利用Landsat TM遥感数据监测冬小麦开花期主要长势参数

为精准农业技术体系中的小麦农艺处方管理决策提供详尽的全局性信息，该文以2007－2009年试验实测数据为基础，以Landsat TM影像为遥感数据源，分析了试验样点开花期冬小麦主要长势参数与品质和产量间以及与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开花期SPAD值、生物量、叶面积指数和叶片氮含量的模型。结果表明：冬小麦开花期，选用作物氮反射指数、近红外波段反射率和归一化植被指数这些遥感变量分别反演冬小麦SPAD值、生物量、叶面积指数和叶片氮含量是可行的；SPAD值、生物量、叶面积指数和叶片氮含量遥感监测模型的精度较高，均方根误差分别为3.12、216.5 kg/hm2、0.269和0.162，以此为基础，制作出具有实际农学意义的冬小麦开花期不同等级SPAD值、生物量、叶面积指数和叶片氮含量遥感监测专题图，实现了主要长势参数空间分布量化表达。基于卫星影像的农田面状信息获取技术克服了点状信息的不足，为农业生产管理决策及时提供信息支持，使该研究技术更利于大面积应用和推广。

Monitoring wheat main growth parameters at anthesis stage by Landsat TM

In order to acquire detail information of a regional winter wheat within growth season to instruct the production, the experiment was carried out in wheat growth season during 2007-2009 in Jiangsu province to monitor main growth parameters with Landsat TM data. The relationships of main growth parameters, grain quality and yield parameters at anthesis stage were analyzed, as well as the relationships of main growth parameters with satellite remote sensing variables. And then the quantitative relationship models were established and evaluated to monitor SPAD, biomass, leaf area index (LAI) and leaf nitrogen content(LNC) in winter wheat using remote sensing spectral variables derived from Landsat TM images. The results showed that at anthesis stage, it was feasible to monitor wheat SPAD, biomass, LAI and LNC using the satellite remote sensing variables of nitrogenous reflection index (NRI), B4 and normalized difference vegetation index (NDVI) respectively. Based on sensitive remote sensing variables, the models for monitoring SPAD, biomass, LAI and LNC at anthesis stage in winter wheat were established, which the root mean square error (RMSE) for SPAD, biomass, LAI and LNC were 3.12, 216.5 kg/hm2, 0.269 and 0.162, respectively. Based on the monitoring models, the thematic mapping of monitoring SPAD, biomass, LAI and LNC under different grades at anthesis stage can be got to realize the spatial quantization expression for monitoring main growth parameters. The technology to obtain large area information using satellite remote sensing data can overcome the shortcoming of point sampling technology, and provide timely informations for agricultural production management decisions.