NDVI时序相似性对冬小麦种植面积总量控制的制图精度影响

遥感技术获取的区域作物面积与作物面积统计数据间常常存在不一致的问题,这在一定程度上影响了作物分布遥感制图信息的应用。为获得与作物面积统计数据一致的高精度作物分布遥感制图信息,该研究以河北省衡水市武邑县为研究区,以时序Sentinel-2遥感影像生成的归一化差值植被指数(Normalized Difference Vegetation Index, NDVI)为研究数据,将冬小麦面积目视解译数据作为遥感提取的区域冬小麦面积总量参考,提出基于复合型混合演化算法(Shuffled Complex Evolution-University of Arizona, SCE-UA)和区域作物种植面积总量控制的NDVI时序相似性阈值优化冬小麦分布制图方法,并进行精度验证。在此基础上,进一步开展不同生育阶段NDVI时序相似性及其相似性组合的冬小麦分布提取精度对比研究。结果表明,利用全生育期NDVI时序相似性获得的冬小麦分布制图结果总量精度达99.99%以上,总体精度达98.08%,Kappa系数为0.96,可以保证遥感提取的区域冬小麦面积与冬小麦种植面积总量控制参考间的高度一致性且能获得较高的作物遥感识别精度。从不同生育阶段NDVI时序相似性及其相似性组合的冬小麦分布提取结果可知,利用出苗期-分蘖期、返青期-拔节期的NDVI时序可获得高精度冬小麦分布提取结果,而利用抽穗期-成熟期的NDVI时序数据提取冬小麦结果则精度较低,且综合不同生育阶段NDVI时序数据有利于冬小麦制图精度的提高。该研究可为高精度冬小麦分布提取和制图技术及其方案优化提供一定参考依据,也可为遥感数据和作物面积统计数据融合的大范围农作物分布遥感制图及统计数据空间化提供一定技术方法参考和思路借鉴。

Effects of NDVI time series similarity on the mapping accuracy controlled by the total planting area of winter wheat

Generally, there is a problem of inconsistency between the total area of regional crops obtained from remote sensing technology and the statistical data of crop area, which affects the application of remote sensing-based crop spatial distribution information to a certain extent. To obtain high accuracy crop spatial distribution information consistent with the statistical data of crop area, a method for extracting and mapping winter wheat spatial distribution was proposed in this study based on threshold optimization of NDVI time series similarity under regional total planting area control, and the accuracy was verified. This study took Wuyi County, Hengshui City, Hebei Province as the study area, based on the Sentinel-2 NDVI data covering the whole growth period of winter wheat, the reference and actual cross-correlation curves were obtained by the Cross Correlogram Spectral Matching (CCSM) algorithm. On this basis, the root mean square error between the two curves was calculated, and a winter wheat extraction model was constructed. Then, using the Shuffled Complex Evolution-University of Arizona (SCE-UA) global optimization algorithm, the visual interpretation data of the regional winter wheat planting area was regarded as the reference for the winter wheat planting area extracted by remote sensing, and the optimal threshold in the winter wheat extraction model was obtained. Finally, according to the optimal threshold, the winter wheat was extracted by using the winter wheat extraction model. On this basis, a comparative analysis on the accuracy of winter wheat mapping results was carried out, which were extracted from the similarity of NDVI time series in the whole growth period and the similarity and similarity combinations of NDVI time series at different growth stages, respectively. The results showed that the regional crop mapping results using the similarity of NDVI time series throughout the whole growth period were excellent, and the total area accuracy was more than 99.99%, the overall accuracy and Kappa coefficient were 98.08% and 0.96, respectively. It was proved that the method could ensure the result consistency between the total area of regional crops obtained by remote sensing and the total amount of control reference data, and a higher recognition accuracy could be obtained. Seen from the crop distribution extraction results based on the similarity and similarity combinations of NDVI time series at different growth stages, the conclusions could be drawn that the NDVI time series from the seedling stage to the tillering stage before winter and from the reviving stage to the jointing stage could be used to obtain high accuracy crop distribution extraction results, while the NDVI time series from the heading stage to the maturity stage were used to extract winter wheat, the accuracy was low. Moreover, the comprehensive application of the similarity of NDVI time series at different growth stages was beneficial to the improvement of crop extraction and mapping accuracy to a certain extent. This study could provide a certain reference for the study on regional high-precision winter wheat mapping, as well as could provide a thought thread for obtaining large-scale, long-term, remote sensing-based regional crop spatial distribution information which was highly consistent with the statistical data of crop area.