基于异源多时相遥感数据提取灌区作物种植结构

用遥感技术提取灌区作物种植结构需要源影像具有适宜的时空分辨率以适应其动态变化特征。该文综合运用多种遥感影像数据，将Landsat ETM+与MODIS NDVI数据融合区分灌区土地利用类型，由融合后的时间系列数据非监督分类结果提取植被指数变化信息，结合作物系数变化规律运用光谱耦合技术提取作物种植结构。根据该方法将漳河灌区作物种植结构区分为水稻—油菜、水稻—小麦、单季夏季作物以及双季经济作物。由地面统计数据和高分辨率IKONOS影像进行了检验，分类精度达到91%并且与统计数据相吻合。结果表明该法不仅能提供更为准确的灌区作物种植结构时空信息，而且节省影像购买成本，方便灌区尺度遥感应用。

Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data

Crop planting structure extraction in irrigated areas includes a range of dynamic parameters which require proper spatial and temporal resolution remotely sensed data. The paper seeks to extract crop planting structure by employing multi-temporal images from multi-sensors. Landsat enhanced thematic mapper plus (ETM+) images and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) monthly data were res-merged to produce a mega data tube, which was then classified using ISO cluster algorithm. Spectral signature of each class was extracted and identified using spectral matching technique taking crop coefficient curve as reference. In the way Zhanghe Irrigation system in southern China was classified into four classes: rice-rapeseed rotation, rice-wheat rotation, single summer crops, and double economic crops. Accuracy assessment suggests good agreement with statistical data and 91% classification accuracy when using IKONOS high resolution images as Ground Truth data. The application demonstrates the method a cost-efficient and robust approach to extract crop planting structure at irrigation system scale.