草坪草叶绿素高光谱估测模型的建立及Pb2+胁迫下的反演研究

本研究以3种常用草坪草种为材料，通过盆栽试验测定了生长旺盛期草坪草冠层成像光谱数据和叶绿素相对含量（Soil and plant analyzer develotrnent，SPAD），通过Person相关系数分析了1/SPAD与19个植被指数的相关性，筛选与叶绿素相关性较高的植被指数进行主成分分析，建立了1/SPAD估测模型，并利用所建模型反演了Pb²⁺胁迫下3种草坪草SPAD的变化。结果表明：植被指数NDVI₆₇₀、VARI、PSRI、ARVI、RGI和GI与1/SPAD极显著相关，可作为主成分分析原始变量；主成分分析得到的2个主成分，可清楚的区分"红象"高羊茅与"肯塔基"早熟禾；多元逐步回归模型y_1/SPAD=-0.117Z₁+0.062Z₂+0.041（R²=0.763，RMSE=0.01），总体估测精度为0.9248，说明利用主成分分析进行草坪草叶绿素的估测效果较佳；最优模型反演发现，除低浓度Pb²⁺（500 mg·L^-1）胁迫显著促进了"红象"高羊茅叶绿素的合成外，3种草坪草的SPAD均随Pb²⁺浓度增加极显著下降。

Hyperspectral-based Estimation on the Chlorophyll Content of Turfgrass

Using three common turfgrass species as materials,through pot experiments,the turfgrass canopy imaging spectrum data and relative chlorophyll SPAD (Soil and plant analyzer develotrnent) were measured during the vigorous growth period,and 1/SPAD and 19 were analyzed by the Person correlation coefficient. Correlation of vegetation index,vegetative index with high correlation with chlorophyll was selected for principal component analysis,1/SPAD estimation model was established,and the model was used to invert the changes of SPAD of three turfgrasses under Pb²⁺stress. The results showed that:the vegetation indices NDVI₆₇₀,VARI,PSRI,ARVI,RGI,and GI were extremely significantly related to 1/SPAD and can be used as the primary variables for the principal component analysis;The two principal components obtained by the principal component analysis can be clearly distinguished "Red Elephant" tall fescue and Kentucky Kentucky bluegrass;Multiple stepwise regression model y_1/SPAD=-0.117Z₁+0.062Z₂+0.041 (R²=0.763,RMSE=0.01),the overall estimation accuracy is 0.9256,indicating that the principal component analysis is a better method to estimate the chlorophyll of turfgrass;The optimal model inversion found that the addition of low concentrations of Pb^{2 +} (500 mg·L^-1) stress significantly promoted the chlorophyll synthesis of "Red Elephant" In addition,the SPAD of all three turfgrasses decreased significantly with the increase of Pb²⁺ concentration.