Quantitative monitoring of sugarcane typhoon disaster based on multi-source remote sensing data

With a foreseen increase in the number of agrometeorological disasters due to climate change, especially in the field of crop lodging. This paper presented an innovative monitoring technique to explore the application potential of muti-source remote sensing data. Based on the sugarcane planting area extracted from sentinel-1 time-series data and combination of Landsat-8 and sentinel-2 MSI images before and after Super Typhoon Hato, a vegetation index distance leveling method was come out and then was applied to assess the sugarcane lodging in Dagang Town, Nansha District, Guangzhou City. The region was caused by strong wind and rainstorm on August 23, 2017. The validation results showed that the multi-temporal Sentinel-1 image data can effectively extract the sugarcane planted area before and after lodging with an accuracy of 87.83%. Compared with other vegetation indices (RVI/LSWI/NBR/EVI/DVI), NDVI was the most sensitive in response to sugarcane lodging. The validation accuracy of extracting farmland damage extent reached 71.64%, among them, the affected area of sugarcane reached 711.33 ha. The study further illustrates the capability of the image vegetation index difference method on monitoring of sugarcane lodging degree at the reginal scale.