Please wait a minute...
img

官方微信

遥感技术与应用  2019, Vol. 34 Issue (4): 775-784    DOI: 10.11873/j.issn.1004-0323.2019.4.0775
作物信息提取专栏     
基于无人机高光谱数据的玉米叶面积指数估算
程雪1,2(),贺炳彦1,黄耀欢2(),孙志刚2,李鼎1,朱婉雪2
1. 长安大学 地质工程与测绘学院,陕西 西安 710000
2. 中国科学院地理科学与资源研究所,北京 100101
Estimation of Corn Leaf Area Index based on UAV Hyperspectral Image
Xue Cheng1,2(),Bingyan He1,Yaohuan Huang2(),Zhigang Sun2,Ding Li1,Wanxue Zhu2
1. Chang 'an University Geological Engineering and Surveying Institute, Xi'an 710000,China
2. Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences,Beijing 100101,China
 全文: PDF(4813 KB)   HTML
摘要:

无人机高光谱遥感是低成本、高精度获取精细尺度农作物生物物理参数和生物化学参数的新型手段,以此快速反演叶面积指数(Leaf Area Index, LAI)对作物长势评价、产量预测具有重要意义。以山东禹城市玉米为研究对象,利用PROSAIL辐射传输模型模拟玉米冠层反射率获取LAI特征响应波段结合相关性定量分析获取对LAI变化最为敏感的波段,并以此计算6种植被指数(Vegetation Index,VI),利用6种回归模型分别对单一特征波段和VI进行反演建模,以实测LAI评定模型精度。研究表明,光谱反射率中516、636、702、760和867 nm等波段对LAI变化最为敏感,以此建立的单一特征波段反演模型预测LAI精度R2为0.44~0.58;RMSE为0.16~0.18,其中636 nm建立的模型(LAI=21.86exp(-29.47R636))相比其他反演模型预测精度较高(R2=0.58,RMSE=0.16);6种植被指数与LAI高度相关,相关性系数R 2为0.85~0.86,以此建立的反演模型相比单一特征波段反演模型精度有所提高,R2为0.66~0.72,RMSE为0.12~0.14;其中mNDVI构建的LAI估算模型(LAI=exp(2.76~1.77/mNDVI))精度最高(R2=0.72,RMSE=0.13)。无人机高光谱遥感是快速、无损监测农作物生长信息的有效手段,为指导精细化尺度作物管理提供依据。

关键词: 无人机高光谱叶面积指数(LAI)    
Abstract:

UAV hyperspectral remote sensing is a new means of low-cost, high-precision acquisition of fine-scale crop biophysical parameters and biochemical parameters, so that the rapid inversion of Leaf Area Index (LAI) has a crop growth assessment and yield prediction. Taking the corn of Shandong Yucheng as the research object, using the PROSAIL radiation transmission model to simulate the corn canopy reflectivity to obtain the LAI characteristic response band,combining correlation quantitative analysis to obtain the most sensitive band for LAI changes, and calculating the 6 vegetation index (VI). Inversion models were modeled on a single sensitive band and VI using six regression models to measure the accuracy of the model by LAI.Studies have shown that the spectral reflectance of 516nm, 636nm, 702nm, 760nm, 867nm are most sensitive to LAI changes, and the single-band inversion model established to predict LAI accuracy (R 2=0.44~0.58; RMSE=0.16~0.18).The model established by 636nm (LAI=21.86exp(-29.47R636)) has higher prediction accuracy than other inversion models (R 2=0.58, RMSE=0.16); The 6 vegetation indexes are closely related to LAI with correlation at a significant level(R 2=0.85~0.86). The accuracy of the established inversion model is improved compared with the single characteristic band inversion model (R 2=0.66~0.72,RMSE=0.12~0.14);The LAI estimation model (LAI=exp(2.76~1.77/mNDVI)) constructed by mNDVI has the highest accuracy (R 2=0.72, RMSE=0.13). UAV hyperspectral remote sensing is an effective means for rapid and non-destructive monitoring of crop growth information, and provides a basis for guiding fine-scale crop management.

Key words: Unmanned Aerial Vehicle (UAV)    Hyperspectral    Leaf Area Index (LAI)
收稿日期: 2019-01-12 出版日期: 2019-10-16
ZTFLH:  TP79  
基金资助: 国家重点研发计划项目“基于无人机的固定源大气污染源排放现场执法遥测技术方法研究”(2016YFC0208202);集成与示范”(2017YFB0503005)
通讯作者: 黄耀欢     E-mail: 1095111645@qq.com;huanyh@lreis.ac.cn
作者简介: 程 雪(1993-),女,黑龙江齐齐哈尔人,硕士研究生,主要从事农业遥感应用研究。E?mail:1095111645@qq.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
程雪
贺炳彦
黄耀欢
孙志刚
李鼎
朱婉雪

引用本文:

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[J]. 遥感技术与应用, 2019, 34(4): 775-784.

Xue Cheng,Bingyan He,Yaohuan Huang,Zhigang Sun,Ding Li,Wanxue Zhu. Estimation of Corn Leaf Area Index based on UAV Hyperspectral Image. Remote Sensing Technology and Application, 2019, 34(4): 775-784.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.4.0775        http://www.rsta.ac.cn/CN/Y2019/V34/I4/775

图 1  研究区位置示意图
参数 取值
光谱范围/nm 380~1 000
光谱通道 270
空间通道 640
光谱分辨率/nm 2~3
空间分辨率/m 0.06
表1  HyperspecTM VNIR成像参数
图2  实验田与LAI采样值
输入参数 描述 取值范围及默认值
Lower WaveLength/nm 波长下限 400
Upper WaveLength/nm 波长上限 1 000
WaveLength increment 波长增量 2.20
Cab 叶片叶绿素含量 40
N 叶片结构参数 1.4
Cm 叶片干物质含量 0.035
Cw 等效水厚度 0.015
表2  PROSPECT模型设置参数
图3  PROSPECT模型模拟反射率与透过率
输入参数 描述 取值范围及默认值
LAI 叶面积指数 0.4~1.7
LAD 平均叶倾角 0.5~8
SI 热点大小 0.001
PSOIL 土壤亮度参数 0.3
TTS/% 太阳天顶角 400
TTO/% 观测天顶角 30
PSI/% 观测相对方位角 0
表3  SAIL模型设置参数
图4  PROSAIL模型模拟玉米冠层光谱反射率
图5  光谱反射率与LAI相关性
波段/nm 响应波段
516 636 702 760 867
LAI -0.82** -0.84** -0.84** 0.66** 0.69**
注:**表示0.01水平差异极显著
表4  LAI与响应波段相关性系数
植被指数 计算公式 参考文献 相关性系数
NDVI (R867-R636)/(R867+R636) Carlson T N(1997)[22] 0.86**
RVI R867/R636 Anderson G L(1993)[23] 0.86**
rNDVI (R760-R702)/(R760+R702) Prasad B et al (2007)[24] 0.85**
mNDVI (R760-R702)/(R760+R702-2R445) Jurgens C(1997)[25] 0.85**
EVI2 2.5·(R867-R636)/(R867+2.4R636+1) Mondal P (2011)[26] 0.86**
OSAVI 1.16·(R867-R636)/(R867+R636+0.16) Steven M D(1998)[27] 0.85**
注:**表示0.01水平差异极显著
表5  LAI与植被指数相关性系数
波段/nm 回归模型 数学模型 决定系数(R2) F值
516 线性 y=-41.15 x + 5.06 0.68 62.66
对数 y=-8.47-4.09 log(x) 0.67 60.88
二次 y=-3.77 + 135.88 x + -884.90 x2 0.69 32.07
y=1.48 x -4.78 0.72 69.22
S型 y=exp( -4.82 + 0.47 / x ) 0.68 64.41
636 线性 y=-24.94 x + 3.64 0.71 73.72
对数 y=-5.06 -2.70 log(x) 0.71 72.28
二次 y=2.90 -11.29 x -62.42 x2 0.71 35.74
y=0.01 x -3.16 0.74 83.47
S型 y=exp( -3.21 + 0.33 / x ) 0.71 74.64
指数 y=21.86 exp( -29.47 x ) 0.76 92.61
702 线性 y=-20.65x + 4.03 0.71 72.44
对数 y=-4.92-3.08 log(x) 0.70 70.83
二次 y=2.41 + 0.96 x-71.88 x2 0.71 35.30
y=0.01 x -3.60 0.73 82.38
S型 y=exp( -3.66 + 0.53 / x ) 0.71 74.51
指数 y=34.34 exp( -24.40 x ) 0.75 90.53
760 线性 y=14.15x -4.65 0.48 27.22
对数 y=6.16+ 5.62 log(x) 0.47 27.06
二次 y=8.14 -50.19 x + 80.82 x2 0.48 13.33
y=261.23 x 6.10 0.43 22.26
S型 y=exp( 6.04-2.43 / x ) 0.43 22.30
指数 y=0.02 exp( 15.33 x ) 0.43 22.19
867 线性 y=14.61 x -3.86 0.43 22.83
对数 y=6.33 + 4.83 log(x) 0.43 22.81
二次 y=-3.26+ 11.02 x + 5.43 x2 0.43 11.03
y=342.02 x 5.33 0.40 19.90
S型 y=exp( 5.28-1.76 / x ) 0.40 20.10
指数 y=0.01 exp( 16.07 x ) 0.40 19.80
表6  各单一特征波段构建的LAI反演模型及评定系数
模型名称 NDVI SR RNDVI mNDVI EVI2 OSAVI
线性 0.73 0.74 0.72 0.73 0.72 0.73
对数 0.73 0.74 0.72 0.73 0.72 0.73
二次 0.73 0.74 0.72 0.74 0.72 0.73
0.76 0.75 0.75 0.78 0.72 0.74
指数 0.74 0.73 0.74 0.76 0.71 0.73
S型 0.77 0.76 0.76 0.78 0.73 0.75
表7  基于植被指数拟合模型相性关系数
植被指数 模型名称 拟合模型 决定系数(R 2 F值
NDVI S型 y=exp( 4.18-2.42 / x ) 0.77 98.40
RVI S型 y=exp( 2.45-9.81 / x ) 0.73 80.52
rNDVI S型 y=exp( 2.24-0.85 / x ) 0.76 97.19
mNDVI S型 y=exp( 2.76-1.77 /x) 0.78 108.90
EVI2 S型 y=exp( 3.48 -1.55 / x ) 0.76 93.40
OSAVI S型 y=exp( 4.10-2.08 / x ) 0.75 90.78
表8  各植被指数构建的LAI反演最优模型及评定系数
图6  特征波段反演模型精度验证
图7  植被指数反演模型精度验证
1 Tian J Y , Wang L , Li X J ,et al . Comparison of UAV and WorldView-2 Imagery for Mapping Leaf area Index of Mangrove Forest[J]. International Journal of Applied Earth Observations and Geoinformation,2017,61:22-31.
2 Gregory P A , Jonathan M O , Scurlock J A ,et al . Global Synthesis of Leaf Area Index Observations: Implications for Ecological and Remote Sensing Studies[J]. Global Ecology and Biogeography,2003,12(3):191-205.
3 Myneni R B , Hoffman S , Knyazikhin Y , et al . Global Products of Vegetation Leaf Area and Fraction Absorbed PAR from Year One of MODIS Data[J]. Remote Sensing of Environment,2002,83:214-231.
4 Gitelson A A , Viña A , Timothy J ,et al . Rundquist,Galina Keydan,Bryan Leavitt. Remote Estimation of Leaf Area Index and Green Leaf Biomass in Maize Canopies[J]. Geophysical Research Letters,2003,30(5):52-56.
5 Groom Q J , Godefroid S , Lockton A J . Validation and Intercomparison of Global Leaf Area Index Products Derived from Remote Sensing Data[J]. Journal of Geophysical Research Biogeosciences, 2015, 113(G2): doi:10.1029/2007JG000635 .
doi: 10.1029/2007JG000635
6 Hosseini M , Mcnairn H , Merzouki A , et al . Estimation of Leaf Area Index (LAI) in Corn and Soybeans Using Multi-polarization C- and L-band Radar Data[J]. Remote Sensing of Environment, 2015, 170:77-89.
7 Liu Z , Chen J M , Jin G , et al .Estimating Seasonal Vriations of Leaf Area Index Using Litterfall Collection and Optical Methods in Four Mixed Evergreen–Deciduous Forests[J]. Agricultural and Forest Meteorology, 2015, 209-210:36-48.
8 Carlson T N , Ripley D A . On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index[J]. Remote Sensing of Environment, 1997, 62(3):241-252.
9 Alonzo M , Bookhagen B , Mcfadden J P , et al . Mapping Urban Forest Leaf Area Index with Airborne LiDAR Using Penetration Metrics and Allometry[J]. Remote Sensing of Environment, 2015, 162:141-153.
10 Manninen T , Korhonen L , Voipio P , et al . Airborne Estimation of Boreal Forest LAI in Winter Conditions: A Test Using Summer and Winter Ground Truth[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1):68-74.
11 Bhardwaj A , Sam L , Akanksha A , et al . UAVs as Remote Sensing Platform in Glaciology : Present Applications and Future Prospects[J]. Remote Sensing of Environment, 2016, 175:196-204.
12 Chen Pengfei , Li Gang , Shi Yajiao ,et al . Verification of A UAV Hyperspectral Sensor and Its Application in Leaf Area Index Inversion of Maize[J]. Scientia Agricultura Sinica,2018,51(8):1464-1474.
12 陈鹏飞,李刚,石雅娇,徐志涛,杨粉团,曹庆军 .一款无人机高光谱传感器的验证及其在玉米叶面积指数反演中的应用[J].中国农业科学,2018,51(8):1464-1474.
13 Pan Haizhu , Chen zhongxin . Application of UAV Hyperspectral Remote Sensing Data in the Inversion of Winter Wheat Leaf Area Index[J].China Agricultural Resources and Regionalization, 2018, 39 (3): 32-37.
13 潘海珠,陈仲新 .无人机高光谱遥感数据在冬小麦叶面积指数反演中的应用[J].中国农业资源与区划,2018,39(3):32-37.
14 Li Jianjian , Zhu Xiaohua , Ma Lingling ,et al . LAI Inversion and Scale Effect Analysis of Multi-type Mixed Crops based on Hyperspectral Hyperspectral Data[J].Remote Sensing Technology and Application, 2017, 32(3): 427-434[
14 李剑剑,朱小华,马灵玲 等 .基于无人机高光谱数据的多类型混合作物LAI反演及尺度效应分析[J].遥感技术与应用,2017,32(3):427-434.]
15 Tu Yexin . Tea Tree Classification and Biochemical Parameter Inversion based on Near-earth Hyperspectral Remote Sensing Data[D].Wuhan: Wuhan University,2017.
15 涂晔昕 . 基于近地高光谱遥感数据的茶树分类和生化参数反演[D].武汉:武汉大学,2017.
16 Walczykowski P , Siok K , Jenerowicz A . Methodology for Determining Optimal Exposure Parameters of a Hyperspectral Scanning Sensor[J]. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B 1:1065-1069.
17 Deng Shubin , Chen Qiujin , Du Huijian , et al . Remote Sensing Image Processing Method Second Edition [M].Beijing: Advanced Education Press,2014
17 邓书斌,陈秋锦,杜会建,等 . ENVI遥感图像处理方法 第二版[M].北京:高等教育出版社,2014.
18 Tan Yibo , Zhao Zhonghui . Main Method for the Determination of Leaf Area Index [J]. Forest Inventory and Planning , 2008, 33(3):45-48.
18 谭一波, 赵仲辉 . 叶面积指数的主要测定方法[J]. 林业调查规划, 2008, 33(3):45-48.
19 Jacquemoud S , Baret F . PROSPECT: A Model of Leaf Optical Properties Spectra.[J]. Remote Sensing of Environment, 1990, 34(2):75-91.
20 Cho M A , Skidmore A K , Atzberger C . Towards Red-edge Positions Less Sensitive to Canopy Biophysical Parameters for Leaf Chlorophyll Estimation Using Properties Optique Spectrales Des Feuilles (PROSPECT) and Scattering by Arbitrarily Inclined Leaves (SAILH) Simulated Data[J]. International Journal of Remote Sensing, 2008, 29(8):2241-2255.
21 Liang Liang , Yang Minhua , Zhang Lianpeng . Hyperspectral Inversion of Wheat Leaf Area Index[J]. Spectroscopy and Spectral Analysis,2011,31(6):1658-1662.
21 梁亮, 杨敏华, 张连蓬 . 小麦叶面积指数的高光谱反演[J]. 光谱学与光谱分析, 2011, 31(6).1658-1662
22 Carlson T N , Ripley D A . On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index[J]. Remote Sensing of Environment, 1997, 62(3):241-252.
23 Anderson G L , Hanson J D , Haas R H . Evaluating Landsat Thematic Mapper Derived Vegetation Indices for Estimating above Ground Biomass on Semiarid Rangelands[J]. Remote Sensing of Environment, 1993, 45(2):165-175.
24 Prasad B , Carver B F , Stone M L , et al . Potential Use of Spectral Reflectance Indices as a Selection Tool for Grain Yield in Winter Wheat under Great Plains Conditions[J]. Crop Science, 2007, 47(4):1426-1440.
25 Jurgens C . The Modified Normalized Dfference Vegetation Index (mNDVI) A New Index to Determine Frost Damages in Agriculture based on Landsat TM Data[J]. International Journal of Remote Sensing, 1997, 18(17):3583-3594.
26 Mondal P . Quantifying Surface Gradients with A 2-band Enhanced Vegetation Index (EVI2)[J]. Ecological Indicators, 2011, 11(3):920-924.
27 Steven M D . The Sensitivity of the OSAVI Vegetation Index to Observational Parameters.[J]. Remote Sensing of Environment, 1998, 63(1):49-60.
[1] 林志玮,丁启禄,黄嘉航,涂伟豪,胡典,刘金福. 基于DenseNet的无人机光学图像树种分类研究[J]. 遥感技术与应用, 2019, 34(4): 704-711.
[2] 周龙飞,张云鹤,成枢,顾晓鹤,杨贵军,孙乾,束美艳. 不同生育期倒伏胁迫下玉米叶面积指数高光谱响应解析[J]. 遥感技术与应用, 2019, 34(4): 766-774.
[3] 王思恒, 黄长平, 张立福, 高显连, 付安民. 陆地生态系统碳监测卫星远红波段叶绿素荧光反演算法设计[J]. 遥感技术与应用, 2019, 34(3): 476-487.
[4] 李琼琼, 柳云龙. 城市居民区土壤重金属含量高光谱反演研究[J]. 遥感技术与应用, 2019, 34(3): 540-546.
[5] 田慧慧, 冯 莉, 赵璊璊, 郭 松, 董继伟. 无人机热红外城市地表温度精细特征研究 [J]. 遥感技术与应用, 2019, 34(3): 553-563.
[6] 云增鑫, 郑光, 马利霞, 王晓菲, 卢晓曼, 路璐. 联合主被动遥感数据定量评价林下植被对叶面积指数估算的影响[J]. 遥感技术与应用, 2019, 34(3): 583-594.
[7] 林沂, 张萌丹, 张立福, 江淼. 高光谱激光雷达谱位合一的角度效应分析[J]. 遥感技术与应用, 2019, 34(2): 225-231.
[8] 李伟, 唐伶俐, 吴昊昊, 腾格尔, 周梅. 轻小型无人机载激光雷达系统研制及电力巡线应用[J]. 遥感技术与应用, 2019, 34(2): 269-274.
[9] 丁海宁. 黄土高原土壤铁元素含量遥感反演方法 [J]. 遥感技术与应用, 2019, 34(2): 275-283.
[10] 牟多铎, 刘磊. ELM与SVM在高光谱遥感图像监督分类中的比较研究[J]. 遥感技术与应用, 2019, 34(1): 115-124.
[11] 皋厦, 申鑫, 代劲松, 曹林. 结合LiDAR单木分割和高光谱特征提取的城市森林树种分类[J]. 遥感技术与应用, 2018, 33(6): 1073-1083.
[12] 陈伟民,张凌,宋冬梅,王斌,丁亚雄,许明明,崔建勇. 基于AdaBoost改进随机森林的高光谱图像地物分类方法研究[J]. 遥感技术与应用, 2018, 33(4): 612-620.
[13] 赵云,谢东海,邓磊,闫亚男,李博旭. 利用多角度影像计算BRDF的方法与系统实现[J]. 遥感技术与应用, 2018, 33(4): 741-749.
[14] 苏阳,祁元,王建华,徐菲楠,张金龙. 基于航空高光谱影像的额济纳绿洲土地覆被提取[J]. 遥感技术与应用, 2018, 33(2): 202-211.
[15] 秦振涛,杨茹,张靖,杨武年. 基于聚类结构自适应稀疏表示的高光谱遥感图像修复研究[J]. 遥感技术与应用, 2018, 33(2): 212-215.