Dr Tingbao Xu

Ph.D. in Resource Management and Environmental Science (ANU) M.Sc. in Cartography and Remote Sensing (Beijing Uni), B.Sc. in Remote Sensing Geology (Zhejiang Uni)
Senior Manager Research

I have been actively engaged in spatio-temporal analysis and modelling in natural resources, environment, climate, agriculture since early 1980s, and have gained extensive experience in climate data interpolation and analysis; climate change impact assessment; environment and natural resources inventory and mapping; agriculture bioclimatic environment analysis and crop yield forecasting; and other relevant applications. I also have a solid background and productive application experience with GIS, Remote Sensing and computing.

I have a rich experience of supervision on Master and PhD students, and undergraduate student projects.

  • Senior Manager (Research) 2011 – present (Senior Modeller 2005 – 2010), Fenner School of Environment & Society, ANU
  • Research Scientist 1999-2005 and Manager for spatial data management and modelling 2001-2005, AGRECON, University of Canberra.
  • Senior GIS officer 1998-1999, ERIN, Department of Environment and Heritage of Australia.
  • PhD Student 1994-1997 and Research Officer 1995-1997, CRES, ANU.
  • Visiting Fellow 1992-1993, CRES, ANU.
  • Assistant Professor 1985-1991, Institute of Remote Sensing and GIS, Beijing University.

Research interests

Project: Climate and FPI data for the National Inventory Report (Lead Chief Investigator)

Fund provider: Department of Industry, Science, Energy and Resources

Delivery: Spatially interpolated, 1 km resolution, climate and forest productivity data for land sector modelling work using the Full Carbon Accounting Model

Background: Gridded national climate and Forest Productivity Index (FPI) data are essential inputs to Australian Full Carbon Accounting Model (FullCAM) which is used to produce annual totals for Australia’s National Inventory Reports in the Department of Industry, Science, Energy and Resources (DISER) (formerly Department of the Environment and Energy). The reports are submitted to the United Nations Framework Convention on Climate Change (UNFCCC) each year to fulfil Australia’s international greenhouse gas inventory reporting commitments as a party to UNFCCC, the Kyoto Protocol and the Paris Agreement.

Fenner School of ANU has been providing high quality products of Climate and FPI to the Department since the inception of national carbon accounting system during the early 2000s. In particular, the ANUSPLIN package built by Fenner School (Hutchinson and Xu), a unique and world leading software to interpolate and build gridded climate surfaces from station recorded climate data, has made Fenner School at a very strong and unique position to provide accurate and reliable high resolution national gridded monthly and daily climate data to FullCAM modelling.

Significant innovations have been introduced by Dr Tingbao Xu to improve the quality of FPI products since 2017, which have significantly improved the quality and accuracy of outputs from FullCAM carbon modelling. These innovations include:

  1. Introduced serial national monthly NDVI data with improved quality and spatial continuity to improve the sensitivity of FPI to environmental drives and to substantially uplift the accuracy of national FPI products.
  2. Constructed new national soil property data layers for FPI modelling from latest gridded national soil data developed by CSIRO, which significantly improved the spatial consistency and accuracy of FPI and Top Soil Moisture Deficit (TSMD) products.
  3. A streamlined computation has been introduced and built for producing FPI and TSMD products, and implementing primary quality assurance. That has resulted in much higher computing efficiency and elimination of human errors, which has effectively enabled the Department to reproduce serial FPI and TSMD products back to 1970. Most importantly, the computation automation has made reproduction of serial FPI and TSMD products feasible in the case such as model upgrading in FullCAM.

This project delivers also the daily climate statistics for crop and grass areas in ABS Statistical Areas Level 2 (SA2) regions. This task is to extract the areal average values of major daily climates (rainfall, mean temperature and pan evaporation, generated using ANUSPLIN from BoM’s stational records) for areas delineated by high resolution crop or grass masks in each ABS Statistical Areas Level 2 (SA2) region.


Project: Improved NDVI data for the National Inventory Report (Lead Chief Investigator)

Fund provider: Department of Industry, Science, Energy and Resources

Delivery: Calibrated and processed MODIS/Terra 1km monthly and annual NDVI national layers for producing consistent FPI products

Background: Normalized Difference Vegetation Index (NDVI) derived from remote sensing data has been the basic index for measuring the “greenness” of the earth’s surface over past decades. It is a popular and robust measure of vegetation activity at the land surface, and is widely used to study the spatial and temporal patterns of vegetation cover at various scales with the objective to understand the role of terrestrial vegetation in regional and global processes, such as global carbon and nitrogen cycle, global hydrological cycle and global energy cycle.

Monthly national NDVI is an essential input to FPI computation. It is used for producing LAI (Leaf Area Index), subsequently the APAR (Absorbed Photosynthetically Active Radiation) and I (Rainfall Intercept) data layers. NDVI is a robust and relatively “direct” measure of vegetation activity at the land surface. It is a materialized dynamic driving component in FPI model. Its value possesses a substantial leverage to producing a not-overly-rainfall-driven FPI value. A high-quality serial monthly NDVI data can play a constructive role in promoting the accuracy and robustness of sequential FPI product, and boosting the practical applications of FPI products.

NOAA/AVHRR NDVI data have been used in previous FPI products. Troubled by persistent system noises, random noises and sensor downgrading issues, NOAA/AVHRR NDVI data has been deteriorated and gradually replaced by MODIS/NDVI products worldwide. However, a proper calibration is essential to utilize MODIS NDVI to replace AVHRR NDVI data for generating consistent FPI products to ensure the continuity of FPI project. An extensive effort has been taken in a review study to investigate and assess NDVI/LAI data sources from MODIS and other satellite platforms, and the relation between AVHRR/NDVI and MODIS/NDVI. The following processes are proposed.


Project: Future Ready Regions EDIS Development Project (Chief Investigator)

Fund provider: NSW Department of Primary Industries


  1. ANUClimate Near Real time Australia national grids of daily climates;
  2. Literature review, feasibility proposal and methodology for improved daily rainfall interpolation encompassing third party networks and new covariates (e.g. satellite rainfall data).



Xu, Y and Xu, T. (2022). An evolving marine environment and its driving forces of algal blooms in the Southern Yellow Sea of China. Marine Environmental Research 178 (2022) 105635. https://doi.org/10.1016/j.marenvres.2022.105635.

Yu, Y, Xu, T.  and Wang, T. (2020). Outmigration Drives Cropland Decline and Woodland Increase in Rural Regions of Southwest China. Land, 2020,9,443,p1-23; doi:10.3390/land9110443.

Terry, W and Xu, T. (2020). Simulating the effects of climate change on the distribution of the threatened Brush-tailed Phascogale Phascogale tapoatafa tapoatafa in eastern Australia. Research Report, Vol 137 (5) 2020. p128-139.

Guo, B., Zhang, J.,Meng, X., Xu, T. and Song Y. (2020). Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN. Scientific Reports 10(1):81, DOI: 10.1038/s41598-019-57078-3.

White, I., Xu, T., Zeng, J., Yu, J., Ma, X., Yang, J., Huo, Z. and Chen, H. (2020). Changing climate and implications for water use in the Hetao Basin, Yellow River, China. Proceedings of the International Association of Hydrological Sciences 383:51-59, DOI: 10.5194/piahs-383-51-2020.

Li, L., Chen, Y., Xu, T., Meng, L., Huang, C. and Shi, K. (2020). Spatial Attraction Models Coupled with Elman Neural Networks for Enhancing Sub-Pixel Urban Inundation Mapping. Remote Sensing. 2020, 12, 2068; doi:10.3390/rs12132068.

Heath, L.C., Tiwari, P. Sadhukhan, B., Tiwari, S., Chapagain, P., Xu, T., Li, G., Ailikun, Joshi, B. and Yan J. (2020). Building climate change resilience by using a versatile toolkit for local governments and communities in rural Himalaya. Environmental Research. 188 (2020) 109636. https://doi.org/10.1016/j.envres.2020.109636.

Li, L., Chen, Y., Xu, T., Shi, K., Liu, R., Huang, C.,  Lu, B. and Meng, L. (2019). Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models. Remote Sens. 2019, 11, 1231; doi:10.3390/rs11101231.

Xia, C., Li,Y., Xu, T., Chen, Q., Ye, Y., Shi, Z., Liu, J., Ding, Q. and Li, X. (2019). Analyzing spatial patterns of urban carbon metabolism and its response to change of urban size: A case of the Yangtze River Delta, China. Ecological Indicators 104 (2019) 615-625. https://doi.org/10.1016/j.ecolind.2019.05.031

Chen, Y., Xu, T., Shui, J., Liu, R., Wahid, S., Shi, K., Yang, H., Guo, L., Cheng, Z., (2019). Characterising spatiotemporal variability of South Asia’s climate extremes in past decades. Climate Research. https://doi.org/10.3354/cr01554.

Li, L., Chen, Y., Xu, T., Shi, K., Huang, C., Liu, R., Lu, B. and Meng, L. (2019). Enhanced Super-Resolution Mapping of Urban Floods Based on the Fusion of Support Vector Machine and General Regression Neural Network, IEEE Geoscience and Remote Sensing Letters. 10.1109/LGRS.2019.2894350.

  • Guo, B., Zhang, J., Xu, T., Croke, B., Jakeman, A, Song, Y., Yang, Q., Lei, X. and Weihong Liao, W. (2018). Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models. Water 2018, 10(11), 1611; doi:10.3390/w10111611.

    Xia, C., Lia, Y., Xu, T., Ye, Y., Shi, Z., Peng, Y. and Liu, J. (2018). Quantifying the spatial patterns of urban carbon metabolism: A case study of Hangzhou, China. Ecological Indicators 95 (2018) 474–484. https://doi.org/10.1016/j.ecolind.2018.07.053.

  • Heath, L., Tiwari, P., Sadhukhan, B., Tiwari, S., Joshi, B., Ailikun, Chapagain, PS., Xu, T., Li, G. and Yan, J. (2018). Using a participatory-based toolkit to build resilience and adaptive capacity to climate change impacts in rural India: A new paradigm shift for rural communities in the Himalaya. APN Science Bulletin, 8(1). doi:10.30852/sb.2018.292.

  • Liu, R., Chen, Y., Wu, J., Xu, T., Gao, L., Zhao, X. (2018). Mapping spatial accessibility of public transportation network in an urban area – A case study of Shanghai Hongqiao Transportation Hub. Transportation Research Part D: Transport and Environment, 59: 478-495, DOI:10.1016/j.trd.2018.01.003

    1. B., Zhang, J. and Xu, T. (2018). Comparison of two statistical climate downscaling models: a case study in the Beijing region, China. International Journal of Water 12(1):22. DOI:10.1504/IJW.2018.10011177
  • Zhong, A., Wang, A., Li, J., Xu, T., Meng, D., Ke, Y., Li, X. & Chen, Y. (2018). Downscaling of passive microwave soil moisture retrievals based on spectral analysis, International Journal of Remote Sensing, 39:1, 50-67, DOI: 10.1080/01431161.2017.1378456

  • Li, L., Xu, T., Chen, Y., Liu, R., Shi, K. and Huang, C. (2017). Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features. Computational Intelligence and Neuroscience, Volume 2017, Article ID 9858531, 9 pages, https://doi.org/10.1155/2017/9858531.

  • Ren Xuan, Zheng Jiang-hua, Mu Chen, Yan Kai, Xu Ting-bao. (2017) Evaluating reliability of grassland net primary productivity estimates using different meteorological interpolation methods. Pratacultural Science,2017,34(3): 439-448

  • Zhang, F., Chen, Y., Yang, H., Xu, T., Cheng, Z. and Liang, J. (2016). Assessment of Reclamation Treatments of Abandoned Farmland in an Arid Region of China. Sustainability 2016, 8(11), 1183; doi: 10.3390/su8111183.

  • Shi, K., Chen, Y., Yu, B., Xu, T., Yang, C., Li, L., Huang, C., Chen, Z., Liu, R. and Wu, J. (2016). Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data. Applied Energy.184 (2016) 450–463.

  • Liu, R., Chen, Y., Wu, J., Gao, L., Barrett, D., Xu, T., Li, X., Li, L., Shi, K., Huang, C. and Yu, J. (2016). Integrating Entropy-Based Na¨ive Bayes and GIS for Spatial Evaluation of Flood Hazard. Risk Analysis, DOI: 10.1111/risa.12698.

  • Yang, H., Chen, Y., Zhang, F., Xu, T. and Cai, X. (2016). Prediction of Salt Transport on Different Soil Textures under Mulched Drip Irrigation in Arid Zone. CSIRO PUBLISHING, Soil Research. http://dx.doi.org/10.1071/SR15169.

  • Shi, K., Chen, Y., Yu, B., Xu, T., Li, L., Huang, C., Liu, R., Chen, Z. and Wu, J. (2016). Urban expansion and agricultural land loss in China: a multiscale perspective. Sustainability 2016, 8(8), 790; doi:10.3390/su8080790.

  • Li, L., Xu, T., Chen, Y. (2016). "Improved Urban Flooding Mapping from Remote Sensing Images Using Generalized Regression Neural Network-Based Super-Resolution Algorithm". Remote Sens. 2016, 8(8), 625; doi:10.3390/rs8080625.

  • Li, L., Chen, Y., Xu, T., Huang, C., Liu, R. and Shi, K. (2016). Integration of Bayesian regulation back-propagation neural network and particle swarm optimization for enhancing sub-pixel mapping of flood inundation in river basins. Remote Sensing Letters. Vol 7 (7) 631-640.

  • Shi, K., Chen, Y., Yu, B., Xu, T., Chen, Z., Liu, R., Li, L. and Wu, J. 2016. Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel dataanalysis. Applied Energy. 168 (2016) 523–533.

  • Liu, R., Chen, Y., Wu, J., Gao, L., Barrett, D., Xu, T., Li, L., Shi, K., Huang, C. and Yu, J. (2015). Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia, Stochastic Environmental Research and Risk Assessment, DOI 10.1007/s00477-015-1198-y.

  • Zhu, L., Gong, H., Dai, Z., Xu, T. and Su, X. (2015). An integrated assessment of the impact of precipitation and groundwater on vegetation growth in arid and semiarid areas. Environment Earth Science. (2015) 74:5009–5021.

  • Li, L., Chen, Y., Xu, T., Liu, R., Shi, K. and Huang, C. (2015). Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm. Remote Sensing of Environment. 164 (2015) 142–154. doi: 10.1016/j.rse.2015.04.009.

  • Xu, T, Croke, B. and Hutchinson, M.F. (2014). Identification of spatial and temporal patterns of Australian daily rainfall under a changing climate. Proceedings 2014 7th International Congress on Environmental Modelling and Software, San Diego, CA, USA, Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.).

  • Phillips, R.D., Peakall, R., Hutchinson, M.F., Linde, C.C., Xu, T., Dixon, K.W. and Hoppe, S.D. (2014). Specialized ecological interactions and plant species rarity: The role of pollinators and mycorrhizal fungi across multiple spatial scales. Biological Conservation, 169:285-295.

  • Hutchinson, M.F and Xu, T. (2013) ANUSPLIN VERSION 4.4 User Guide, Fenner Schools of Environment and Society, The Australian national University.

  • Xu, T. and Hutchinson, M.F. (2013). New developments and applications in the ANUCLIM spatial climatic and bioclimatic modelling package. Environmental Modelling and Software, 40: 267-279.

  • Phillips, R.D., Xu, T., Hutchinson, M.F., Dixon, K.W. and Peakall, R. (2013). Convergent specialization - the sharing of pollinators by sympatric genera of sexually deceptive orchids. Journal of Ecology, 2013, 101, 826-835.

  • Xu, T. and Hutchinson, M.F. (2012). New developments in the ANUCLIM bioclimatic modelling package. Proceedings 2012 International Congress on Environmental Modelling and Software Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.)

  • Xu, T. and Hutchinson, M.F. (2011) ANUCLIM VERSION 6.1 User’s Guide, Fenner Schools of Environment and Society, The Australian national University.

  • Hutchinson, M.F., Xu, T. and Stein, J. (2011) Recent Progress in the ANUDEM Elevation Gridding Procedure. Geomorphometry, 2011, Redlands, California, USA.

  • Xu, T., Moore, I.D. and Gallant, J. (1993) Fractals, Fractal Dimensions and Landscapes -- A Review. Geomorphology, 8/4:245-262.