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Integration of soil moisture data with Vegetation Condition Temperature Indices to improve drought mapping and crop yield estimation in China using Copernicus Sentinel data (CENTA2-SGGE20-TANS)

  • Full or part time
  • Application Deadline
    Friday, January 10, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Drought is one of the major concerns for the sustainability and socio-economic development in rural areas in China. Monitoring of crop growth conditions is important for economic development and to avoid food production issues. The use of remotely sensed data has proved to be very important in agricultural crop growth monitoring and irrigation scheduling. The prediction of crop yields have direct impact on food management strategies [1]. The yield of a crop is influenced by many factors during its growth cycle. To better estimate yield and provide information that may help with mitigating negative factors that reduce yield, monitoring of the crop’s biophysical properties, at frequent periods if necessary.

We can undertake this monitoring in situ, from a drone, or from an aircraft. However, if we want to do this at scale, over vast agricultural plains in China for example, then we need to use satellite data. Previously, we have had to rely on this monitoring using coarse-resolution instruments (minimum 500m pixel size). However, in the last 3 years, a new generation of European satellites, built as part of the Copernicus programme has provided unprecedented levels of data at high repeat frequency (6-days) and higher resolution (20m) at thermal, optical and radar wavelengths.

The aim of this PHD project is to develop methods to integrate estimates of soil moisture content and crop water content with existing approaches that includes the Vegetation Temperature Condition Index (VTCI), a drought monitoring index developed using Normalized-Difference-Vegetation-Index (NDVI) and Land-Surface-Temperature (LST) values. These modelled estimates will come primarily from the Sentinel-1 C-band radar mission and integrated into water balance/soil wetness models. The primary study area will be the Guanzhong Plain, located in Shaanxi Province, China and covering an area of ~12,000 km2.

The student will be expecting to spend 6 months in China, working primarily with the group of Prof. Wang at China Agricultural University. Here she/he will undertake field work and also develop further training on the nature of agricultural systems, field and crop patterns and current machine learning approaches. The costs associated with this visit to China are fully covered.

Entry Requirements:

UK Bachelor Degree with at least 2:1 in a relevant subject or overseas equivalent.

Available for UK and EU applicants only.

Applicants must meet requirements for both academic qualifications and residential eligibility: http://www.nerc.ac.uk/skills/postgrad/

How to Apply:

Please follow refer to the How to Apply section at http://www2.le.ac.uk/study/research/funding/centa/how-to-apply-for-a-centa-project and use the Geography Apply button to submit your PhD application.

Upload your CENTA Studentship Form in the proposal section of the application form.

In the funding section of the application please indicate you wish to be considered for NERC CENTA Studentship.

Under the proposal section please provide the name of the supervisor and project title/project code you want to apply for.

Funding Notes

This project is one of a number of fully funded studentships available to the best UK and EU candidates available as part of the NERC DTP CENTA consortium.

For more details of the CENTA consortium please see the CENTA website: View Website.

Applicants must meet requirements for both academic qualifications and residential eligibility: View Website

The studentship includes a 3.5 year tuition fee waiver at UK/EU rates

An annual tax free stipend (For 2019/20 this is currently £15,009)

Research Training Support Grant (RTSG) of £8,000.

References

[1] Prasad, A. K. , Chai, L. , Singh, R. P., and Kafatos, M. (2006) ‘Crop yield estimation model for Iowa using remote sensing and surface parameters’, International Journal of Applied Earth Observation and Geoinformation, 8, 26-33, https://doi.org/10.1016/j.jag.2005.06.002

[2] Zheng, Y., Ren, H., Guo, J., Ghent, D., Tansey, K., Hu, X., Nie, J., and Chen, S. (2019) ‘Land surface temperature retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, using a split-window algorithm’, Remote Sensing, 11, 650, https://doi.org/10.3390/rs11060650

[3] Wang, H., Liu, X., Zhang, X., Wang, P., Lin, H., and Yu, L. (2018) ‘Spatiotemporal crop NDVI responses to climatic factors in mainland China’, International Journal of Remote Sensing, 40, 1-15, https://doi.org/10.1080/01431161.2018.1500725

[4] Xun, L., Wang, P., Li, L., Wang, L., and Kong, Q. (2018) ‘Identifying crop planting areas using Fourier-transformed feature of time series MODIS leaf area index and sparse-representation-based classification in the North China Plain’, International Journal of Remote Sensing, 40, 1-19, https://doi.org/10.1080/01431161.2018.1492181

[5] Sun, W., Wang, P., Zhang, S., Zhu, D., Liu, J., Chen, J., Yang, H. (2008) ‘Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China’, International Journal of Remote Sensing, 29, 5133-5144, https://doi.org/10.1080/01431160802036557

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