基于光谱遥感的冬小麦籽粒淀粉积累量的监测

为了建立基于叶绿素密度淀粉积累量的最佳种植密度光谱监测模型,利用遥感技术准确测量小麦淀粉积累量,本试验通过大田试验,根据品种特性和晋中地区推广种植密度分别设置5个种植密度(300万苗.hm 2、450万苗.hm 2、600万苗.hm 2、750万苗.hm 2、900万苗.hm 2),对其冠层光谱、叶绿素密度和淀粉积累量进行了测定,并利用统计学方法对5个种植密度的数据进行分析,对混合密度进行模拟。结果表明,在5个密度梯度和模拟混合密度下所建立的淀粉积累量光谱监测模型中,以密度750万苗.hm 2条件下的NDVI(1 200 nm,670 nm)所建立的光谱模型最好,其精确度可达0.920 6。用2009—2010年的试验数据对模型进行检验,其预测值和实测值的R2也可达0.954 2,表明750万苗.hm 2的种植密度是对冬小麦淀粉积累量进行预测的最佳密度,依此所建立的冬小麦淀粉积累动态监测光谱模型是可行的。同时,所建立模拟混合密度模型的精确度可达0.883 1,验证R2也可达0.905 4,说明该模拟混合密度模型能够较准确地预测不同种植密度条件下的淀粉积累量。因此,模拟混合密度模型在现实应用中具有较好的适用性和普适度。

Monitoring grain starch accumulation in winter wheat via spectral remote sensing

Starch is a major photosynthate and quality index for winter wheat. Planting density influences the growth and development of winter wheat through factors, such as, thermal, light, temperature, etc. This in turn influences the generation, development and transportation of photosynthate to wheat grains which eventually determine wheat yield and quality. Chlorophyll density is strongly related with spectral parameters and accumulated starch. Thus, chlorophyll density was used to serve as a link between canopy spectra and starch accumulation in this study. The aim of the study was to explore suitable density for forecasting accumulated starch content for the purpose of building a model for the accurate forecasting of starch accumulation via spectral remote sensing. In this study, "Jing 9549" winter wheat cultivar was cultivated in 2009 and the "Jing 9549", "Le 639" and "Chang 4738" cultivars cultivated in 2010 at planting densities of 3.0×106 plant·hm^-2, 4.5×10⁶ plant·hm^-2, 6.0×10⁶ plant·hm^-2, 7.5×10⁶ plant·hm^-2, 9.0×10⁶ plant·hm^-2. In the field experiments, canopy spectral, chlorophyll density and starch accumulation of winter wheat were measured in the five different planting densities. The accuracy of the monitoring model with NDVI (1 200 nm, 670 nm) was highest (0.920 6) at 7.50×10⁶ plant·hm^-2 wheat planting density. The model was verified with data for the cultivation period of 2009 to 2010. The result showed a strong agreement with a correlation coefficient of 0.954 2. The 7.5×10⁶ plant·hm^-2 density was the most reasonable planting density for monitoring starch accumulation in winter wheat. Also the data for the five densities were integrated to construct a multi-density simulation model. The multi-density model accuracy was 0.883 1 and its relative error (RE) was also the lowest (0.905 4). Thus to some extent, the multi-density simulation model was widely applicable and practically significant. The spectral remote sensing monitoring model for observed optimum density and accumulated starch at different wheat planting densities gave the theoretical basis and guidance for large-scale monitoring of wheat quality from space.