基于高光谱的微生物作用下玉米叶片叶绿素含量估算研究

Estimation of chlorophyll content in maize leaves based on hyperspectral under the action of microorganism

利用盆栽试验,对不同微生物作用下的玉米植株叶片叶绿素含量和高光谱反射率进行测定,探讨不同微生物作用对植被叶片叶绿素含量的影响及其光谱特征变化,并尝试利用高光谱遥感技术对不同处理条件下玉米叶片叶绿素含量进行监测识别。四种不同处理,按照CK组、CR组、G.m组、G.m+CR组的顺序所选特征变量依次为Sa/Sc、Sb_((658-670))、Sd_((691-709))、SDb,四种处理均以指数模型预测效果最佳,CR组模型Y=54.4(exp(-7.5t))预测精度最高,CK组模型Y=36.0(exp(1.1t))预测精度最低,决定系数分别为0.938和0.600。所建模型预测精度理想,证实了高光谱遥感技术植被监测的可行性,为微生物复垦领域对不同微生物作用下植被叶片叶绿素含量的高效无损监测与评价提供了新的方法与途径。

A pot experiment was conducted to determine the chlorophyll content and hyperspectral reflectance of the leaves of maize plants under the action of different microorganisms,and then investigate the effect with different microorganisms of vegetation leaves on the chlorophyll content and the changes of its spectral characteristics,meanwhile,the high spectral remote sensing technology was used to monitor and identify the chlorophyll content of maize leaves under different treatment conditions.Four different treatments,with the sequence of CK group,CR group,G.m group and G.m+CR group,the selected feature variables are Sa/Sc,Sb_((658-670)),Sd_((691-709))and SDb respectively,the index model has the best prediction effect within the four different treatments,the prediction accuracy of CR group model Y=54.4(exp(-7.5 t))is the highest,the CK group model Y=36.0(exp(1.1 t))has the lowest prediction precision,the determinant coefficients are 0.938 and 0.600 respectively.The prediction accuracy of the model is ideal,which confirms the feasibility of vegetation monitoring by hyperspectral remote sensing technology.In the field of microbial reclamation,a new method and approach for monitoring andevaluating the chlorophyll content of vegetation leaves under the action of different microorganisms is provided.