Improved corn phenology monitoring using translation and weighting of characteristic points from time-series vegetation index

Abstract

Crop phenology plays a vital role in field management and yield prediction of crops. The current remote sensing phenology identification methods utilize characteristic points extracted from time-series vegetation index curves to directly correspond to the beginning of growth stages. However, due to the differences between the meaning of remotely sensed phenological dates and ground-observed phenological stages, there may be certain systematic errors in phenology identification using this method. Therefore, the study proposed a novel phenology extraction framework for crop phenological stages, which does not directly correspond to the dates of remote sensing characteristic points extracted from NDVI curves to the ground phenological stages, but establishes functions between them to improve monitoring accuracy, including single-characteristic point translation method (SCTM) and double-characteristic points weighting method (DCWM). The two methods were applied for monitoring the corn phenology in 12 states in the United States using MODIS. The results showed that DCWM had a better performance than SCTM in phenology extraction, and both of them were superior to the conventional method in which the characteristic points directly correspond to the crop phenological stage. Combining the two methods, the optimal RMSEs of Emerged, Silking, Dough, Dented, Mature and Harvested were 5.28 days, 3.44 days, 4.65 days, 3.88 days, 4.09 days and 6.73 days. Compared with the results from direct correspondence method, they were decreased by 80.48 %, 41.69 %, 40.15 %, 22.55 %, 13.53 % and 29.38 %. The R² also increased by 20.51 %, 9.52 %, 8.93 %, 17.74 %, 16.67 %, 3.03 %, respectively. The framework proposed in this study is a further in-depth study based on the extraction of remote sensing characteristic points, which significantly improves the monitoring accuracy of corn phenological stages, and provides technical enlightenment for the precise phenological extraction in future study.