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

Abstract

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.

Plant phenotyping relevance

リンゴ樹の開花日という植物フェノタイプを、NDVI時系列とSentinel-2画像から推定・地図化する手法を開発し、画像枚数と精度を比較検証しており、フェノタイピング手法が中心である。

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