In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery

Abstract

Sugarcane is a significant crop in terms of annual biomass in the world. Timely and accurate mapping of sugarcane planting is important for food security and sustainability. However, accurately remote-sensing-based mapping sugarcane remains challenging due to two reasons: 1) the scarcity of sugarcane training samples, and 2) the diverse sugarcane fields planting dates. This article proposed a novel transfer learning algorithm for accurate sugarcane planting mapping through space and temporary in the U.S. This algorithm only depended on one place's training data to mapping other places’ growing sugarcane. We distilled burning sugarcane fields as training labels from Sentinel-2 in Palm Beach County and neighboring area in 2021. The time-invariant phenology features were calculated from composite Sentinel-2 normalized difference vegetation index (NDVI) series using linear cosine regression (LCR). They integrated as training samples to construct a one-class support vector machine (OCSVM) classifier, generating Jun.–Nov. growing sugarcane maps in Plam Beach County and Lafourche County 2022. Meanwhile, a postprocess method was used to improve mapping quality. These sugarcane maps were validated using surveyed and observed field dataset and compared against the cropland data layer (CDL). As a result, the sugarcane maps achieved the best accuracy in its mature stage (Sep. 2022), which exhibited nearly complete phenological characteristics. The maps show low misclassification rates and higher accuracy compared to the CDL 2022. Moreover, the LCR-OCSVM method was confirmed to have superior transfer learning capability for sugarcane classification across different regions and periods compared to the Harmonic-OCSVM method.

Keywords

• Burning sugarcane
• linear cosine regression
• one-class support vector machine
• sugarcane mapping