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UCL Home  /  Geography  /  People  /  Academic Staff  /  Qingling Wu

Dr Qingling Wu

Room 106

UCL Department of Geography
North-West Wing 
London WC1E 6BT

Phone: +44 (0) 20 7679 5412


Office Hours (Term 2, 2020-2021)
Mondays: 10:00-11:00
Tuesdays: 14:30-15:30

Qingling Wu is currently a Senior Teaching & Research Fellow in Remote Sensing in the Department of Geography, which she joined in 2013. Before joining UCL, she completed her PhD in Geography at Clark University (Massachusetts, USA), specializing in GIS and Remote Sensing. During her doctoral studies, Qingling worked as a research assistant at Clark Labs, contributing to the development of the IDRISI software package. She has a B.Eng. in Software Engineering from China University of Geosciences in Wuhan, China.

Peer-reviewed Publications

Huang J, Gómez-Dans JL, Huang H, Ma H, Wu Q, Lewis PE, Liang S, Chen Z, et al. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology, 276-277: 107609-107609.

Huang, J., Ma, H., Sedano, F., Lewis, P., Liang, S., Wu, Q., . . . Zhu, D. (2019). Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model. European Journal of Agronomy, 102, 1-13.

Wu, Q. (2014). Region-shrinking: a hybrid segmentation technique for isolating continuous features, the case of oceanic eddy detection. Remote Sensing of Environment, 153, 90-98.

Other Publications

Lansley, G., Wu, Q., Singleton, A., Unwin, D., and Kemp. K., (2015) Instructor’s Manual for Geographic Information Science and Systems (4th Edition) by P. Longley, M. Goodchild, D. Maguire, and D. Rhind., Wiley, Chichester.

Wu, Q. (2013) From phenomena to objects, segmentation of fuzzy objects and its application to oceanic eddies. Doctoral Dissertation, Clark University Graduate School of Geography, Worcester, MA USA.

Conference Presentations

Wu, Q. 2014. Region-shrinking: A segmentation technique for oceanic eddy detection. Poster presentation at ESA Earth Observation Summer School. August 4-14, 2014. Frascati, Italy

J. R. Eastman and Q. Wu. 2012. Extended Empirical Orthogonal Teleconnection analysis for the study of coupled earth systems. In Paper Session: Spatiotemporal Thinking, Computing and Applications I --- How do we think and compute spatiotemporally in geography? Association of American Geographers Annual Meeting. February 24-28, 2012. New York, NY.

Wu, Q. and J. R. Eastman. 2011. From fields to objects: Time series analysis of segmentation of fiat boundaries, a case study of mesoscale eddies. Association of American Geographers Annual Meeting. April 12-16, 2011. Seattle, WA.

Wu, Q. and J. R. Eastman. 2010. Extended EOT analysis of multi-level ocean/atmosphere temperature. Association of American Geographers Annual Meeting. April 14-18, 2010. Washington, D.C.

Wu, Q. and J. R. Eastman. 2008. An exploration of the geography and dynamics of atmospheric Carbon Monoxide through time series analysis of MOPITT imagery. Poster Session, Association of American Geographers Annual Meeting. April 15-19, 2008. Boston, MA.

Broadly, I am interested in applying state-of-the-art image processing, data mining, and data assimilation techniques to the understanding of global environmental change.

Currently, I am a Co-I of the STFC funded "Sentinels of Wheat" (SOW) project for assimilating EO data into crop models. This project aims to demonstrate the practical benefits of EO-enabled crop monitoring and yield prediction for sustainable agriculture, in order to reduce rural poverty and to close inequality gaps.

In the past, I have also been interested in image analysis, with a specialization in the development and application of novel feature extraction methods. For example, the development of novel image segmentation techniques (object-based image analysis) for the detection and tracking of fuzzily-bounded amorphous phenomena such as oceanic eddies. A sample paper can be found here.

Succesful grants:

  • Aug 2020 - Jul 2021, "Building Capacity for Food-Crop Monitoring in Ghana Using Earth Observation", Chair’s Award, Newton Prize 2019, (Co-I, £460k), funded by BEIS (Click to read about news on this award)
  • Mar 2020 - Mar 2022, "AMAZING- Advancing MAiZe INformation for Ghana", UKRI UK-China AgriTech Impact Development Fund, (Co-I, £500k), funded by STFC
  • Apr 2018 - Mar 2020, "Sentinels of Wheat", Newton UK-China AgriTech project uplift. (Co-I, £708k), funded by STFC
Here is a short film explaining how we use EO and DA techniques to forecast yield prediction in China:

    Our SOW research outputs feed directly into agricultural production planning in China. Our research delivers significantly improved simulations and predictions of crop performance that assist in the strategic planning, optimization of regional production and management of national food supply and security. The lead partner in China, IARRP-CAAS runs the China Agriculture Remote Sensing Monitoring System (CHARMS) which monitors crop acreage change, yield, production, drought and other agriculture-related information for 7 main crops in China. CHARMS provides information directly to the Ministry of Agriculture and Rural Affairs (MARA), and related agriculture management sectors, in the form of detailed bi-weekly reports submitted to MARA throughout the growing season. The optimizations being undertaken in this project allows the use of advanced Data Assimilation (DA) techniques on a regional/national scale which had previously been impossible due to the high computational demand of the algorithms. Understanding seasonal crop production and yield outcomes for each growing season helps with national scale planning of food production, and gives advanced warning of any significant imbalance between food production and demand. This project has recently been awarded the Chair's Award of Newton Prize 2019. In Ghana, 13 million people are employed in the rural sector and many of them are smallholder farmers that still rely on subsistent rain-fed agriculture. The funded activities address a pressing need in Ghana to improve the levels of crop performance information for yield optimization and national production planning. Built upon a series of previous ODA projects, our work will help Ghana in building its capacity in smallholder cropland monitoring, as an example to address global challenges in reducing poverty and gender inequality. The monitoring capacity will also make a stride in addressing sustainable farming in Ghana and further enable Ghana's ability to mitigate the impact of climate change on agriculture, thus enhancing food security and agricultural sustainability. Having timely and accurate growth information on their main food crop would significantly improve management practices and help to avoid loss for food-crop farmers that are dominated by females in Ghana. The project undertaken will contribute to agricultural production monitoring in Ghana and bring a series of tangible impacts at farm/local scale, benefit stakeholders at the national level, make impacts worldwide, and contribute to the global scientific communities.

    Qingling currently (AY 2020-21) teaches:

    • GEOG0027 - Environmental Remote Sensing
    • GEOG0111 – Scientific Computing (Python)

    She has convened the MSc in Remote Sensing & Environmental Mapping programme and taught the following modules during AY 2019-20:

    • GEOG0027 - Environmental Remote Sensing
    • GEOG0040 – Principles & Practice of Remote Sensing
    • GEOG0110 – Analytical and Numerical Methods
    • GEOG0111 – Scientific Computing (Python)
    • GEOG0113 - Terrestrial Carbon: Modelling and Monitoring


      Modules taught in the past:

      • GEOGG125 – Principles of Spatial Analysis
        • GEOGG142 – Global Monitoring of Environment and Society (one lecture)
        • GEOG1006 – Ideas in Geography (four lectures)
        • GEOGG120 – Models in Environmental Science (one lecture)
        • GEOG3065/GEOGG153 – Geodemographics and Population Geography
        • GEOG2025 – GIS and Geodemographics

        I have advised or co-advised four MSc students in the past. This year, I'm supervising five MSc students and 2nd supervising one bachelor student.