【摘要】 采用5个新的植被指数(GDVI、GRVDI、GSAVI、GOSAVI、GMSAVI),并与以往遥感估产所用的9个植被指数一起遴选,选出最佳植被指数构建最优模型,用以实现苜蓿和无芒雀麦混播草地快速无破坏性精准测产。结果表明:以近红外光(760nm)和绿光(600nm)光谱变量组合计算的绿色优化土壤调节植被指数(GOSAVI)为自变量构建的二次方程式是混播草地群落最优估产模型,以近红外光(760nm)和绿光(600nm)光谱变量组合计算的绿色土壤调节植被指数(GSAVI)为自变量构建的三次方程式是苜蓿种群最优估产模型,而无芒雀麦种群最优估产模型却是以波长971nm处一阶微分为自变量构建的三次方程式。更多还原

【Abstract】 The fresh forage yield of mixed sowing pasture of alfalfa(Medicago sativa L.) and Smooth-Brome(Bromus inermis Leyss.)was estimated with hyperspectral remote sensing technology.The best vegetation indexes were selected from the new five vegetation index(GDVI,GRVDI,GSAVI,GOSAVI,GMSAVI) along with the nine vegetation index used in past remote sensing estimation of yield,the optimal fresh forage yield estimation model was built.The results showed that a quadratic model based on Green Optimization Soil Adjust Vegetation Index(GOSAVI,NIR=760nm,Green=600nm)was the best for estimation of the total fresh forage yield of mixed sowing pasture.And the estimation model of the fresh forage yield of alfalfa prefers to a cubic equation based on Green Soil Adjust Vegetation Index(GSAVI,NIR=760nm,Green=600nm);the estimation mode of the fresh forage yield of Smooth-Brome favors a cubic equation based on the first derivative value at wavelength 971nm.更多还原