基于适宜尺度选择的冬小麦赤霉病遥感监测

为快速、准确获取江淮麦区县域冬小麦赤霉病发生信息,选用中、高空间分辨率卫星遥感影像做多尺度信息融合研究。在筛选适宜冬小麦田块分布特征的空间尺度遥感影像基础上,通过分析冬小麦长势指标和赤霉病病情指数之间的互作关系,构建基于多农学参数的冬小麦赤霉病估测模型,并对县域冬小麦赤霉病空间变化进行遥感监测。结果表明:(1)2m×2m、8m×8m和16m×16m三种空间尺度融合影像的均值相差不大,平均梯度和标准差存在明显差异。16m×16m融合影像的清晰度最好,信息量也多,比较适合研究区域冬小麦田块分布特征。(2)16m×16m融合影像提取的归一化植被指数(NDVI)和比值植被指数(RVI)值明显高于2m×2m和8m×8m融合影像,说明16m×16m融合影像光谱信息量较丰富,有利于冬小麦的识别。(3)冬小麦叶面积指数、叶片叶绿素含量和地上部生物量是影响赤霉病发生的主要长势指标。基于主要长势指标构建冬小麦赤霉病估测模型,平方根误差(REMS)为10.5%,相对误差为14.6%。该方法可以实现对县域冬小麦赤霉病空间变化的有效监测。

Remote Sensing Monitoring of Winter Wheat Scab Based on Suitable Scale Selection

In order to accurately and quickly acquire winter wheat scab information in Yangtze-Huai river region in China, this paper studied the multi-scale information fusion using medium and high spatial resolution satellite remote sensing images. On the basis of screening suitable spatial scale remote sensing images for the distribution characteristics of winter wheat field, we analyzed the interaction between winter wheat growth condition index and scab disease index, and built a winter wheat scab estimation model based on multi-agronomic parameters and monitor the spatial change of winter wheat scab in county area. The results showed that three spatial scale fusion images of 2 m× 2 m, 8 m×8 m and 16 m× 16 m have little difference, and there are obvious differences in average gradient and standard deviation. The definition of 16 m× 16m fusion image is the best, and the spectral information is abundant, which is beneficial to the identification of winter wheat in the test area. Based on the main influence index of winter wheat scab such as leaf area index, leaf chlorophyll content and aboveground biomass weight, the estimation model of winter wheat scab was constructed with the REMS of 10.5% and the relative error of 14.6%, respectively. The method proposed in this study can effectively monitor the spatial change of winter wheat scab in county area.