多源遥感数据耦合CBA-Wheat模型的冬小麦生物量估算研究  

Estimating winter wheat biomass by coupling the CBA-Wheat model and multispectral remote sensing

作　　者：王士俊 刘苗 赵钰 柳昭宇 刘修宇 冯海宽[2] 隋学艳[4] 李振海 WANG Shijun;LIU Miao;ZHAO Yu;LIU Zhaoyu;LIU Xiuyu;FENG Haikuan;SUI Xueyan;LI Zhenhai(College of Geodesy and Geomatics Information Technology,Shandong University of Science and Technology,Qingdao 266590,China;Key Laboratory of Quantitative Remote Sensing in Agriculture of Agriculture and Rural Affairs,Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;China Institute of Intelligent Information Processing and Systems,Central South University,Changsha 410083,China;Institute of Agricultural Information and Economics,Shandong Academy of Agricultural Sciences,Jinan 250100,China)

机构地区：[1]山东科技大学测绘与空间信息学院,青岛266590 [2]北京市农林科学院信息技术研究中心农业农村部农业遥感机理与定量遥感重点实验室,北京100097 [3]中南大学智能信息处理及系统研究所,长沙410083 [4]山东省农业科学院农业信息与经济研究所,济南250100

出　　处：《遥感学报》2024年第12期3123-3135,共13页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42271396);山东省重点研发计划(编号:2022LZGC021);国家重点研发计划(编号:2021YFB3901303)。

摘　　要：生物量是反映作物生长状况的重要指标,及时准确估计冬小麦地上生物量对于产量预测和田间管理决策具有重要意义。综合考虑遥感植被指数VI(Vegetation Index)与数字化生育期ZS(Zadoks Stage)创建的作物生物量模型CBA-Wheat(Crop Biomass Algorithm for Wheat),虽然适用于全生育时期的冬小麦生物量估算,但是由于模型参数基于地面高光谱数据构建,而卫星遥感数据在应用过程中,需要使用更多的地面实测数据进行模型参数的调试,因而限制了该模型的推广使用。因此,本研究采用遗传优化算法GA(Genetic Algorithm)对CBA-Wheat模型进行全局优化确定模型最优参数,利用高分辨率遥感影像提取VI与试验记录的ZS数据,分别构建以不同VI为输入变量的冬小麦生物量反演模型,并进行验证。结果表明:增强型植被指数EVI2(Enhanced Vegetation Index2)为输入变量建立的模型精度最高,冬小麦生物量估算验证的决定系数(R^(2))和均方根误差(RMSE)分别达到0.92 t/hm 2和1.37 t/hm 2;基于CBA-Wheat模型的生物量估算精度效果优于基于偏最小二乘回归方法的生物量估算精度(R^(2)=0.85,RMSE=1.87 t/hm 2)。综上,本研究基于遗传算法优化的CBA-Wheat模型不仅具有较高的反演精度,而且适用于冬小麦多个生育期反演,在使用遥感卫星数据进行大面积生物量预测方面具有较好的应用潜力。Biomass is an important indicator that reflects the growth status of crops.Timely and accurate estimation of aboveground biomass of winter wheat is crucial for yield prediction and field management decision-making.The crop biomass model(CBA-Wheat)developed using the remote sensing spectral index(VI)and digital Growth Stage(ZS)is suitable for the estimation of winter wheat biomass in the whole growth period.The first layer of this model is a linear model of AGB and VI,and it constructs linear regression models between AGB and VI in different growth periods.The AGB model coefficients of each period have a good evolutionary law with ZS.However,the model parameters are based on ground hyperspectral data in a previous study,and the satellite data need extensive ground-measured data to calibrate the model parameters,thus limiting popularization at the regional scale.In this study,the Genetic Algorithm(GA)is used to optimize the parameters of CBA-Wheat model globally.GA combines the survival rules of the fittest in biological evolution with the random information exchange system of the chromosome within the population and has an efficient global optimization effect on some nonlinear,multimodel,multiobjective function optimization problems.The two input variables of CBA-Wheat are field-recorded ZS data and VI from high-resolution remote sensing images.Four VI-based CBA-Wheat models,namely,enhanced vegetation index 2(CBA-WheatEVI2),difference vegetation index(CBA-Wheat DVI),ratio vegetation index(CBA-WheatRVI),and modified simple ratio vegetation index(CBA-WheatMSR),are constructed.The best model is used for AGB mapping.Meanwhile,partial least squares regression(PLSR)is adopted to compare the accuarcy of CBA-Wheat models.Results showed that all four CBA-Wheat models have good accuracy,and the simulated winter wheat biomass is consistent with the measured biomass.Among the models,CBA-Wheat EVI2 has the highest determination coefficient(R^(2))and root mean square error(RMSE)of 0.92 and 1.37 t/ha,respectively.Compared with t

关 键 词：冬小麦 地上生物量 遗传算法 CBA-Wheat 多源数据 EVI2 Sentinel-2 遥感 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] P2[自动化与计算机技术—控制科学与工程]