摘要
为探究双向反射分布(BidirectionalReflectanceDistributionFunction,BRDF)模型改进的光化学植被指数(Photochemical Reflectance Index,PRI)反演水稻冠层光能利用率(Light Use Efficiency,LUE)能力,该研究利用多角度水稻冠层辐射数据和同期通量观测数据,引入BRDF模型对多角度PRI进行观测角度标准化处理。获取晴天09:00—15:00每半小时数据156组,其中130组数据用来建模,另外26组数据对所建模型进行验证。结果表明:BRDF模型在晴空指数(ClearnessIndex,CI)较低时拟合效果较差,随着CI的升高模型模拟效果变好;BRDF模型的拟合参数受光照条件和植被状况的影响,不同CI范围下的各向同性权值ki与LUE相关性均良好(决定系数大于0.3),在0.6≤CI<0.7时相关性最佳,决定系数为0.63;无论是否采用BRDF模型的角度校正,由PRI反演LUE的模型均可采用线性形式或指数形式;但采用BRDF模型的角度校正后,反演模型精度得到显著提升,决定系数从0.46(P<0.01,校正前)提高到0.8(P<0.01,校正后);验证结果显示,采用BRDF模型的角度校正前后,相对反演偏差指数由1.34提升到2.6,同时验证的拟合决定系数也由0.44提高到0.87。该研究相比较传统多角度遥感观测的PRI指数,BRDF模型的角度修正提高了PRI对水稻LUE的反演能力,证明了多角度冠层光谱观测可利用BRDF模型提高其植被指数对植物生理活动探测能力的可行性。
The Photochemical Reflectance Index(PRI)has great potential for estimating Light Use Efficiency(LUE).Mixed canopy structure under different lighting and observation geometries can affect the remote sensing observations of the PRI,leading to some errors in estimating vegetation photosynthesis.Multi-angle observation is an effective method for resolving differences in observation geometry.Fitting multi-angle observations to a Bidirectional Reflectance Distribution Function(BRDF)can normalize PRI to a common solar-observation target position,resulting in a significant increase in the correlation between canopy PRI and LUE,and has been well used in forest ecosystems.However,studies on the standardization of multi-angle PRI by BRDF models on crop canopies are still not well developed.To investigate the ability of the improved PRI by BRDF model to estimate the LUE of rice canopies,this paper used multi-angle rice canopy reflection data and contemporaneous flux data from the National Meteorological Station in Shouxian,Anhui,a semi-empirical,kernel-driven BRDF model was used to standardize the multi-angle PRI for each half-hourly from 09:00 to 15:00 during the observation period.This paper explored the BRDF model simulated PRI values and the model parameter characteristics under different physiological and non-physiological conditions.The results showed that the BRDF model fitting was poorly at low Clearness Index(CI)and became better as the CI increased,with the best performance at CI=0.9(R^(2)=0.41).The parameters of the BRDF model are influenced by light conditions and vegetation status,where the isotropic weights(ki)correlated well with LUE for different CI ranges(linear regression coefficients R^(2)>0.3 for all CI ranges),with the best correlation at 0.6≤CI<0.7(R^(2)=0.63);the daily variation of volume scattering weights(kv)showed a"U-shape"during the observation period,which was consistent with the daily variation of LUE.After correction by the BRDF model,the R2 of the exponential regression between PRI and LUE increased from 0.46(P<0.05)to 0.8(P<0.01).Compared with the PRI from simple multi-angle remote sensing observations,the angular correction by the BRDF model improved the ability of PRI estimating LUE;this study also demonstrated the feasibility that multi-angle canopy spectral observations can use the BRDF model to improve vegetation indices tracking plant physiological activity.This study has reference value for processing multi-angle vegetation remote sensing observation data and for remote sensing estimation of physiological activities related to photosynthesis in regional vegetation.The BRDF model can be practiced in multi-angle satellite observations to achieve the monitoring of photosynthetic capacity in a large area of farming area.Subsequent experiments in different farming areas will be carried out to calibrate the wider band spectral observations available from satellite data to achieve a comprehensive understanding of the correlation between LUE variation and spectral reflectance in lutein and fluorescence-related absorption characteristics,and to provide some reference for the calibration of parameters in agricultural areas in the global productivity model.
作者
张展豪
郭建茂
郭彩云
金淑媛
Zhang Zhanhao;Guo Jianmao;Guo Caiyun;Jin Shuyuan(College of Applied Meteorology,Nanjing University of Information Science&Technology,Nanjing 210044,China;Jiangsu Key Laboratory of Agricultural Meteorology,Nanjing 210044,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第14期211-218,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
江苏省自然科学基金(BK20191139)
国家气候中心数值模式发展专项(QHMS2020008)。
关键词
反演
水稻
光能利用率
多角度光化学植被指数
核驱动BRDF模型
晴空状况
inversion
rice
light use efficiency
multi-angle photochemical reflectance index
kernel-driven BRDF model
sky conditions
