基于Landsat TM影像的冬小麦拔节期主要长势参数遥感监测

摘要

[目的]强化冬小麦长势遥感监测机制,为田间生产管理提供信息支撑.[方法]以2007-2009年试验实测数据为基础,以Landsat TM影像为数据源,分析试验样点拨节期冬小麦主要长势参数与品质、产量以及卫星遥感变量间的相关性,分别建立及评价了TM影像遥感变量监测冬小麦拔节期叶面积指数(LAI)、生物量、SPAD值和叶片氮含量(LNC)的模型.[结果]冬小麦拔节期,选用中红外波段的反射率(B5)、归一化植被指数(NDVI)、DSW5和绿波段的反射率(B2)等遥感变量分别反演冬小麦的SPAD值、生物量、LAI和LNC是可行的;SPAD值,生物量、LAI和LNC遥感监测模型的精度较高,以此为基础,制作出了具有实际农学意义的冬小麦拔节期不同等级SPAD值、生物量,LAI和LNC遥感监测专题图,实现了主要长势参数空间分布量化表达.[结论]研究结果可为广大农学家、农业部门决策者和田问管理人员提供及时的农情信息.%[Objective]As the growth status of winter wheat has an important effects on grain quality, the mechanism of monitoring winter wheat growth status using remote sensing technology should be further strengthened, thus the growing information could be provided for field production management in wheat growth season.[Method]The experiment was carried out in Jiangsu province in wheat growth season during 2007-2009 to monitor the main growth status parameters at jointing stage with landsat TM images.In the experiment, 431 points have been acquired to determin the main growth status paramenters.The satellite remote sensing data were used to monitor winter wheat growth status.The relationships between the main growth status parameters, grain quality and yield parameters at jointing stage, and between main growth status parameters and satellite remote sensing variables were analyzed.Through comparing the correlation between different remote sensing variables and main growth status parameters, some variables were chosed.The quantitative relationship models were established to monitor SPAD, biomass, LAI and LNC in winter wheat using remote sensing spectral variables derived from landsat TM images,and then the established models were evaluated by coefficient of determination (R2) and root mean square error (RMSE).[Result]At wheat jointing stage, it was feasible to monitor wheat SPAD, biomass, LAI and LNC by using the satellite remote sensing variables ofB5, NDVI, DSW5 and B2, respectively.After the precision of the monitoring models tested by using independent datasets in 2007 and 2008, the models could be utilized to accurately monitor the main growth status parameters in winter wheat.Based on the monitoring models, the thematic maps were producted to monitor SPAD, biomass, LAI and LNC under different grades at jointing stage with remote sensing, so as to realize the spatial quantization expression of monitored main growth status parameters.[Conclusion]The results have provided timely agricultural information for the agronomists, agricultural sector decision makers and field managers, and is convenient to make field management measures in time.