Lodging in wheat is one of the main constraints limiting yield and grain quality. Accurate information about crop lodging susceptibility during the growing season is critical for improving yield estimates and for targeting the expenditure on lodging control. In this context, this study aims to estimate safety factor against root lodging (SFA) as a measure of lodging susceptibility by exploiting Sentinel-1 data using Extreme Gradient Boosting Regression. Through extensive field experiments during a crop season, several crop variables were collected from several plots in multiple visits, and the corresponding metrics were extracted from the Sentinel-1 images. Our results show that the field measured SFA correlated well with the field lodging and the cross-validated regression model could estimate SFA with an R2cv = 0.73 and RMSEcv = 0.59. Thus, the SFA measure constitutes a state-of-the-art approach in the remote sensing community for the assessment of root lodging susceptibility.
|Number of pages||3|
|Publication status||Published - 12 Jul 2021|
|Event||IEEE- International Geoscience and Remote Sensing Symposium- IGARSS 2021 - Brussels|
Duration: 12 Jul 2021 → 16 Jul 2021
|Conference||IEEE- International Geoscience and Remote Sensing Symposium- IGARSS 2021|
|Period||12/07/21 → 16/07/21|