中图分类号：TP751；S812 文献标识码：A 文章编号：1007-0435（2025）07-2206-13

引用格式：，等.基于无人机获取草地植物地上生物量的信息损失分析及修正[J].草地学报，2025，33 （7） ：2206-2218 LI Jia-xin，JINGui-li，LIU Wen-hao，et al.Analyzing and Modifyingof Grassland Plant InformationLoss ofAboveg round Biomass based onUAV Data[J].Acta Agrestia Sinica，2025，33（7） ：2206-2218

Analyzingand ModifyingofGrassland PlantInformation Lossof AbovegroundBiomassbasedonUAVData

LI Jia-xin，JIN Gui-li*，LIU Wen-hao，WANG Sheng-ju，CHEN Meng-tian，LI Wen-xiong， DU Wen-lin，LI Chao，HU Xiu-wen

（Colege ofGrassand Science，Xinjiang Agricultural University/Xinjiang KeyLaboratoryof GrasslandResources andEcology， Urumqi，Xinjiang 830005，China）

Abstract：In order to explore the information lossin UAV（Unmanned aerial vehicle） monitoring of grassland biomass，this study compared the ground hyperspectral and on-site measured data with the multispectral images of the desert grassland community of Seriphidium transiliense，and used the vegetation index combined with linear regresson method to construct the inversion model of aboveground biomass of the plant community at different phenological periods.The error and rule of UAV acquisition of aboveground biomass were discussed and the modified model was established.The result shows that：（1）In April，June and September，the best UAV multi-spectral models were NDVI（Normalized diference vegetation index），RVI（Ration vegetation index） and NDVI，with accuracy of 65.61% ， 48.26% and 61.59% ，respectively. Among all the ground hyperspectral data， NDVI is the most optimal， and the accuracy was 71. 77% ， 53.63% and 67.58% ，respectively.（2） The ground hyperspectral retrieval ability was better than the UAV multispectral retrieval ability，the loss rates ofmultispectral data from UAV at each phenological stagewere ΔNDVIgt;ΔRVIgt;I OVI（Differencevegetation index），and the loss rates of optimal model were 8.58% ， 9.89% and 8.87% ，respectively.（3）The UAV multi-spectral accuracy of all models was improved by 0.02%～3.74% ，The modified NDVI mode in April had the highest accuracy of 66.36%（y=403.431x+17.5936） .In summary，by screening the best inversion models，analyzing the information loss of diferent platforms，and modifying the UAV multi-spectral of the UAV can give full play to the advantages of diferent platforms and improve the estimation accuracy.It also proves the feasibility of using high-resolution remote sensing data to modify low-spatial-resolution data.

Keywords： Unmanned aerial vehicle;Ground hyperspectral;Aboveground;Inversion;Informationloss;Modification

地上生物量（Abovegroundbiomass，AGB）是反映草地生长状况的关键指标，准确高效获取地上生物量信息对草地资源管理至关重要[1-2]。目前多采用地面测量和遥感监测估算地上生物量，地面测量获取数据信息准确有效，但费时费力，受时间、天气、交通等因素影响难以实现区域性动态监测[3]。光学遥感为大面积高效监测植物群落结构特征提供了可能。无人机（Unmannedaerialvehicle，UAV能够捕获地物的光谱特征，常用于植物冠层群落特征提取[4-5]，其搭载传感器具有灵活、高效、便捷的特点，克服了卫星和航空遥感的时空分辨率限制，弥补了地面遥感采集效率较低，空间覆盖能力有限的不足，与地面调查数据更好地衔接，在地面观测、卫星和航空遥感之间搭建起信息推绎的桥梁，显著提高了生态系统动态监测的时间频度和空间尺度[6-7]。

目前，大量研究通过光谱植被指数构建多元线性回归（Multiplelinearregression，MLR）模型对植被的叶面积指数、叶绿素和地上生物量等属性进行精确研究[8-10]，并且利用优化植被指数[11]、结合光谱变换等形式进一步提高模型的估测精度，在预测森林蓄积量12、农作物[13和草地生物量[14等研究中得到应用。然而，无人机在数据采集过程中易受到外部环境干扰和下垫面影响，即使使用最先进的无人机传感器系统，仍然可能遗漏大量的植被信息，从而降低植物群落特征的估计精度[15]，因此了解无人机获取草地植物群落地上生物量数据信息损失情况亟待进一步探讨，通过与地面高光谱进行对比能够分析不同数据源的精度差异，判断无人机多光谱在植物地上生物量估测中是否存在信息损失。……