Yin, Feng; (2025) A new approach for crop monitoring using optical remote sensing. Doctoral thesis (Ph.D), UCL (University College London).

Text PhD_thesis_FengYin.pdf - Accepted Version Access restricted to UCL open access staff until 1 July 2026. Download (252MB)

Abstract

Food security remains a key challenge worldwide in the 21st century. There is a growing recognition of the potential of using Earth Observation (EO) data to monitor crops and provide valuable information to improve global food security. This thesis focuses on extracting information related to crop growth status and crop yield from EO data. The key contributions of this work include: 1. Develop a novel atmospheric correction method called SIAC. The proposed method produces surface reflectance that is consistent and interoperable between different optical sensors and provides per-pixel uncertainty information. /./ 2. Build a new dynamic crop canopy radiative transfer model to extract biophysical properties from Sentinel-2 (S2) time series data. This approach allows for better constraint of biophysical parameter retrieval from EO data, addressing the well-known issues of equifinality and underdeterminedness in such retrievals. The model can handle gaps and noise within the S2 time series, providing robust and gap-free estimates of PROSAIL biophysical parameters. /./ 3. Estimate and predict field-level crop yields at national to regional scales using the retrieved crop biophysical properties and the aggregated yield statistics with limited access to field-level yield data. Estimated yield maps exhibit significant spatial variations, which are not captured by aggregated statistics. Yield gap analysis shows fields with consistently lower or higher yields over multiple years. Furthermore, it demonstrates that in-season yield prediction can be achieved with archetype model parameters updated with new S2 observations throughout the crop growth season. /./ This work demonstrate how different types of crop information can be systematically extracted from EO data. The methodologies developed in this thesis can form the foundation of a comprehensive crop monitoring system capable of delivering a wide range of crop-related information. However, generating actionable insights will require the integration of other data sources and the further development of new models.

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