Monitoring apple flowering date at 10 m spatial resolution based on crop reference curves

文献类型： 外文期刊

第一作者： Duan, Mengqi

作者： Duan, Mengqi;Sun, Liang;Liu, Yu;Yang, Peng;Wang, Zhao

作者机构：

关键词： Crop reference curves; Apple flowering date; NDVI reconstruction; Remote sensing; 10 m spatial resolution; Flowering date monitoring

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：7.7； 五年影响因子：8.4 ）

ISSN： 0168-1699

年卷期： 2024 年 225 卷

页码：

收录情况： SCI

摘要： Apple cultivation is a mainstay industry that promotes agricultural development and boosts farmers' income in Shaanxi Province. Monitoring the flowering date of apple trees is essential for frost damage prevention and yield assessment. However, conventional ground survey methods suffer from high costs and low accuracy, and traditional approaches relying on meteorological data have limitations in spatial resolution. In this study, a set of Normalized Difference Vegetation Index (NDVI) time series, referred to as Crop Reference Curves (CRC), was extracted from pure apple tree MODIS pixels. Subsequently, this CRC was utilized to reconstruct daily 10 m NDVI data from Sentinel-2 imagery. By comparing the spatial phenological variances between the CRC and the reconstructed NDVI sequence, the historical apple flowering date was monitored and mapped with a 10 m spatial resolution in Shaanxi Province. Furthermore, we compared and analyzed the effects of Sentinel-2 images input number (5, 6, 8, and 9 scenes) on the accuracy of flowering monitoring. The results revealed that the scheme using 8 images with an average annual distribution yielded an absolute error of 2 days in monitoring the flowering date in six counties of Yan'an in 2019, indicating an effective fitting effect on monitoring the apple flowering date. The scheme employing 9 images achieved an absolute error of 1.33 days, offering the highest precision in monitoring apple flowering date. Furthermore, when using 9 images, the average error in flowering monitoring remained within 2 days from 2019 to 2021 in four validation study areas, demonstrating strong fitting and practical applicability for monitoring apple flowering dates. This method can be utilized for rapid, efficient and high-precision monitoring of apple flowering date in a wide range with a 10 m spatial resolution. Additionally, the analysis of reconstructed NDVI characteristic can serve as a technical reference for fruit forest classification and growth trend prediction.

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