A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data

Abstract

Sugarcane is a major crop for sugar and biofuel production. Historically, mapping large sugarcane fields meticulously depended heavily on gathering comprehensive and representative training samples. This process was time-consuming and inefficient. Addressing this drawback, this study proposed a novel index, the Normalized Difference Vegetation Index (NDVI)-Based Sugarcane Index (NBSI). NBSI analyzed the temporal variation of sugarcane’s NDVI over a year. Leveraging the distinct growth phases of sugarcane (transplantation, tillering, rapid growth and maturity) four measurement methodologies, f(W1), f(W2), f(V) and f(D), were developed to characterize the features of the sugarcane growth period. Utilizing imagery from Landsat-8, Sentinel-2, and MODIS, this study employed the enhanced gap-filling (EGF) method to reconstruct NDVI time-series data for seven counties in Chongzuo, Guangxi Zhuang Autonomous Region, during 2021, subsequently testing NBSI’s ability to extract sugarcane. The results demonstrate the efficiency of NBSI with simple threshold settings: it was able to map sugarcane cultivation areas, exhibiting higher accuracy when compared to traditional classifiers like support vector machines (SVM) and random forests (RF), with an overall accuracy (OA) of 95.24% and a Kappa coefficient of 0.93, significantly surpassing RF (OA = 85.31%, Kappa = 0.84) and SVM (OA = 85.87%, Kappa = 0.86). This confirms the outstanding generalizability and robustness of the proposed method in Chongzuo. Therefore, the NBSI methodology, recognized for its flexibility and practicality, shows potential in enabling the extensive mapping of sugarcane cultivation. This heralds a new paradigm of thought in this field.