Abstract
Crop lodging, the tilting of stems from their natural upright position, usually occurs after a heavy storm event. Since lodging of a crop seriously affects its yield, rapid assessment of crop lodging is valuable for farmers, policymakers, agronomists, insurance companies, and relief workers. Synthetic Aperture Radar (SAR) sensors have been recognized as valuable data sources for mapping lodging extent because of their good penetrating power and high-resolution remote sensing ability. Compared to other sources, SAR’s weather and illumination independence and large area coverage at fine spatial resolution (3 m to 20 m) support frequent and detailed observations. Because of these advantages, SAR has the potential in supporting near real-time monitoring of lodging in fields when combined with automated image processing. In this study, a method based on change detection using modified Hidden Markov Random Field (HMRF) and Sentinel-1A data were utilized to identify lodging and map its extent. Results obtained have shown that when lodging occurs, the VH polarization’s backscatter (σVH) increases between the pre-lodging event image and the post-lodging event image. The increase in σVH is due to the increase in volume scattering and vegetation-soil double bounce scattering resulting from the structural changes in the crop canopy. Using Sentinel-1A images and applying our proposed approach across several fields in Iowa and Illinois, we mapped the extent of the 2020 Derecho (wind storm) lodging disaster. In addition, we separated lodged regions into severely and moderately lodged areas. We estimated that approximately 2.56 million acres of corn and 1.27 million acres of soybean were lodged. Further analysis also showed the separation between un-lodged (healthy) fields and lodged fields. The observations in this study can guide future use of SAR-based information for operational crop lodging assessment.
Highlights
With the increase in global population and the increase in food demand, the monitoring of agricultural activities has been of utmost importance
The authors found that several polarimetric features, such as horizontal transmit and vertical receive (HV) intensity, double-bounce scattering, and volume scattering derived from RADARSAT-2 data were helpful in sugarcane lodging identification
Despite these and other studies carried out throughout the last decade, the integration of Synthetic Aperture Radar (SAR) remote sensing into routine mapping of lodging and severity assessment remains difficult for the following reasons: (a) The acquisition of SAR dataset to coincide with the specific date of lodging is not always feasible; (b) The heterogeneous distribution of lodging makes it difficult to be detected
Summary
With the increase in global population and the increase in food demand, the monitoring of agricultural activities has been of utmost importance. Chauhan, et al [14], explored the advantage of multi-sensor SAR data (Sentinel-1 and RADARSAT-2) to develop a quantitative approach to detect crop lodging stages (moderate, severe, and very severe) based on the crop angle of inclination. The authors found that several polarimetric features, such as horizontal transmit and vertical receive (HV) intensity, double-bounce scattering, and volume scattering derived from RADARSAT-2 data were helpful in sugarcane lodging identification Despite these and other studies carried out throughout the last decade, the integration of SAR remote sensing into routine mapping of lodging and severity assessment remains difficult for the following reasons: (a) The acquisition of SAR dataset to coincide with the specific date of lodging is not always feasible; (b) The heterogeneous distribution of lodging makes it difficult to be detected. There is no method available for corn and soybean lodging over large spatial areas
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