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作物学报 ›› 2021, Vol. 47 ›› Issue (12): 2532-2540.doi: 10.3724/SP.J.1006.2021.11002

• 研究简报 • 上一篇    

高分六号遥感影像植被特征及其在冬小麦苗期LAI反演中的应用

张矞勋1,2(), 齐拓野4, 孙源3, 璩向宁1,2, 曹媛1,2, 吴梦瑶1,2, 刘春虹1,2, 王磊1,2,*()   

  1. 1宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地, 宁夏银川 750021
    2宁夏大学西北退化生态系统恢复与重建教育部重点实验室, 宁夏银川 750021
    3中国科学院遥感与数字地球研究所, 北京 100101
    4宁夏大学生态环境学院, 宁夏银川 750021
  • 收稿日期:2021-01-06 接受日期:2021-04-26 出版日期:2021-12-12 网络出版日期:2021-06-15
  • 通讯作者: 王磊
  • 作者简介:E-mail: 17839221635@163.com
  • 基金资助:
    “西部之光”人才培养计划“西部青年学者”A类项目(XAB2017AW06);国家民用空间基础设施陆地观测卫星共性应用支撑平台(Y930280A2F);国家自然科学基金项目(31760707);宁夏回族自治区西部一流学科建设项目(NXYLXK2017B06)

Vegetation characteristics of GF-6 remote sensing image and application on LAI retrieval of winter wheat at seedling stage

ZHANG Yu-Xun1,2(), QI Tuo-Ye4, SUN Yuan3, QU Xiang-Ning1,2, CAO Yuan1,2, WU Meng-Yao1,2, LIU Chun-Hong1,2, WANG Lei1,2,*()   

  1. 1Breeding Base for State Key Laboratory of Land Degradation and Ecosystem Restoration in Northwest China, Ningxia University, Yinchuan 750021, Ningxia, China
    2Key Laboratory for Restoration and Reconstruction of Degenerated Ecosystem in Northwest China under Ministry of Education, Ningxia University, Yinchuan 750021, Ningxia, China
    3Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing, 100101, China
    4School of Ecology and Environment, Ningxia University, Yinchuan 750021, Ningxia, China
  • Received:2021-01-06 Accepted:2021-04-26 Published:2021-12-12 Published online:2021-06-15
  • Contact: WANG Lei
  • Supported by:
    West Light Foundation of the Chinese Academy of Sciences for Young Scholars in Western China A-class-project(XAB2017AW06);Common Application Support Platform for Land Observation Satellite of National Civil Space Infrastructure(Y930280A2F);National Natural Science Foundation of China(31760707);Ningxia Higher Education First-Class Discipline Construction Fund(NXYLXK2017B06)

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摘要：

高分六号(GF-6)遥感卫星是中国首颗精准农业观测的高分卫星, 与高分一号(GF-1)组网运行, 除具有与GF-1 WFV传感器相同的波段外, 国内首次增加了能够有效反映作物特有光谱特性的红边波段。为评价高分六号卫星影像的农作物监测能力, 以苗期冬小麦为研究对象, 结合地面同步观测的冠层光谱和LAI实测数据, 分析高分六号卫星影像的波段数量、波段光谱范围及新增的红边植被波段特征; 通过提取GF-6 WFV影像中的反射率数据构建植被指数, 借助人工神经网络, 对比GF-6 WFV传感器不同植被指数组合构建反演模型的精度, 以此探究GF-6 WFV红边波段在冬小麦苗期叶面积指数反演的应用能力。结果表明, 高分六号遥感影像能较好反映真实的植被特征; 同时在对冬小麦LAI反演时增加GF-6 WFV传感器的两个红边波段及红边植被指数数据, 对苗期冬小麦LAI反演模型精度有较大的提高, R2分别调高12.48%, RMSE降低14.75%。

关键词: 高分六号, 红边参数, 叶面积指数反演, 冬小麦, BP神经网络

Abstract:

As Chinese first high-resolution satellite for precision agricultural observations, the GF-6 remote sensing satellite operates in a network with GF-1. In addition to having the same waveband as the GF-1 WFV sensor, red-edge band is added to the Chinese satellite firstly, which can effectively reflect the unique spectral characteristics of the crop. In order to evaluate the monitoring capabilities of the GF-6 satellite imagery for crops, the seedling stage of winter wheat was selected as the research object. Combined with the ground synchronous observation canopy spectrum and the LAI measured data, we analyzed the quantity of bands, the band spectrum and the features of the added red edge vegetation band of the GF-6 satellite image. Furthermore, we constructed vegetation indices by extracting reflectance data from GF-6 remote sensing images and made comparison between the inversion accuracy of the model established by the combination of different wavebands of GF-6 WFV sensor with the help of artificial neural network. Finally, the application ability of GF-6 WFV red edge band in inversing LAI of winter wheat at seeding stage was explored. The results showed that the GF-6 remote sensing image reflected the characteristics of vegetation more realistically. When inverting the winter wheat LAI, the two red-edge bands and the red-edge vegetation index data of the GF-6 WFV sensor were added, which greatly improved the accuracy of the winter wheat LAI inversion model at seedling stage, with the increased R2 of 12.48% and the decreased RMSE of 14.75%.

Key words: GF-6, red edge parameter, LAI inversion, winter wheat, BP neural network

图1

研究区地理位置与样地分布图 GF-6 WFV传感器4、3、2波段合成。"

表1

植被指数及计算公式"

植被指数
Vegetation index		 名称
Name		 公式
Equation		 参考文献
Reference
NDVI 归一化植被指数
Normalized Difference Vegetation Index		 NDVI=(NIR-R)/(NIR+R) [30]
NDVI710 红边归一化植被指数710
Red Edge Normalized Vegetation Index 710		 NDVI710=(RE Ⅰ-R)/(NIR+R) [30]
NDVI750 红边归一化植被指数750
Red Edge Normalized Vegetation Index 750		 NDVI750=(REⅡ-R)/(NIR+R) [30]
ARVI 大气阻抗植被指数
Atmosphere Resistant Vegetation Index		 ARVI=(NIR-(2R-BLUE))/(NIR+(2R-BLUE)) [31]
SAVI 土壤调节植被指数
Soil-Adjusted Vegetation Index		 SAVI=(NIR-R)/(NIR+R+L)×(1+L) (L=0.5) [32]
CIgreen 叶绿素指数
Green chlorophyll index		 CIgreen=NIR/GREEN-1 [32]

表2

GF-1 WFV、GF-6 WFV、Landsat8-OLI影像对应波段参数对比"

传感器
Sensor		 空间分辨率
Spatial resolution (m)		 辐射分辨率
Radiometric
resolution (bit)		 蓝
Blue
(nm)		 绿
Green
(nm)		 红
Red
(nm)		 近红外
NIR
(nm)		 红边I
RE I
(nm)		 红边II
RE II
(nm)
GF-6 WFV 16 12 0.45-0.52 0.52-0.59 0.63-0.69 0.77-0.89 0.69-0.73 0.30-0.77
GF-1 WFV 16 10 0.45-0.52 0.52-0.59 0.63-0.69 0.77-0.89 — —
Landsat8-OLI 30 12 0.45-0.51 0.53-0.59 0.64-0.67 0.85-0.88 — —

图2

传感器光谱响应函数及冬小麦光谱曲线"

图3

植被指数与叶面积指数交互对比图"

表3

BP神经网络反演模型精度"

处理
Treatment		 精度Accuracy (R2)
训练样本
Training samples		 测试样本
Testing samples
组合1 Combination 1 0.7235 0.6250
组合2 Combination 2 0.7802 0.6862
组合3 Combination 3 0.7272 0.6338
组合4 Combination 4 0.7944 0.7126

图4

BP神经网络模型反演结果"

图5

BP神经网络模型反演精度"

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