时-空-谱融合自动纯化生成样本的农作物遥感分类

准确及时地获取农作物种植面积是粮食估产的重要基础，对稳定市场和粮食安全至关重要。随着卫星遥感技术的发展和农作物识别算法的成熟，遥感在农业领域得到了广泛深入的应用，但农作物自动化识别普遍受到缺乏足够代表性训练样本数据的制约。该研究提出了一种多阶段样本纯化策略，综合考虑时间、空间、光谱和物候信息，基于历史作物空间分布图和当季遥感影像自动生成高质量的训练样本，以支持农作物的自动化识别。利用谷歌地球引擎（google earth engine，GEE）云平台及Sentinel-2数据，采用随机森林算法对浙江省两地区的水稻进行自动化识别。研究结果表明，该方法能够利用历史专题数据和当季遥感影像生成最新且充足的训练样本，样本点精度高达98.5%。样本点数量和影像特征对分类结果影响的定量分析结果表明，作物识别的精度超过96%，Kappa系数超过0.93。此外，所提算法对含有误差的历史分类数据表现出较好的鲁棒性。研究结果可为区域级农作物识别提供一种可靠的样本自动化生成方法，在大尺度自动化作物制图中具有广泛的应用潜力。

Automatic purifying and generating graded samples for crop remote sensing classification using temporal-spatial-spectral fusion

Accurately and timely acquiring the planted area of crops can greatly contribute to grain yield estimation, market stability and food security. Remote sensing has been widely applied in agriculture, with the advancement of satellite remote sensing and mapping. However, the crop mapping is generally constrained by the insufficient number of representative training samples. In this study, a multi-stage sample purification was developed to integrate the temporal, spatial, spectral, and phenological information. The historical and spatial distribution of crops and current-season remote sensing images were utilized to automatically generate high-quality training samples. The crop was automatically identified. The multi-year crop distribution of the dataset was selected to extract the stable cultivation regions of target crops. Subsequently, morphological processing was conducted to minimize the plot edge effects. Area filtering was then used to avoid fragmentation for the large-scale planting areas of target crops. The random points were also generated for the sample points. Initially, spectral filtering was applied to the sample points to enhance their spectral purity. Then, the potential erroneous sample points were filtered using phenological features. Finally, the probability of random forest was used for the final selection, in order to obtain the ultimate data of sample points. The spatiotemporal reconstruction was conducted to filter the remote sensing data in a time series using the Google Earth Engine (GEE) cloud platform and Sentinel-2 data. The linear interpolation was employed with the Whittaker smoother. Subsequently, Random Forest was used to achieve the automated identification of early-season, one-season late, and double-cropping late rice in Wenzhou and Linhai City, Zhejiang Province, China. The feature bands included the reflectance data in three spectra (green, red, and near-infrared bands) and six vegetation indices, namely NDVI, LSWI, EVI, GCVI, NDTI, and NDSVI. The results indicate that sufficient training samples were generated using historical thematic data and current-season remote sensing images, thus achieving a sample accuracy as high as 98.5%. Experimental results indicate that the better performance of sample purification was achieved with the noticeably higher accuracy, compared with the rest. There was some impact of sample point quantity and image features on classification. Recognition accuracy was improved with an increase in the number of sample points. A plateau was reached when the number exceeded 3 000. Features in the irrigation period shared the most significant impact on rice identification. Random Forest model was utilized to obtain the accuracy of early-season rice over 96%, with a Kappa coefficient exceeding 0.93, and an F1 score exceeding 0.95. The planting areas of early, single-season late, and double-season late rice in Wuling City and Linhai City were taken as the ground truth values, in order to compare with the identified rice area. The high correlation was achieved with a coefficient of determination (R²) of 0.96. There was a high level of consistency between the identified area and the ground truth data. The high spatial resolution imagery showed that the rice identification exhibited high spatial detail and accuracy, with the accurate delineation of field boundaries. The improved model demonstrated high recognition accuracy, particularly in the early identification of rice. Additionally, the generation of an error map was simulated to evaluate the robustness of historical classification data with errors. This finding can be expected to identify other crops, such as corn, soybeans, and wheat. The efficiency of remote sensing crop areas was improved to reduce the impact of human factors. This finding can provide regional-level crop identification using sample generation, indicating the broad potential applications in large-scale automated crop mapping.