Speaker
Description
- Introduction
- Study area, data and methods
2.1 Study area
2.2 Used data and their processing - Results and discussion
3.1 Comparison of the radar vegetation index (RVI) with normalized difference vegetation index (NDVI)
3.2 Possibility to delineate the differences between high and low yield patterns - Conclusion
Summary
Crop assessment and monitoring with optical sensor imagery is limited by cloud cover and therefore high acquisition gaps. In contrast, radar images are not influenced by weather conditions thus offer dense time series. In April 2014 and 2016, the European Space Agency launched the new synthetic aperture radar (SAR) satellites Sentinel-1A and Sentinel-1B. Their sensors feature an enhanced radiometric and spatial resolution as well as a fundamentally improved temporal coverage compared with previous (C-band) SAR satellites.
In this study, 105 Sentinel-1A/B co- and cross-polarized (VH/VV) scenes acquired over Thuringia were analysed to retrieve parameters for wheat monitoring (summer/winter barley) and detection of disturbances inside the fields. Additional information, such as meteorological data (rainfall, temperature) and yield information have been integrated into this project.
The radar vegetation index (RVI) was investigated as an alternative to the normalized difference vegetation index (NDVI) to estimate phenological stages of the crops. The RVI has shown very similar trends compared to NDVI.
We used in situ yield information to assess the possibilities to delineate the differences between high and low yield patterns in the Sentinel-1 time series data. We have examined that the direct use of the co- and cross-polarized SAR data has a higher distinguishability between high and low yield than the RVI.
