Deep learning-based phenology extraction and crop classification in arid oasis using Sentinel-2 time series

Abstract

Multi-temporal remote sensing data in large-scale crop phenology identification and classification have become increasingly utilized, particularly for precision management in arid oasis agricultural regions with complex cropping systems. In this study, we developed a deep learning framework integrating Sentinel-2 multi-temporal imagery and normalized difference vegetation index (NDVI) time series for mapping cotton, winter jujube, and tiger nut crops in Tumushuke City, Xinjiang Uygur Autonomous Region, China. We employed the minimum redundancy maximum relevance (mRMR) algorithm for spectral and vegetation index feature selection, followed by Savitzky-Golay (S-G) filtering and double logistic function fitting, to automatically extract the key phenological parameters (start of season (SOS), peak of season (POS), and end of season (EOS)), significantly improving phenological feature extraction accuracy. By incorporating multi-temporal Sentinel-2 data and a multi-scale feature fusion approach, we could systematically compare five classification models (multi-layer perceptron (MLP), residual network-18 (ResNet-18), convolutional long short-term memory (ConvLSTM), Transformer, and random forest classifier (RFC)), demonstrating that high-resolution spatial details substantially enhance crop boundary delineation and classification consistency in complex environments. Further optimization of Transformer's spatial representation through multi-scale window analysis revealed that the use of 1×1+3×3+5×5 convolutional windows achieves an optimal balance between accuracy and computational efficiency. Independent validation on unseen areas confirmed robust model transferability, with F1 scores of 94.37%, 87.75%, and 86.35% for the three crops (winter jujube, cotton, and tiger nut), respectively. This study validates the high-precision identification potential of Sentinel-2 temporal data and deep neural networks for multi-crop environments, enabling the precise spatial mapping of crop distributions and providing methodological support for smart agricultural decision-making in arid oasis regions.

Plant phenotyping relevance

Sentinel-2時系列から植物の生育季節性（SOS、POS、EOS）を自動抽出し、その精度向上と独立地域での検証を行っており、作物分類を含む解析ワークフローにおける植物フェノタイプ抽出が中心的です。

Code and data availability

The supplied blocks describe Sentinel-2 imagery, RTK field survey samples, TIMESAT/S-G phenology extraction, and deep learning models, but contain no public dataset deposit, author code release, trained model checkpoint, or supplement with such assets. No availability statements or author URLs appear, and allowed_urls,

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