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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (12): 2532-2540.doi: 10.3724/SP.J.1006.2021.11002

• RESEARCH NOTES • Previous Articles    

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 Online:2021-12-12 Published:2021-06-15
  • Contact: WANG Lei E-mail:17839221635@163.com;WL8999@163.com
  • 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)


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

Fig. 1

Geographical location of the study area and the sample sites Composite map of 4, 3, and 2 bands of GF-6 WFV sensor."

Table 1

Vegetation indices and equations"

Vegetation index
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]

Table 2

Key property of GF-1 WFV, GF-6 WFV, and Landsat8-OLI"

Spatial resolution (m)
resolution (bit)


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

Fig. 2

Sensor spectral response function and spectral curve in winter wheat"

Fig. 3

Interactive comparison of vegetation index and leaf area index"

Table 3

Accuracy of BP neural network inversion model"

精度Accuracy (R2)
Training samples
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

Fig. 4

Inversion results of BP neural network"

Fig. 5

Inversion accuracy of BP neural network model"

[1] 毛博慧, 李民赞, 孙红, 刘豪杰, 张俊逸, Zhang Q. 冬小麦苗期叶绿素含量检测光谱学参数寻优. 农业工程学报, 2017, 33(增刊1):164-169.
Mao B H, Li M Z, Sun H, Liu H J, Zhang J J, Zhang Q. Optimization of spectroscopy parameters and prediction of chlorophyll content at seeding stage of winter wheat. Trans CSAE, 2017, 33(S1):164-169 (in Chinese with English abstract).
[2] 张领先, 陈运强, 李云霞, 马浚诚, 杜克明, 郑飞翔, 孙忠富. 可见光光谱的冬小麦苗期地上生物量估算. 光谱学与光谱分析, 2019, 39:2501-2506.
Zhang L X, Chen Y Q, Li Y X, Ma J C, Du K M, Zheng F X, Sun F Z. Estimating above ground biomass of winter wheat at early growth stages based on visual spectral. Spectr Spectr Anal, 2019, 39:2501-2506 (in Chinese with English abstract).
[3] 马培培, 李静, 柳钦火, 何彬彬, 赵静. 中国区域MuSyQ叶面积指数产品验证与分析. 遥感学报, 2019, 23:1232-1252.
Ma P P, Li J, Liu Q H, He B B, Zhao J. Multisensor synergistic quantitative leaf area index product of China. J Remote Sens, 2019, 23:1232-1252 (in Chinese with English abstract).
[4] 龙泽昊, 秦其明, 张添源, 许伟. 基于长短期记忆网络的冬小麦连续时序叶面积指数预测. 光谱学与光谱分析, 2020, 40:898-904.
Long Z H, Qin Q M, Zhang T Y, Xu W. Prediction of continuous time series leaf area index based on long short-term memory network: a case study of winter wheat. Spectr Spectr Anal, 2020, 40:898-904 (in Chinese with English abstract).
[5] 栾青, 郭建平, 马雅丽, 张丽敏, 王婧瑄. 玉米叶面积指数估算通用模型. 中国农业气象, 2020, 41:506-519.
Luan Q, Guo J P, Ma Y L, Zhang L M, Wang J X. A general model for estimating leaf area index of maize. Chin J Agrometeorol, 2020, 41:506-519 (in Chinese with English abstract).
[6] 李振洲, 贺正, 贾彪, 刘志, 付江鹏, 刘慧芳. 滴灌玉米叶面积指数归一化建模与特征分析. 中国土壤与肥料, 2020, (3):189-195.
Li Z Z, He Z, Jia B, Liu Z, Fu J P, Liu H F. LAI normalization modeling and characteristic analysis of maize with drip irrigation. Soil Fert Sci China, 2020, (3):189-195 (in Chinese with English abstract).
[7] 常文涛, 王浩, 宁晓刚, 张翰超. 融合Sentinel-2红边波段和Sentinel-1雷达波段影像的扎龙湿地信息提取. 湿地科学, 2020, 18(1):10-19.
Chang W T, Wang H, Ning X G, Zhang H C. Extraction of Zhalong wetlands information based on images of sentinel-2 red-edge bands and sentinel-1 radar bands. Wetland Sci, 2020, 18(1):10-19 (in Chinese with English abstract).
[8] 吾木提·艾山江, 买买提·沙吾提, 陈水森, 李丹. 基于GF-1/2卫星数据的冬小麦叶面积指数反演. 作物学报, 2020, 46:787-797.
Umut H, Mamat S, Chen S S, Li D. Inversion of leaf area index of winter wheat based on GF-1/2 image. Acta Agron Sin, 2020, 46:787-797 (in Chinese with English abstract).
[9] 刘明星, 李长春, 李振海, 冯海宽, 杨贵军, 陶惠林. 基于高光谱遥感与SAFY模型的冬小麦地上生物量估算. 农业机械学报, 2020, 51(2):192-202.
Liu M X, Li C C, Li Z H, Feng H K, Yang G J, Tao H L. Estimation of dry aerial mass of winter wheat based on coupled hyperspectral remote sensing and SAFY model. Trans CSAM, 2020, 51(2):192-202 (in Chinese with English abstract).
[10] 陶惠林, 徐良骥, 冯海宽, 杨贵军, 苗梦珂, 林博文. 基于无人机高光谱长势指标的冬小麦长势监测. 农业机械学报, 2020, 51(2):180-191.
Tao H L, Xu L J, Feng H K, Yang G J, Miao M K, Lin B W. Monitoring of winter wheat growth based on UAV hyperspectral growth index. Trans CSAM, 2020, 51(2):180-191 (in Chinese with English abstract).
[11] 王磊, 耿君, 杨冉冉, 田庆久, 杨闫君, 周洋. 高分一号卫星影像特征及其在草地监测中的应用. 草地学报, 2015, 23:1093-1100.
Wang L, Geng J, Yang R R, Tian Q J, Yang Y J, Zhou Y. Characteristics and application of GF-1 image in glassland monitoring. Acta Agrest Sin. 2015, 23:1093-1100 (in Chinese with English abstract).
[12] 张漫, 苗艳龙, 仇瑞承, 季宇寒, 李寒, 李民赞. 基于车载三维激光雷达的玉米叶面积指数测量. 农业机械学报, 2019, 50(6):12-21.
Zhang M, Miao Y L, Qiu R C, Ji Y H, Li H, Li M Z. Maize leaf area index measurement based on vehicle 3D LiDAR. Trans CSAM, 2019, 50(6):12-21 (in Chinese with English abstract).
[13] 程雪, 贺炳彦, 黄耀欢, 孙志刚, 李鼎, 朱婉雪. 基于无人机高光谱数据的玉米叶面积指数估算. 遥感技术与应用, 2019, 34:775-784.
Cheng X, He B Y, Huang Y H, Sun Z G, Li D, Zhu W X. Estimation of corn leaf area index based on UAV hyperspectral image. Remote Sens Technol Appl, 2019, 34:775-784 (in Chinese with English abstract).
[14] 江杰, 张泽宇, 曹强, 田永超, 朱艳, 曹卫星, 刘小军. 基于消费级无人机搭载数码相机监测小麦长势状况研究. 南京农业大学学报, 2019, 42:622-631.
Jiang J, Zhang Z Y, Cao Q, Tian Y C, Zhu Y, Cao W X, Liu X J. Use of a digital camera mounted on a consumer-grade unmanned aerial vehicle to monitor the growth status of wheat. J Nanjing Agric Univ, 2019, 42:622-631 (in Chinese with English abstract).
[15] Xing N C, Huang W J, Xie Q Y, Shi Y, Ye H C, Dong Y Y, Wu M Q, Sun G, Jiao Q J. A transformed triangular vegetation index for estimating winter wheat leaf area index. Remote Sens, 2019, 12:16-34.
doi: 10.3390/rs12010016
[16] 孙红, 刘宁, 邢子正, 张智勇, 李民赞, 吴静珠. 马铃薯冠层光谱响应特征参数优化与生长期判别. 光谱学与光谱分析, 2019, 39:1870-1877.
Sun H, Liu N, Xing Z Z, Zhang Z Z, Li M Z, Wu J S. Parameter optimization of potato spectral response characteristics and growth stage identification. Spectr Spectr Anal, 2019, 39:1870-1877 (in Chinese with English abstract).
[17] 刘洁, 李静, 柳钦火, 何彬彬, 于文涛. 全球典型植被叶片光谱特征及其对LAI反演的影响分析. 遥感技术与应用, 2019, 34(1):155-165.
Liu J, Li J, Liu Q H, He B B, Yu W T. Global leaf spectral characteristics of typical vegetation and it’s impacts on LAI inversion. Remote Sens Technol Appl, 2019, 34(1):155-165 (in Chinese with English abstract).
[18] 葛元梅, 陈翔宇, 洪帅, 马露露, 吕新, 张泽. 基于红边参数不同品种的估算模型. 新疆农业科学, 2019, 56:1032-1040.
Ge Y M, Chen X Y, Hong S, Ma L L, Lyu X, Zhang Z. Establishment of estimation model for different varieties based on red edge parameters. Xinjiang Agric Sci, 2019, 56:1032-1040 (in Chinese with English abstract).
[19] 余蛟洋, 常庆瑞, 班松涛, 田明璐, 由明明. 猕猴桃叶片SPAD值高光谱估算模型构建. 干旱地区农业研究, 2018, 36(6):168-174.
Yu J Y, Chang Q R, Ban S T, Tian M L, You M M. Hyperspectral models for estimating SPAD values of kiwifruit leaves. Agric Res Arid Areas, 2018, 36(6):168-174 (in Chinese with English abstract).
[20] Jiang S, Wang F, Shen L M, Liao G P. Local detrended fluctuation analysis for spectral red-edge parameters extraction. Nonlin Dynam, 2018, 93:995-1008.
doi: 10.1007/s11071-018-4241-y
[21] 罗丹, 常庆瑞, 齐雁冰. 基于红边参数和人工神经网络的苹果叶片叶绿素含量估算. 西北农林科技大学学报(自然科学版), 2019, 47(1):107-115.
Luo D, Chang Q R, Qi Y B. Estimation of chlorophyll content in apple leaves based on red edge parameters and artificial neural network. J Northwest A&F Univ (Nat Sci Edn), 2019, 47(1):107-115 (in Chinese with English abstract).
[22] 刘俊, 孟庆岩, 葛小三, 刘顺喜, 陈旭, 孙云晓. 基于BP神经网络的夏玉米多生育期叶面积指数反演研究. 遥感技术与应用, 2020, 35(1):174-184.
Liu J, Meng Q Y, Ge X S, Liu S X, Chen X, Sun Y X. Leaf area index inversion of summer maize at multiple growth stages based on BP neural network. Remote Sens Technol Appl, 2020, 35(1):174-184 (in Chinese with English abstract).
[23] Asha V, Anil K, Priyadarshi U. Effect of red-edge region in fuzzy classification: a case study of sunflower crop. J Indian Soc Remote Sens, 2020, 48:645-657.
doi: 10.1007/s12524-020-01109-4
[24] 李雪玲, 董莹莹, 朱溢佞, 黄文江. 基于EnMAP卫星和深度神经网络的LAI遥感反演方法. 红外与毫米波学报, 2020, 39(1):111-119.
Li X L, Dong Y Y, Zhu Y N, Huang W J. Leaf area index estimation with EnMAP hyperspectral data based on deep neural network. J Infr Millim Waves, 2020, 39(1):111-119 (in Chinese with English abstract).
[25] 王强, 舒清态, 罗洪斌, 王冬玲, 字李, 谢福明. 基于机载LiDAR和光学遥感数据的热带橡胶林叶面积指数反演. 西北林学院学报, 2020, 35(4):132-139.
Wang Q, Shu Q T, Luo H B, Wang D L, Zi L, Xie F M. Inversion of leaf area index of tropical Hevea brasiliensis forest based on airborne LiDAR and optical remote sensing data. J Northwest For Univ, 2020, 35(4):132-139 (in Chinese with English abstract).
[26] 郭云开, 许敏, 张晓炯, 刘雨玲. 结合PRO-4SAIL和BP神经网络的叶绿素含量高光谱反演. 测绘通报, 2020, (3):21-24.
Guo Y K, Xu M, Zhang X J, Liu Y L. Chlorophyll hyperspectral inversion with PRO-4SAIL and BP neural networks. Bull Surv Map, 2020, (3):21-24 (in Chinese with English abstract).
[27] 陈士城, 王宝水. 基于神经网络的LAI遥感反演影响因素分析. 地理空间信息, 2019, 17(12):72-74.
Chen S C, Wang B S. Analysis of influence factors for LAI inversion based on neural network. Geosp Inf, 2019, 17(12):72-74 (in Chinese with English abstract).
[28] 赵磊. 东营市土地资源遥感综合评价. 遥感信息, 2008, (1):82-86.
Zhao L. An integrated assessment of land resources in Dongying city by remote sensing. Remote Sens Inf, 2008, (1):82-86 (in Chinese with English abstract).
[29] Wang L, Yang R R, Tian Q J, Yang Y J, Zhou Y, Sun Y, Mi X F. Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications. Remote Sens, 2015, 7:2089-2108.
doi: 10.3390/rs70202089
[30] 梁继, 郑镇炜, 夏诗婷, 张晓彤, 唐媛媛. 高分六号红边特征的农作物识别与评估. 遥感学报, 2020, 24:1168-1179.
Liang J, Zheng Z W, Xia S T, Zhang X T, Tang Y Y. Crop recognition and evaluation-using red edge features of GF-6 satellite. J Remote Sens, 2020, 24:1168-1179 (in Chinese with English abstract).
[31] 张仁华, 饶农新, 廖国男. 植被指数的抗大气影响探讨. 植物学报, 1996, 38(1):53-62.
Zhang R H, Rao N X, Liao G N. Approach for a vegetation index resistant to atmospheric effect. J Integr Plant Biol, 1996, 38(1):53-62 (in Chinese with English abstract).
[32] Ma Y, Fang S H, Peng Y, Gong Y, Wang D. Remote estimation of biomass in winter oilseed rape ( Brassica napus L.) using canopy hyperspectral data at different growth stages. Appl Sci, 2019, 9:545-563.
doi: 10.3390/app9030545
[33] 吴晓萍, 徐涵秋, 蒋乔灵. GF-1、GF-2与Landsat-8卫星多光谱数据的交互对比. 武汉大学学报(信息科学版), 2020, 45(1):150-158.
Wu X P, Xu H Q, Jiang Q L. Cross-comparison of GF-1, GF-2 and Landsat-8 OLI sensor data. Geom Inf Sci Wuhan Univ, 2020, 45(1):150-158 (in Chinese with English abstract).
[34] 郭建茂, 王星宇, 李淑婷, 谢晓燕, 刘荣花, 于庚康. 基于冠层光谱红边参数和植被指数的冬小麦水分胁迫监测. 江苏农业科学, 2019, 47(10):88-94.
Guo J M, Wang X Y, Li S T, Xie X Y, Liu R H, Yu K K. Study on water stress monitoring of winter wheat based on canopy spectral red-edge parameters and vegetation index. Jiangsu Agric Sci, 2019, 47(10):88-94 (in Chinese with English abstract).
[35] 张沁雨, 李哲, 夏朝宗, 陈健, 彭道黎. 高分六号遥感卫星新增波段下的树种分类精度分析. 地球信息科学学报, 2019, 21:1619-1628.
doi: 10.12082/dqxxkx.2019.190116
Zhang Q Y, Li Z, Xia C Z, Chen J, Peng D L. Tree species classification based on the new bands of GF-6 remote sensing satellite. J Geo-inf Sci, 2019, 21:1619-1628 (in Chinese with English abstract).
[36] 李文杰, 郭晓雷, 杨玲波, 闫鸣, 邹晨曦, 方亚华, 孙涵, 黄敬峰. 基于GF-6卫星影像多特征优选的酿酒葡萄精准识别. 农业工程学报, 2020, 36(18):165-173.
Li W J, Guo X L, Yang L B, Yan M, Zou C X, Fang Y H, Sun H, Huang J F. Accurate recognition of wine grapes using multi-feature optimization based on GF-6 satellite images. Trans CSAE, 2020, 36(18):165-173 (in Chinese with English abstract).
[37] Darvishzadeh R, Atzberger C, Skidmore A K, Abkar A A. Leaf Area Index derivation from hyperspectral vegetation indices and the red edge position. Int J Remote Sens, 2009, 30:6199-6218.
doi: 10.1080/01431160902842342
[38] 郑踊谦, 董恒, 张城芳, 黄鹏. 植被指数与作物叶面积指数的相关关系研究. 农机化研究, 2019, 41(10):1-6.
Zheng Y Q, Dong H, Zhang C F, Huang P. Study on the relationship between vegetation indices and leaf area index of crop. J Agric Mech Res, 2019, 41(10):1-6 (in Chinese with English abstract).
[39] 徐卫星, 薛华柱, 靳华安, 李爱农. 融合遥感先验信息的叶面积指数反演. 遥感技术与应用, 2019, 34:1235-1244.
Xu W X, Xue H Z, Jin H A, Li A N. Retrieval of leaf area index by fusing prior information from remote sensing data. Remote Sens Technol Appl, 2019, 34:1235-1244 (in Chinese with English abstract).
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