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Objective Soybean is the world’s most important legume crop that serves as a major source of high-protein food, the primary ingredient for livestock feed, and an essential source of edible oil. They play a crucial role in the world"s food production, with a global annual production of around 370 million metric tons. China is one of the major soybean producing countries globally, with an annual production of around 17 million metric tons. However, China"s domestic soybean production is insufficient to meet production and living needs, and it is highly reliant on imports, accounting for more than 80% of its soybean consumption. Consequently, China"s food security faces significant structural challenges. Remote sensing technology is a powerful tool for monitoring soybean cultivation and can provide basic data support for various countries to release signals of changes in agricultural product markets, strengthen the guidance of agricultural product markets, and formulate effective agricultural economic development strategies. The traditional method of estimating soybean planting area through agricultural surveys is usually time-consuming, labor-intensive, and subject to subjective factors, leading to inaccurate and imprecise results. In contrast, remote sensing technology utilizes satellite, aerial, or drone-based sensors to capture and analyze the electromagnetic radiation reflected or emitted by the Earth"s surface, providing a more objective and efficient way of monitoring crop planting areas. While methods based on vegetation indices time series and phenology are widely used for crop recognition including soybeans, and the focus has been primarily on the impact of the vegetation index time series feature or phenological feature in soybean recognition, and there has been limited research on the time series curve itself. There is a lack of analysis and research on the spectral characteristics of the key growth stages for soybeans, and no standard spectral time series curve for soybeans has been established to summarize their changing patterns. Additionally, mainstream crop recognition methods face difficulties in obtaining samples, especially in large-scale mapping, where the quality and quantity of samples are the main limiting factors. Method This study proposes a soybean recognition method based on the standard spectral time series curve on the Google Earth Engine (GEE) cloud platform. By adding the climate weight factor, the standard spectral time series curve of soybean can be accurately recognized. Result Using the features of the standard spectral time series curve combined with the random forest classifier, a soybean distribution map in Heilongjiang Province in 2020 was extracted. The classification confusion matrix shows that the overall accuracy of soybean recognition is 86.95%, the user accuracy is 90.91%, the producer accuracy is 86.14%, and the F1-Score is 0.8846. Compared to statistical area data, the area accuracy reaches 95.94%. Conclusion The study focuses on analyzing the difference between the spectral time series curves of soybean and corn and the impact of meteorological factors on the curves, and establishes a method for mapping the standard spectral time-series curve information to samples, thereby solving the problem of insufficient samples for mapping a large area of soybean. Furthermore, this paper designs experiments to verify the robustness of this soybean recognition method based on the standard spectral time series curve in terms of time scale and disaster situations. These results provide scientific evidence and technical support for the monitoring of soybean growth, disaster evaluation, and the formulation of international agricultural product trade strategies.