基于时序遥感数据的农作物种植制度研究进展与展望

及时准确掌握农作物种植制度时空分布信息,对于确保国家粮食安全与农业结构合理具有重要意义。随着时序遥感影像质量的不断提高,基于时序遥感数据的农作物种植制度研究备受关注。本文从研究框架、遥感特征参数以及数据产品等角度,分析了基于时序遥感数据的农作物种植制度最新研究进展。研究发现:① 前农作物种植制度研究框架,主要包括耕地复种指数和农作物制图等相关内容,其问题在于需要高质量耕地分布数据支撑以及易将热带亚热带湿润区撂荒地误判为农作物等;② 于红边和短波红外的新型多维度光谱指数,有助于更好地揭示农作物生长发育过程,大尺度农作物时序遥感制图取得了系列研究成果,但需要应对不同作物光谱差异细微、同种作物在不同区域和年份存在明显类内异质性的挑战;③ 尺度中高分辨率耕地复种指数产品不断丰富,但其时效性和时空连续性有待加强;④ 欧美少数国家外,目前农作物分布数据产品覆盖的作物类型有限,我国大尺度农作物种植制度数据产品欠缺,特别是复杂多熟制农业区。随着多源遥感数据时空谱分辨率的不断提高以及云计算平台性能的不断发展,我们对以下方面进行了研究展望:① 新研究框架,建立直接提取耕作区、农作物种植模式的农作物种植制度一体化遥感监测技术框架;② 一步加强新型多维度遥感指数及其物候特征指标设计,拓展农作物种植制度监测的遥感特征参数;③ 立作物种植制度变化遥感监测技术,实现多年信息连续自动提取。

Progress and Prospect on Mapping Cropping Systems Using Time Series Images

Updated spatiotemporal explicit data on cropping system is vital for ensuring the implementation of the national food security strategy and reasonable cropping structures. Time series analysis techniques are playing a more important role in agricultural remote sensing along with the continuously improved quality of remote sensing time series images. This paper analyzes main progresses and challenges in the field of cropping systems mapping using time series images from three aspects: mapping framework, remote sensing feature parameters, and data products. We find that: (1) The current cropping system mapping framework which mainly includes cropping intensity and agricultural planting structures, needs to cope with the problems of pre-requirements of cropland distribution data with high-quality. However, the existing land use/cover data could not fully fulfil this requirement due to the complex spectral characteristics of cropland introduced by multiple cropping systems over large regions. It is difficult to accurately derive information on cropping intensity using traditional time series vegetation indices datasets. Specifically, cropland fallow/abandonment in humid regions might be misclassified as single crop due to its corresponding high values of vegetation indices. Cropland abandonment and fallow are not negligible in recent decades and need further investigations, especially in China; (2) Novel multi-dimensional spectral indices based on red-edge and short-wave near-infrared bands are efficient in revealing the crop growth processes. Great progresses have been made in crop mapping in recent years. However, crop mapping at large scale is challenged by the minor differences among different crops as well as distinct heterogeneity within the same crop across different regions and multiple years; (3) There are increasing available remote sensing products of cropping intensity from national to global scale, however, the timeliness and spatiotemporal continuity need to be further improved; (4) Except for a few countries in North America and Europe, crop distribution maps at national scale are not fully available or limited to several staple crops with coarse resolution. There is a deficiency of finer datasets on cropping systems at large scale, especially in the complex multi-cropped regions. Fortunately, new technologies (i.e., cloud computing platform and deep learning algorithms) and emerging multi-sources remote sensing data with higher spatial, spectral, and temporal resolution provide great opportunities for spatiotemporally continuously detecting changes in cropping system at large scale. Future research should be focused on the following directions. First, we could improve the research strategy by developing an integrated mapping framework for directly deriving information on cropland and cropping patterns without relying on existing cropland distribution data. Second, we need to enrich the phenological features through exploring multiple-dimensional and less exploited spectral indices, such as the pigment indices, soil indices, nitrogen indices, and dry matter indices. Finally, we can develop spatiotemporal continuous change detection techniques for automatically tracking changes in cropping systems at multiple years and large scale.