基于数字表面模型的冬小麦生物量估算  

Above-ground biomass estimation of winter wheat based on digital surface model

作　　者：郭燕[1,2] 贺佳 位盼盼[1,2] 曾凯 史舟 叶粟[3] 杨秀忠[1,2] 郑国清 王来刚 GUO Yan;HE Jia;WEI Pan-pan;ZENG Kai;SHI Zhou;YE Su;YANG Xiu-zhong;ZHENG Guo-qing;WANG Lai-gang(Institute of Agricultural Information Technology,Henan Academy of Agricultural Sciences/Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology,Ministry of Agriculture and Rural Affairs,Zhengzhou,Henan 450002,China;Henan Engineering Research Center of Crop Planting Monitoring and Warning,Zhengzhou,Henan 450002,China;College of Environment and Resources,Zhejiang University,Hangzhou,Zhejiang 310058,China)

机构地区：[1]河南省农业科学院农业信息技术研究所/农业农村部黄淮海智慧农业技术重点实验室,河南郑州450002 [2]河南省农作物种植监测与预警工程研究中心,河南郑州450002 [3]浙江大学资源与环境学院,浙江杭州310058

出　　处：《南方农业学报》2025年第1期53-63,共11页Journal of Southern Agriculture

基　　金：国家重点研发计划项目(2022YFD2001105);河南省中央引导地方科技发展资金项目(Z20231811179);河南省农业科学院农业遥感创新团队项目(2024TD28)。

摘　　要：【目的】构建冬小麦主要生育时期生物量估算模型,分析不同水处理和不同年份情景下估算模型的迁移能力,为冬小麦生物量快速估算、表型研究及制定作物水肥决策提供技术支撑。【方法】通过设置不同水氮处理,采用大疆M600 Pro无人机搭载安洲科技K6多光谱成像仪获取冬小麦关键生育期影像,提取影像数字表面模型,基于无人机影像提取株高,通过BP神经网络构建并改进冬小麦生物量估算模型。【结果】水氮耦合自然状态条件下冬小麦实测株高的变化较小,但在氮充足条件下灌溉可增加冬小麦实测株高。无人机提取株高与实测株高的线性决定系数(R^(2))为0.81,即无人机提取株高可解释81%的株高变异。基于无人机遥感影像提取株高构建的冬小麦生物量估算模型,R^(2)、均方根误差(RMSE)、相对分析误差(RPD)分别为0.58、4528.23 kg/ha和1.25,说明该模型可对冬小麦生物量进行快速估算,但模型稳健性较差(RPD<1.4),估算值(16198.27 kg/ha)较实测值(16960.23 kg/ha)偏小,且估算值较分散。通过数据转换,基于生物量/无人机提取株高比值构建的冬小麦生物量估算模型R^(2)、RMSE、RPD分别为0.88、2291.90 kg/ha和2.75,改进后的模型稳健性较强(RPD>2.0),估算值(17478.21 kg/ha)与实测值(17222.59 kg/ha)较接近,模型估算精度提高了51.72%。经验证,改进的冬小麦生物量估算模型在不同水处理和不同年份情景下具有较强的迁移能力,迁移估算模型的R^(2)均在0.85以上,能实现对冬小麦生物量的精准快速估算。【结论】利用无人机影像提取株高信息,通过数据转换,能有效提高冬小麦生物量估算模型的估算精度。改进的冬小麦生物量估算模型在不同水处理和不同年份情景下均表现出较强的迁移能力,但在不同氮水平情景下的迁移能力存在差异,因此,模型迁移利用前应对不同情景数据集进行直方图特征分析,并综合�【Objective】To construct a biomass estimation model for the key growth stages of winter wheat and analyze the transferability of the estimation model under different water treatments and in different years scenarios,which could provide technical support for the rapid estimation of winter wheat above-ground biomass,phenotypic research,and crop water and fertilizer decision-making.【Method】In this study,by setting different water and nitrogen treatments,the DJI M600 Pro unmanned aerial vehicle(UAV)equipped with the Anzhou Technology K6 multispectral imager was used to acquire images of winter wheat during the key growth stages.The digital surface model(DSM)of the images was extracted,and the plant height was extracted based on the UAV images.The winter wheat above-ground biomass estimation model was constructed and improved through the BP neural network method.【Result】Under the natural condition of water-nitrogen coupling,the change in the measured plant height of winter wheat was relatively small,but irrigation under nitrogen-sufficient conditions could increase the measured plant height of winter wheat.The linear determination coefficient(R^(2))between the plant height extracted by the UAV and the measured plant height was 0.81,indicating that the plant height extracted by the UAV could explain 81%of the plant height variation.For the winter wheat above-ground biomass estimation model constructed based on the plant height extracted from UAV remote sensing images,R^(2),rootmean-square error(RMSE)and relative performance deviation(RPD)were 0.58,4528.23 kg/ha and 1.25 respectively.This showed that the model could rapidly estimate the winter wheat above-ground biomass,but the model had poor robustness(RPD<1.4).The estimated value(16198.27 kg/ha)was smaller than the measured value(16960.23 kg/ha),and the estimated values were relatively scattered.Through data transformation,for the winter wheat biomass estimation model constructed based on the ratio(above-graund biomass/plant height extracted by UVA ration,R

关 键 词：冬小麦 生物量 株高 数字表面模型(DSM) 迁移能力 

分 类 号：S127[农业科学—农业基础科学]