Monitoring grain starch accumulation in winter wheat via spectral remote sensing
基于光谱遥感的冬小麦籽粒淀粉积累量的监测

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.