Remote sensing of regional-scale maize lodging using multitemporal GF-1 images

Lodging stress affects the yield, quality, and mechanical harvesting capacity of maize, thereby resulting in a reduction in maize production. The timely monitoring of maize lodging assists in determining the extent and degree of the effects. This study proposed a method for this purpose using Gaofen-1 (GF-1) optical satellite images, which can be significant for lodging field management, production assessments, and implementation of the related remedial measures. The evaluation index of the lodging grade was constructed by measuring the proportion and angle of maize lodging after the lodging incident. Variations in spectral reflectance and vegetation index were calculated before and after lodging. The competitive adaptive reweighted sampling algorithm was used to screen the optimal combination of variables sensitive to the evaluation index of the maize lodging grade. A remote sensing monitoring model was established by using random forest (RF) and partial least squares (PLS). Results show that the maize lodging monitoring model established by RF is better than that of PLS. The accuracy of monitoring the lodging damage using the GF-1 images reaches 79%. Meanwhile, the classification map of the lodging grade we constructed using an RF model is consistent with the actual lodging area. Hence, this method can effectively characterize the intensity of lodging stress and can also be used to assess the range of damages caused by regional-scale lodging.