Combining Vegetation Indices to Identify the Maize Phenological Information Based on the Shape Model

Huizhu Wu, Bing Liu, Bingxue Zhu (), Zhijun Zhen, Kaishan Song and Jingquan Ren
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Huizhu Wu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Bing Liu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Bingxue Zhu: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Zhijun Zhen: College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
Kaishan Song: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Jingquan Ren: Institute of Meteorological Science of Jilin Province, Changchun 130062, China

Agriculture, 2024, vol. 14, issue 9, 1-18

Abstract: Maize is the world’s largest food crop and plays a critical role in global food security. Accurate phenology information is essential for improving yield estimation and enabling timely field management. Yet, much of the research has concentrated on general crop growth periods rather than on pinpointing key phenological stages. This gap in understanding presents a challenge in determining how different vegetation indices (VIs) might accurately extract phenological information across these stages. To address this, we employed the shape model fitting (SMF) method to assess whether a multi-index approach could enhance the precision of identifying key phenological stages. By analyzing time-series data from various VIs, we identified five phenological stages (emergence, seven-leaf, jointing, flowering, and maturity stages) in maize cultivated in Jilin Province. The findings revealed that each VI had distinct advantages depending on the phenological stage, with the land surface water index (LSWI) being particularly effective for jointing and flowering stages due to its correlation with vegetation water content, achieving a root mean square error (RMSE) of three to four days. In contrast, the normalized difference vegetation index (NDVI) was more effective for identifying the emergence and seven-leaf stages, with an RMSE of four days. Overall, combining multiple VIs significantly improved the accuracy of phenological stage identification. This approach offers a novel perspective for utilizing diverse VIs in crop phenology, thereby enhancing the precision of agricultural monitoring and management practices.

Keywords: optical remote sensing; crop phenology; land surface water index (LSWI) (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: 2024
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