AUTHOR=Zhang Hongchi , Peng Dailiang , Dou Changyong , Lou Zihang , Zhang Xiaoyang , Yu Le , Song Kaishan , Zhang Yaqiong , Hu Jinkang , Zheng Shijun , Lv Yulong , Liu Shengyi , Zhang Yizhou , Peng Hao 

TITLE=Enhancing early-season soybean identification through optical and SAR time-series integration

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1656628

DOI=10.3389/fpls.2025.1656628

ISSN=1664-462X

ABSTRACT=Soybean is an important grain and cash crop in China, and timely knowledge of its distribution is crucial for food security. However, traditional survey methods are time-consuming and limited in coverage. In contrast, satellite remote sensing enables large-scale, continuous, and cost-effective monitoring, providing reliable support for crop classification and yield forecasting. However, the high spectral similarity between soybean and maize during key phenological stages presents a major challenge for reliable classification. To address this, we propose a multi-source remote sensing approach that integrates Sentinel-1 SAR and Sentinel-2 optical time-series imagery. This method combines statistical descriptors, harmonic fitting parameters, phenological indicators, and radar-based features within a random forest classifier to achieve accurate soybean mapping. The study was conducted in the Jiusan Reclamation Area of Heilongjiang Province using satellite imagery from May to October 2019 for multi-source classification and temporal analysis. We systematically evaluated classification performance across different data sources and phenological stages and introduced the Earliest Identifiable Time (EIT) metric to assess temporal detection capabilities. Results show that the multi-source fusion approach outperforms single-source methods, achieving an overall accuracy (OA) of 96.85%, a Kappa coefficient of 0.9493, and an F1-score of 95.84% for soybean. Notably, SAR data significantly improved classification during the flowering stage—when optical imagery is often constrained—resulting in a maximum F1-score increase of 6.96%. Soybean classification accuracy increased rapidly with crop development, and the EIT was advanced to Day of Year (DOY) 210, approximately 20 days earlier than with optical data alone. These findings demonstrate the effectiveness of multi-source remote sensing in enhancing both the accuracy and timeliness of crop classification under complex climatic conditions, offering valuable support for precise soybean mapping and in-season monitoring.