农业机械学报2026，Vol.57Issue(17)：54-64,103,12.DOI:10.6041/j.issn.1000-1298.2026.17.005
基于Sentinel-1/2时序数据与历史信息融合的甘蔗分布提取方法
Sugarcane Distribution Extraction Method Based on Sentinel-1/2 Time-series Data and Historical Information Fusion
摘要
Abstract
Accurately mapping sugarcane planting distribution is of great significance for monitoring the sugar crop industry and assessing sugar supply security in China.To address the limitation of existing methods that primarily relied on single-year remote sensing observations and failed to fully utilize multi-year continuous planting prior information,the Chongzuo City in Guangxi Zhuang Autonomous Region was selected as the study area.A multi-branch long short-term memory(LSTM)classification framework integrating multi-year historical information was constructed based on time-series data from Sentinel-2 L2A and Sentinel-1 SAR imagery.Two strategies for incorporating prior information were systematically compared:the historical data strategy(using raw remote sensing features from the previous n years)and the historical probability strategy(using continuous sugarcane probability maps output by a baseline model for the previous n years).The classification performance was evaluated for four target years from 2022 to 2025.The results demonstrated that introducing three years of historical information significantly improved classification accuracy across all data source schemes.Among all configurations,the fusion of Sentinel-1 and Sentinel-2 combined with three years of historical data achieved the best performance,with an average F1-score of 90.6％,a Kappa coefficient of 0.854,representing an improvement of approximately 8.3 percentage points in F1-score compared with the single-year baseline model.Under the three-year retrospective condition,both the historical data and historical probability strategies yielded F1-scores consistently above 90％,showing high consistency between the two approaches.As the retrospective period increased from one to three years,the classification accuracy exhibited an overall upward trend,and this optimal retrospective period of three years coincided well with the typical three-year agronomic cycle of sugarcane cultivation in Guangxi,characterized as"one year of newly planted cane followed by two years of ratoon cane".Furthermore,the fusion of Sentinel-1 and Sentinel-2 data did not demonstrate a significant synergistic effect in the baseline scenario without historical information;however,after incorporating three years of historical information,the fusion scheme significantly outperformed the optical-only scheme,indicating that the structural sensitivity of SAR data can be fully leveraged with the support of temporal prior information.Based on the optimal scheme,it generated 10-meter resolution sugarcane distribution maps for Chongzuo City from 2022 to 2025,confirming the feasibility and effectiveness of the proposed method for multi-year continuous sugarcane mapping.关键词
甘蔗/分布提取/长短期记忆网络/Sentinel-1/Sentinel-2/时序分类Key words
sugarcane/distribution extraction/long short-term memory network/Sentinel-1/Sentinel-2/time-series classification分类
农业科技引用本文复制引用
孙琬婷,何芸,朱秀芳,李乐..基于Sentinel-1/2时序数据与历史信息融合的甘蔗分布提取方法[J].农业机械学报,2026,57(17):54-64,103,12.基金项目
广东省基础与应用基础研究基金项目(2023A1515010897) （2023A1515010897）
