Abstract: To quickly and effectively obtain crop lodging information, this study proposed a remote sensing method for monitoring maize lodging using HJ-1(Small Satellite Constellations for Environment and Disaster Monitoring and Forecasting) CCD data. This paper took one area in Gongzhuling, Jilin Province as an example, where large-scale maize lodging occurred in 2012, caused by Typhoon Bolaven. The spectral characteristics of lodging and normal maize were first analyzed and summarized before and after the Typhoon. The results showed that compared with the normal field, the canopy reflectance increased in chromatic and near-infrared bans, but the vegetable index decreased in the lodged field. Four vegetation indices and a principal component were calculated, which extracted from 4 bans spectral data set. Binary Logistic models of lodging and normal maize classification were constructed with these 5 varieties. The prediction accuracies of the classification models were measured by ground survey samples. The principal component model could get the optimal results of recognition, and the classification accuracy on the test set was 96.23%. The classification accuracies of NDVI model and RVI model followed, the classification accuracies were about 80%. Finally, the principal component model was applied to recognize maize lodging using the spectral image, and the results confirmed that the proposed modes can accurately predict the distribution of maize lodging. The proposed maize lodging recognition method based on binary Logistic, provided a theoretical basis for monitoring large-scale lodged maize filed using multispectral data.

Key words: Remote sensing, Small satellite constellations for environment and disaster monitoring and forecasting, Disaster, Maize, Lodging