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遥感技术与应用  2022, Vol. 37 Issue (1): 272-278    DOI: 10.11873/j.issn.1004-0323.2022.1.0272
草地遥感专栏     
基于无人机与卫星遥感的草原地上生物量反演研究
李淑贞(),徐大伟,范凯凯,陈金强,佟旭泽,辛晓平,王旭()
中国农业科学院农业资源与农业区划研究所,北京 100081
Research of Grassland Aboveground Biomass Inversion based on UAV and Satellite Remoting Sensing
Shuzhen Li(),Dawei Xu,Kaikai Fan,Jinqiang Chen,Xuze Tong,Xiaoping Xin,Xu Wang()
Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China
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摘要:

草原生物量是评价草原生态系统功能的重要参数。为了快速、准确、有效地估算草原地上生物量,以呼伦贝尔草原为研究区,基于无人机多光谱影像和卫星遥感(Sentinel-2)影像,选择GNDVI、LCI、NDRE、NDVI、OSAVI、EVI等6个植被指数,结合实测地上生物量数据,建立植被指数回归模型,并采用留一法交叉验证进行精度评价。结果表明:基于无人机多光谱影像的LCI-生物量回归模型(RRMSE为18%,测量值与预测值R2为0.70)和NDRE-生物量模型(RRMSE为18%,测量值与预测值R2达到0.71)精度高于其他植被指数回归模型;基于无人机多光谱影像的生物量—植被指数模型(RRMSE均低于22%)模拟精度均优于基于Sentinel-2影像的生物量—植被指数模型(RRMSE均高于25%),可以更精确地反演草原地上生物量,研究结果可为草原生物量精准反演提供科学方法和依据。

关键词: 无人机Sentinel?2地上生物量红边植被指数    
Abstract:

Grassland biomass is an important parameter to evaluate the grassland ecosystem function. To estimate the grassland aboveground biomass rapidly, accurately and effectively, six vegetation indices (GNDVI, LCI, NDRE, NDVI, OSAVI and EVI) were selected and calculated based on UAV multi-spectral images and satellite remote sensing (Sentinel-2) images, combined with the ground measured biomass data. The vegetation index regression model was established, and the precision was verified by the left one method. The results showed that the accuracy of LCI-biomass regression model (RRMSE = 18%, the measured and predicted R2 = 0.70) and NDRE-biomass model (RRMSE = 18%, the measured and predicted R2 = 0.71) based on UAV multi-spectral images was higher than that of other vegetation -biomass models. The biomass-vegetation index models based on UAV multi-spectral images (RRMSE lower than 22%) have better simulation accuracy than Sentinel-2 biomass-vegetation index models (RRMSE higher than 25%), which can more accurately retrieve the aboveground biomass of Hulunbuir grassland. The results can provide scientific methods and basis for accurate retrieval of grassland biomass.

Key words: UAV    Sentinel-2    Aboveground biomass    Red edge    Vegetation index
收稿日期: 2021-06-15 出版日期: 2022-04-08
ZTFLH:  S812  
基金资助: 国家重点研发计划项目(2017YFE0104500);中央级公益性科研院所基本业务费专项(1610132020028)
通讯作者: 王旭     E-mail: 82101202278@caas.cn;wangxu01@caas.cn
作者简介: 李淑贞(1996-),女,山东寿光人,硕士研究生,主要从事农业资源与环境遥感研究。E?mail:82101202278@caas.cn
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引用本文:

李淑贞,徐大伟,范凯凯,陈金强,佟旭泽,辛晓平,王旭. 基于无人机与卫星遥感的草原地上生物量反演研究[J]. 遥感技术与应用, 2022, 37(1): 272-278.

Shuzhen Li,Dawei Xu,Kaikai Fan,Jinqiang Chen,Xuze Tong,Xiaoping Xin,Xu Wang. Research of Grassland Aboveground Biomass Inversion based on UAV and Satellite Remoting Sensing. Remote Sensing Technology and Application, 2022, 37(1): 272-278.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2022.1.0272        http://www.rsta.ac.cn/CN/Y2022/V37/I1/272

图1  研究区及样点地理位置示意图
植被指数计算公式
绿色归一化植被指数GNDVIρnir-ρgreenρnir+ρgreen
叶片叶绿素指数LCIρnir-ρrededgeρnir+ρred
归一化差异红边NDREρnir-ρrededgeρnir+ρrededge
归一化植被指数NDVIρnir-ρredρnir+ρred
优化土壤调整植被指数OSAVIρnir-ρredρnir+ρred+0.16
增强型植被指数EVI2.5×ρnir-ρredρnir+6×ρred-7.5×ρblue+1
表1  植被指数与计算方法
植被指数生物量估算模型R2FP
GNDVI鲜重y =840.19x - 104.500.6199.28***
干重y =326.90x - 48.540.5682.87***
LCI鲜重y = 1 453.52x + 68.410.72165.50***
干重y = 566.46x + 18.590.67130.80***
NDRE鲜重y = 2 977.87x - 5.120.73168.60***
干重y = 1 143.90x - 8.370.66121.80***
NDVI鲜重y = 542.77x - 17.150.6199.87***
干重y = 212.41x - 15.260.5785.60***
OSAVI鲜重y = 992.10x + 45.950.6199.23***
干重y = 394.52x + 7.840.5991.83***
EVI鲜重y = 110.32x + 52.820.64113.30***
干重y = 43.64x + 11.080.61101.40***
表2  基于无人机植被指数的草原地上生物量回归模型
图2  无人机与卫星植被指数模型精度对比
GNDVILCINDRENDVIOSAVIEVI
鲜重0.580.700.710.590.580.62
干重0.540.650.640.550.570.59
表3  基于无人机估算模型的精度评价
植被指数生物量估算模型R2FP
GNDVI鲜重y =664.1x-81.900.277.30*
干重y =273.44x-48.670.256.72*
LCI鲜重y =1 572.61x+73.030.3611.27**
干重y =580.98x+24.270.277.52*
NDRE鲜重y =2 536.58x+52.590.3510.97**
干重y =887.37x+21.370.246.34*
NDVI鲜重y =376.02x+69.720.267.14*
干重y =161.93x+9.560.277.43*
OSAVI鲜重y =653.46x+23.790.277.48*
干重y =258.93x-1.040.246.22*
EVI鲜重y =620.67x+48.880.318.88**
干重y =252.15x+6.490.287.85*
表4  基于Sentinel-2植被指数草原地上生物量回归模型
GNDVILCINDRENDVIOSAVIEVI
鲜重0.120.250.220.120.120.13
干重0.110.130.120.120.100.11
表5  基于Sentinel-2估算模型的精度评价
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