为寻求高效、高精度的东北一季稻种植面积提取方法，该研究以辽宁省盘锦市为研究区，利用覆盖水稻关键物候期的6景GF-6 WFV单时相影像和时序影像，构建光谱特征、植被指数、水体指数和红边指数4类特征变量，采用平均不纯度减少的方法进行重要性排序并通过袋外误差方法选择最优输入特征，建立基于特征优选的随机森林模型，对2020年盘锦市水稻种植分布进行提取。结果表明：（1）基于水稻不同物候期的单时相影像，总体分类精度均在94%以上，以处于水稻移栽期影像分类结果最佳，其总体精度、F1值（水稻）、Kappa系数与实地验证点精度分别为97.67%、98.84%、0.97和97.22%；（2）与单时相影像相比，利用时序影像进行土地覆被分类和水稻信息提取能够有效提高分类精度，其总体精度、F1值（水稻）、Kappa系数与实地验证点精度分别为99.33%、100.00%、0.99和97.22%；（3）对有无红边信息参与的水稻提取结果进行对比分析，红边波段和红边指数的引入可使分类精度有所提高；（4）引入紫边与黄边波段能够提高分类精度，但分类结果精度提高效果次于红边信息。该研究证明，基于特征优选的随机森林模型，利用水稻移栽期的单时相影像提取水稻种植分布可满足实际应用精度需求，但利用时序影像可进一步提高分类精度。此外，GF-6卫星的新增波段均能够提高水稻分类精度，显示出GF-6卫星在作物精细提取方面具有巨大应用潜力。

Searching for an efficient and high-precision method for mapping paddy rice planting distribution in Northeast China has important implications for accurate paddy rice yield estimation and agricultural policies making. In this study, paddy rice planting distribution was mapped by feature optimization random forest method in Panjin City, Liaoning Province. Based on the land coverage types, this study acquired 2000 samples of 1000 paddy rice samples, 250 water samples, 300 wetland samples, 150 dry land samples and 300 construction land samples. Training samples and testing samples accounted for 70% and 30%, respectively. In addition, 36 paddy rice field validation points were obtained through field surveys. The spectrum features, vegetation indexes, water index and red edge indexes were constructed by using the GF-6 WFV images taken in the periods of May 11th, May 25th, June 1st, June 6th, July 20th and August 22nd in 2020. According to the phenological phase of paddy rice in Panjin City, these images corresponded to the trefoil stage, transplanting stage, returning green stage, booting stage and heading stage, respectively. The returning greening stage image was covered by June 1st and June 6th. The feature importances of single temporal images and time series images were calculated, and out-of-bag(OOB) estimations on different feature combination models were performed based on OOB data. The optimal input features were selected after comprehensively considering the accuracy and complexity. Then feature optimization random forest model was established to extract the paddy rice planting area and spatial distribution information in Panjin City in 2020. According to the testing samples and the paddy rice field validation points, the accuracy evaluation of classification results showed that: (1) Based on the single temporal images with different phenological phases, all of the classification accuracy were 94% above. The classification result of the image in the paddy rice transplanting stage was the best that the overall accuracy, F1_score(paddy rice), Kappa coefficient and field validation points accuracy were 97.67%, 98.84%, 0.97 and 97.22% , respectively; (2) On the basis of comparison with the classification results of single temporal images, using time-series images for land coverage classification and paddy rice information extraction effectively improved the classification accuracy and reduced misclassification and omission, and the paddy rice classification map polygons were more regular. The overall accuracy, F1_score(paddy rice), Kappa coefficient and field validation points accuracy with time series images were 99.33%, 100%, 0.99 and 97.22%; (3) Through analyzing of the paddy rice extraction results with or without red edge bands and red edge indexes, the classification accuracy was improved by the introduction of red edge information. This study proved that based on the feature optimization random forest model, the paddy rice information was accurately extracted by using the single temporal image of paddy rice transplanting stage. Compared with single temporal image, using time-series images improved the classification accuracy. Considering the complexity and running speed of the model, the single temporal image of paddy rice transplanting stage was used to extract paddy rice planting area to meet the accuracy requirement in practical applications. (4) Though analyzing the results of paddy rice extraction without purple band and the yellow band, this study proved that the introduction of purple band and yellow band can improve the classification accuracy, but the effect of improving the accuracy of the classification result is inferior to the red edge information. In addition, improving the classification accuracy of paddy rice and enhancing crop recognition capabilities by red edge information, purple band and yellow band, showed that the GF-6 satellite had broad application prospects in crop precise identification and area extraction.