顾及物候的玉米作物干旱遥感监测模型研究

项目名称： 顾及物候的玉米作物干旱遥感监测模型研究

项目编号： No.41501459

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 沈永林

作者单位： 中国地质大学（武汉）

项目金额： 20万元

中文摘要： 准确、动态地监测玉米干旱对区域农业生产管理具有重要的指导意义。现有干旱遥感监测模型主要反映地表的综合干旱程度，忽略了物候年际变化对模型精度的影响，难以准确地实现玉米作物的干旱监测。本研究以河南为例，开展顾及物候的玉米作物干旱遥感监测模型研究，具体包括：(1)综合考虑遥感影像分维特征、植被指数、有效积温等特征，研究玉米关键物候期的遥感特征提取及动态检测方法，解决遥感物候与农学信息连接的时空匹配及信息转化问题。(2)揭示物候影响玉米干旱监测的潜在机制，研究物候调节的干旱遥感监测模型的构建及干旱分级方法。(3)综合地面测站观测的土壤相对湿度、作物实际亩产量、干旱事件等，研究监测模型的时空适用性评价方法。本课题研究有望提高玉米物候检测和干旱遥感监测的时效性与准确性，为特定作物的遥感干旱监测提供理论依据。

中文关键词： 农业旱灾；高光谱遥感；作物生长模型

英文摘要： Accurately and dynamically monitor the drought of corn crop has great guiding significance on regional agricultural production and management. Drought indices derived from remote sensing have been regarded as an important indicator for agricultural drought monitoring. However, most of indices are designed for reflecting the comprehensive severity of drought, which cannot be directly applied to crop-specific (e.g., corn) drought monitoring. As a typical satellite-derived drought index, vegetation condition index (VCI) has been successfully used by normalizing to multi-year maximum and minimum values of normalized difference vegetation index (NDVI). When it is applied to drought monitoring of corn crop, its time benchmark will be affected by the inter-annual variation of phenology. Therefore, it is critical to establish a drought monitoring model taking into account corn phenology. The experimental study will be conducted in Henan Province of China. With the combination of the remote sensing vegetation index, fractal dimension, and accumulated growing degree days, the key corn phenological phase will be estimated dynamically, and the correlation between remote sensing phenology and agriculture information will be verified in space and time; Analyzing the potential mechanism of corn phenology on drought monitoring, establish the drought monitoring model and divide the corresponding drought degree; Considering the relative soil humidity, actual area yield of corn, drought events with the ground observation from stations, the applicability of drought monitoring model will be evaluated in space and time. This topic research is expected to improve the accuracy of corn phenology detection and drought monitoring.

英文关键词： agriculture drought;hyperspectral remote sensing;crop growth model