基于时序定量遥感的冬小麦长势监测与估产研究

遥感技术是高效、客观监测农作物生长状态的重要手段，对农业生产管理具有重要意义。以安徽龙亢农场为研究区，收集了中高分辨率多源卫星遥感数据并进行了定量化处理，构建了冬小麦叶绿素密度、叶面积指数的遥感反演模型，生产了长时序冬小麦植被参数卫星遥感产品。通过监测冬小麦叶绿素密度、叶面积指数的时序变化规律，分析了不同品种冬小麦的长势情况，发现高产量小麦在越冬期长势显著优于低产量小麦。在此基础上，构建了基于归一化植被指数（NDVI）的冬小麦估产模型，结果表明：利用小麦抽穗期和乳熟期的累计NDVI值可以实现产量的精确估算，据此绘制了龙亢农场2017年冬小麦产量遥感估算地图，产量分布与实际种植情况吻合良好。实现了基于时序卫星定量遥感数据的冬小麦长势监测和产量预测，为区域范围内农作物长势监测提供了一种有效途径。

Growth Monitoring and Yield Prediction of Winter Wheat based on Time-series Quantitative Remote Sensing Data

Remote sensing is an important approach for crop growth monitoring efficiently and subjectively, and is helpful for the agricultural productivity. In this paper, Longkang Farm in Anhui province, China, was selected as a case for the study. Remote sensing images with middle-high spatial resolution from different satellite-based sensors were collected and quantitively processed. Statistics models for the estimation of chlorophyll density and leaf area index were built based on vegetation indices. Time-series products of vegetation parameters were produced. We analyzed the temporal patterns of chlorophyll density and leaf area index and found that the high-yield wheat grew much better than the low-yield wheat during the winter. In addition, we built a yield prediction model based on the Normalized-Difference Vegetation Index (NDVI) for winter wheat. The results showed that, using accumulated NDVI at heading and milk stage, the yield can be accurately estimated. The winter wheat yield prediction map of Longkang farm was produced based on time-series satellite images. This study provided an efficient approach for crop growth monitoring.