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

## TL;DR

This paper shows how combining satellite radar and optical data improves early identification of soybean crops in China, making crop monitoring more accurate and timely.

## Contribution

The study introduces a novel multi-source remote sensing method integrating SAR and optical time-series data for enhanced soybean classification.

## Key findings

- The multi-source fusion approach achieved 96.85% overall accuracy in soybean classification.
- SAR data improved classification during the flowering stage by up to 6.96% in F1-score.
- The earliest identifiable time for soybean was advanced to Day of Year 210 using the new method.

## 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.