TGRS.2016.2552462.

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.

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., doi:10.1007/s00484-016-1147-6

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.

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.

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.

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.

Zhang, X., 2015. Reconstruction of a Complete Global Time Series of Daily Vegetation Index Trajectory from Long-term AVHRR Data. Remote Sensing of Environment, http://dx.doi.org/10.1016/j.rse.2014.10.012.

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.

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.

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.

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.

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.

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, http://dx.doi.org/10.1007/s00484-014-0802-z

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.

Yanmin, S., Schaaf, C., Zhang, X.  Xiaoyang; 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.

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,  117, doi:10.1029/2012JD017459.

Zhang, X., Mitchell D. 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.

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.

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, 10.1109/TGRS.2011.2149535.

Zhang, X. and Goldberg, M, 2011. Monitoring Fall Foliage Coloration Dynamics Using Time-Series Satellite Data. Remote Sensing of Environment, 115 (2): 382-391.

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.

 Zhang, X., Goldberg, M., Tarpley, D., Friedl, M., Morisette, J., Kongan, 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.

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.

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.

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.

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, doi:10.1016/j.atmosenv.2008.04.060.

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, DOI: 10.1117/1.2948785.

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, doi:10.1016/j.rse.2008.02.006.

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.

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, doi:10.1029/2006JG000217.

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:10.1029/2006GL025879.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Friedl, M. A, McIver, D. K, Hodges, J. C., Zhang, X., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schnieder, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C. B. 2002. Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment, 83(1-2), 287-302.

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.

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.

Selected Book Chapters

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.

Zhang, X., Drake, N. A., and 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, pp 69-90.

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 Zhang, X. (ed), Phenology and Climate Change,    InTec, pp 297-320.

Friedl, M.A., Zhang, X., and 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, pp709-721.

Drake, N.A., Zhang, X., Symeonakis, E., Patterson, G., and 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.

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.

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.

Refereed Journal Papers and Book Chapters in Chinese (>20)

Xiaoyang_Zhang_CV_2016.pdf

GSE 792–S01
Advanced Methods in Geospatial Modeling:
Computation for Remote Sensing Analysis and Product Generation 

GEOG 485/585-S01
Quantitative Remote Sensing

Fangjun Li, Ph.D. in Geospatial Science & Engineering with specialization in Remote Sensing Geography, South Dakota State University. 

Dissertation: TBD.

Jianmin Wang, Ph.D. in Geospatial Science & Engineering with specialization in Remote Sensing Geography, South Dakota State University.

 Dissertation: TBD.

Dong Yan (6/2014-present)

 Senthilnath  Jayavelu (2/2015-present)