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

Yingpin Yang, Zhifeng Wu, Dakang Wang, Cong Wang, Xiankun Yang, Yibo Wang (), Jinnian Wang, Qiting Huang, Lu Hou, Zongbin Wang and Xu Chang
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Yingpin Yang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Zhifeng Wu: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Dakang Wang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Cong Wang: School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Xiankun Yang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Yibo Wang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Jinnian Wang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Qiting Huang: Agricultural Science and Technology Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
Lu Hou: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Zongbin Wang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China
Xu Chang: Institute of Aerospace Remote Sensing Innovations, Guangzhou University, Guangzhou 510006, China

Agriculture, 2025, vol. 15, issue 15, 1-17

Abstract: 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.

Keywords: sugarcane; phenology retrieval; time series; remote sensing; spatiotemporal fusion; NDVI (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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