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UCL Home  /  Geography  /  People  /  Research Students  /  Feng Yin

Feng Yin

Contact

Feng-Yin.jpgRoom 106,
UCL Department of Geography,
University College London,
Gower Street,London,
WC1E 6BT

feng.yin.15@ucl.ac.uk

 

 

 

 

 

 

Research

Research Topic: Next Generation Crop Monitoring and Forecasting system

Food security remains one of the key global challenges in this century. The increasing food consumption driven by the growing global population, negative effects from climate changes and ever sever resources scarcity, along with other adverse factors, will consequently worsen the situation in the coming decades. In the meantime, sustainable intensification has been proposed to replace the unsustainable crop practices used in green revolution over the last half century. Under the increasing pressures over world food supplies, timely, comprehensive, transparent, and accurate information on crop production are critical. Since 1970s remote sensing technology has used to monitor crop growth and estimate crop yields, because it can supply measurements related to instantaneous values of various canopy state variables. Unfortunately, inconsistent methods and data are used in current crop monitoring systems and yield gap analysis. Meanwhile, a decision support system, which has local relevance to individual farmland, is yet to exist to determine the potential risks and suggest effective procedures for all level of users in the crop system. Lastly, equal accessibility for farmers cross countries and regions should be one indispensable characteristic for any solution in securing future food status, as the benefits of Green Revolution have been unequally shared and show large inter-regional disparities.

We propose a practical while effective crop production monitoring and forecasting system over regional to national scale with field level details. Data input to the system are Copernicus Sentinel 2 and Sentinel 1 time series images, which has 10-60 meters spatial resolution to allow information derived from them are relevant to individual farmland even smallholdings with hundreds m^2 area. State-of-art radiative transfer models are used to reduce the atmospheric effects on optical measurements and biophysical parameters retrievals for both optical and microwave data, which guarantees the compatibility and interoperability of data and information used in our system. Crop simulation model, WOFOST, is used to bridge time series of biophysical parameters to the final crop yield, which is also mechanistic and has high level of explainability and interoperability than the empirical methods. Machine learning methods are used to emulate radiative transfer models to allow for fast mapping from satellite measurements to crop state parameters. Since this system involves large volume of data inputs and extensive computation that can be hardly handled by most of the users, the whole system has been deployed on Google Earth Engine, which provides all the required data as well as planetary-scale analysis capabilities for free to the public. Another very important characteristic of this system is that it is fully compatible to the Open-data policy, which serves the basics of equal accessibility to this system for all users. Currently, this system has been tested over North China Plain with an area of more than 400,000 square kilometers, and we are able to deliver winter wheat yield maps with 10 meters spatial resolution for last three years. Application of this system will be further expanded to Ghana to help relieve the food security in that country.

Project website: https://www.geog.ucl.ac.uk/research/research-projects/sentinels-of-wheat

Supervisor: Professor Philip Lewis and Professor Mat Disney

Background

2019 – Present, University College London
PhD student in Remote Sensing

2015 – 2016, University College London
MSc in Remote Sensing (Distinction)

2011 – 2015, China University of Geosciences, Beijing
BEng in Resource exploration

Publications

Paper:

Yin, F., Lewis, P. E., Gomez-Dans, J., & Wu, Q. (2019, February 21). A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI. https://doi.org/10.31223/osf.io/ps957