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Growth monitoring of winter wheat in Beijing area using HJ-1A hyperspectral imagery

机译:利用HJ-1A高光谱成像监测北京地区冬小麦生长。

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Dynamic monitoring crop growth can keep track of the crop growth status, seedlings, soil moisture, nutriture and their changes and then the appropriate management strategies will be taken to ensure timely the normal growth and development. This study represented the potential of satellite hyperspectral imagery to monitor winter wheat biophysical and biochemical characteristics through narrow-band indices in Beijing. Three hyperspectral images (HSI, 100m, 115Bands, 460.04–951.54nm) of Environment and Disaster Monitoring and Forecasting of Small Satellite Constellation A (HJ-1A) were acquired in 5th April, 2009. Canopy spectral reflectance, leaf area index (LAI) and chlorophyll content (CHLa, CHLb) of winter wheat were measured synchronously with HJ1-1A visited. Firstly, the Gaussian function is taken to simulate the response function of HSI, and then the ground hyperspectral data is matched to the HSI channels to get the simulated HSI. Secondly, the correlation relationships between the simulated HSI and its mathematical transforms (derivative HSI, normalized spectral index (NDSI), subtraction index (SSI)) and the observed LAI and CHL were analyzed, respectively, to select the sensitive bands of the LAI and CHL. Finally, the indexes NDSI(748.2,765.11) and SSI(759.39,776.82), which were built with these above sensitive bands, were chosen to estimate LAI and CHL, respectively. On basis of the above analysis, these constructed indexes were applied in observed HSI. The HSI data were processed by radiometric calibration, vertical stripes elimination, atmospheric correction, and geometric correction. And then, mapping the LAI and CHL to quantitatively monitor the crop growth.
机译:动态监测农作物生长情况可以跟踪农作物生长状况,幼苗,土壤水分,营养状况及其变化,然后采取适当的管理策略以确保及时地正常生长和发育。这项研究代表了卫星高光谱图像通过北京的窄带指数监测冬小麦生物物理和生化特征的潜力。 2009年4月5日获得了三颗小卫星星座A(HJ-1A)的环境和灾难监测与预报的高光谱图像(HSI,100m,115Bands,460.04–951.54nm)。冠层光谱反射率,叶面积指数(LAI)与HJ1-1A同步测定了冬小麦的叶绿素含量(CHLa,CHLb)。首先,采用高斯函数对HSI的响应函数进行仿真,然后将地面高光谱数据与HSI通道进行匹配,得到仿真的HSI。其次,分别分析了模拟HSI及其数学转换(导数HSI,归一化光谱指数(NDSI),减法指数(SSI))与观测到的LAI和CHL之间的相关关系,以选择LAI和CHL。最后,分别选择由上述敏感带构建的指标NDSI(748.2,765.11)和SSI(759.39,776.82)分别估计LAI和CHL。在以上分析的基础上,将这些构建的指标应用于观测的恒指。 HSI数据通过辐射校准,垂直条纹消除,大气校正和几何校正进行处理。然后,绘制LAI和CHL以定量监视作物生长。

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