Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time-series Sentinel-2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel-2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ-measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world.

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大规模农作物种植识别的遥感分类方法：以咸海盆地为例

苏联解体以来，咸海盆地农作物种植结构发生了巨大变化，这些变化值得研究。然而，流域尺度的历史作物遥感制图仍然具有挑战性，特别是由于混合像素导致农田边缘的作物错误分类。因此，我们提出了一种基于时间序列Sentinel-2遥感影像和Google Earth Engine平台的田野分割方法来约束田野边缘，然后采用随机森林算法基于时间序列Landsat/Sentinel-2影像进行农作物分类和作物物候信息，以生成 20 世纪 90 年代以来咸海盆地的历史作物地图。结果表明，提取的场地边缘与现场测量的场地尺寸数据之间的交集为0.65。2019年农作物测绘的总体精度为95.2%。然后，我们将我们的方法扩展到1991-2015年期间的历史测绘，精度范围为82.8%至91.3%。此外，我们应用于历史映射的方法在准确性和策略匹配方面效果良好。这些发现表明我们的方法可以准确地区分农田边缘，以减少由于混合像素导致的分类错误。该方法有望解决咸海流域历史作物制图的农田边缘问题，并可为世界其他流域历史作物分类提供参考。