农作物种植结构遥感提取研究进展

摘要

农作物种植结构信息对农业生产管理、农业可持续发展及国家粮食安全等具有重要意义。本文中概括了农作物种植结构遥感提取的理论基础，归类了近10年间不同农作物种植结构遥感提取技术方法，重点评述了不同技术方法的特点及应用情况，讨论和展望了未来农作物种植结构遥感提取研究的发展方向。当前，光谱特征、时相特征和空间特征是农作物种植结构遥感提取的三大理论基础。基于单一影像源的种植结构提取方法操作简单，但往往难以获取种植结构“最佳识别期”的遥感影像；基于多时序影像源的种植结构提取方法可以充分利用农作物季相节律特征，成为当前农作物种植结构遥感提取的主流方法。在基于多时序影像源的种植结构提取方法中，多特征参量法较单一特征参量法更适用于农作物种植结构复杂区域，基于多特征参量的统计模型法一定程度上解决了混合像元问题，但模型的鲁棒性有待提高。此外，遥感与统计数据融合的农作物种植结构提取法在国家及全球大尺度的农作物种植结构提取中具有优势，但较低的制图分辨率使得数据产品的区域适宜性较差。未来农作物种植结构遥感提取将以区域“作物一张图”为目标，充分发挥多源数据组合利用的优势，围绕多类型作物同步提取和大范围作物种植结构提取开展深入研究，重点加强遥感数据预处理、特征参量提取和分类器高效选择等关键技术研究，从而提升农作物种植结构遥感提取的时空尺度，满足多方位的农业应用需求。%Mapping crop patterns with remote sensing is of great implications for agricultural production, food security and agricultural sustainability. In this paper, the theoretical basis behind the mapping was summarized, mapping methods were classified into several categories, characteristics and applicabilities of different mapping methods in the latest decade were discussed intensively, and some important directions and priorities for future studies were proposed. Currently, spectral, temporal and spatial features are the major theoretical bases for crop pattern mapping. The mapping method based on single imagery is characterized by its simple implementation, but with difficulty of capturing imagery at the best time for distinguishing different crops. Instead, the mapping method based on time-series of imagery can make full use of temporal features and is thus widely used for crop mapping, among which the methods using multiple features are more suitable than the ones using a single feature for regions with complicated planting structure. To some extent, feature-oriented statistical modeling method can resolve the mixed-pixel problem but its robustness needs to be improved. Furthermore, large-scale crop pattern mapping can be done by combining the remote sensing and agriculture statistics. However, due to coarse resolution, the derived maps show poor region suitability. Future crop pattern mapping should target at developing“a map of crops”, the emphasis must be put on covering more crop types, enlarging the mapping areas, utilizing the superiority of blending multi-source data, strengthening the data preprocessing, optimizing the feature extraction and classifier selection, and improving the temporal and spatial scales of crop pattern mapping so as to better meet the needs of multi-faceted agricultural applications.