Sugarcane Phenology Retrieval in Heterogeneous Agricultural Landscapes Based on Spatiotemporal Fusion Remote Sensing Data

文献类型： 外文期刊

第一作者： Yang, Yingpin

作者： Yang, Yingpin;Wu, Zhifeng;Wang, Dakang;Yang, Xiankun;Wang, Yibo;Wang, Jinnian;Hou, Lu;Wang, Zongbin;Chang, Xu;Yang, Yingpin;Wu, Zhifeng;Wang, Dakang;Yang, Xiankun;Wang, Yibo;Wang, Jinnian;Hou, Lu;Wang, Zongbin;Chang, Xu;Wang, Cong;Huang, Qiting

作者机构：

关键词： sugarcane; phenology retrieval; time series; remote sensing; spatiotemporal fusion; NDVI

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 15 期

页码：

收录情况： SCI

摘要： Accurate phenological information on sugarcane is crucial for guiding precise cultivation management and enhancing sugar production. Remote sensing offers an efficient approach for large-scale phenology retrieval, but most studies have primarily focused on staple crops. The methods for retrieving the sugarcane phenology-the germination, tillering, elongation, and maturity stages-remain underexplored. This study addresses the challenge of accurately monitoring the sugarcane phenology in complex terrains by proposing an optimized strategy integrating spatiotemporal fusion data. Ground-based validation showed that the change detection method based on the Double-Logistic curve significantly outperformed the threshold-based approach, with the highest accuracy for the elongation and maturity stages achieved at the maximum slope points of the ascending and descending phases, respectively. For the germination and tillering stages with low canopy cover, a novel time-windowed change detection method was introduced, using the first local maximum of the third derivative curve (denoted as Point A) to establish a temporal buffer. The optimal retrieval models were identified as 25 days before and 20 days after Point A for germination and tillering, respectively. Among the six commonly used vegetation indices, the NDVI (normalized difference vegetation index) performed the best across all the phenological stages. Spatiotemporal fusion using the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) significantly improved the monitoring accuracy in heterogeneous agricultural landscapes, reducing the RMSE (root-mean-squared error) by 21-46%, with retrieval errors decreasing from 18.25 to 12.97 days for germination, from 8.19 to 4.41 days for tillering, from 19.17 to 10.78 days for elongation, and from 19.02 to 15.04 days for maturity, highlighting its superior accuracy. The findings provide a reliable technical solution for precision sugarcane management in heterogeneous landscapes.

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