Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (4) : 179-184     DOI: 10.6046/gtzyyg.2017.04.27
Inversion of leaf area index in Heihe Oasis based on CASI data
YANG Yuwei1, DAI Xiaoai1,2, NIU Yutian1, LIU Hanhu1, YANG Xiaoxia1, LAN Yan1
1. Academic of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China;
2. Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resources of China, Chengdu 610059, China
Download: PDF(3589 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  As the vegetation canopy’s important parameter, the leaf area index (LAI) has important significance for crop growth monitoring and yield estimation. In this study, the authors used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye Oasis experimental area in Heihe River Basin as the experiment object and relied on physical and statistical model to estimate the inversion of the LAI. The process is as follows: First, the optimal linear regression model is established by using the normalized difference vegetation index (NDVI) and the corresponding measured LAI data. Then the physical model is adopted based on the combination of the mixed pixel decomposition model and the multiple scattering vegetation canopy model. With the linear regression model as the reference, the multiple scattering vegetation canopy model is modified, and the semi-empirical LAI inversion model is constructed. Finally, the fitting effects of the models are compared with each other. The results show that the semi-empirical model is the best model for LAI inversion in oasis area and its estimation accuracy of R2 increases significantly to 0.89. This study provides technical support for the estimation of crop leaf area index in high precision, and will further promote the study and application of quantitative remote sensing theory about precision agriculture.
Keywords GF-2 satellite image      Beidou satellite navigation system      GPS      ortho-rectification      accuracy validation      rational function model     
:  TP751.1  
Issue Date: 04 December 2017
E-mail this article
E-mail Alert
Articles by authors
HE Guojin
LONG Tengfei
YIN Ranyu
SONG Xiaolu
YUAN Yiqin
LING Saiguang
Cite this article:   
JINAG Wei,HE Guojin,LONG Tengfei, et al. Inversion of leaf area index in Heihe Oasis based on CASI data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 179-184.
URL:     OR
[1] 刘 洋,刘荣高,陈镜明,等.叶面积指数遥感反演研究进展与展望[J].地球信息科学学报,2013,15(5):734-743.
Liu Y,Liu R G,Chen J M,et al.Current status and perspectives of leaf area index retrieval from optical remote sensing data[J].Journal of Geo-Information Science,2013,15(5):734-743.
[2] 任 哲,陈怀亮,王连喜,等.利用交叉验证的小麦LAI反演模型研究[J].国土资源遥感,2015,27(4):34-40.doi:10.6046/gtzyyg.2015.04.06.
Ren Z,Chen H L,Wang L X,et al.Research on inversion model of wheat LAI using cross-validation[J].Remote Sensing for Land and Resources,2015,27(4):34-40.doi:10.6046/gtzyyg.2015.04.06.
[3] 陈 健,王文君,盛世杰,等.基于机载MASTER数据的果园叶面积指数遥感反演[J].国土资源遥感,2015,27(2):69-74.doi:10.6046/gtzyyg.2015.02.11.
Chen J,Wang W J,Sheng S J,et al.Leaf area index retrieval of orchards based on airborne MASTER data[J].Remote Sensing for Land and Resources,2015,27(2):69-74.doi:10.6046/gtzyyg.2015.02.11.
[4] Sellers P J,Randall D A,Collatz G J,et al.A revised land surface parameterization(SiB2) for atmospheric GCMS.Part I:Model formulation[J].Journal of Climate,1996,9(4):676-705.
[5] Herrmann I,Pimstein A,Karnieli A,et al.LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands[J].Remote Sensing of Environment,2011,115(8):2141-2151.
[6] Liang L,Di L P,Zhang L P,et al.Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method[J].Remote Sensing of Environment,2015,165:123-134.
[7] 包 刚,覃志豪,周 义,等.基于高光谱数据和RBF神经网络方法的草地叶面积指数反演[J].国土资源遥感,2012,24(2):7-11.doi:10.6046/gtzyyg.2012.02.02.
Bao G,Qin Z H,Zhou Y,et al.The application of Hyper-spectral data and RBF neural network method to retrieval of leaf area index of grassland[J].Remote Sensing for Land and Resources,2012,24(2):7-11.doi:10.6046/gtzyyg.2012.02.02.
[8] 李新辉,宋小宁,冷 佩.利用CHRIS/PROBA数据定量反演草地LAI方法研究[J].国土资源遥感,2011,23(3):61-66.doi:10.6046/gtzyyg.2011.03.11.
Li X H,Song X N,Leng P.A quantitative method for grassland LAI inversion based on CHRIS/PROBA data[J].Remote Sensing for Land and Resources,2011,23(3):61-66.doi:10.6046/gtzyyg.2011.03.11.
[9] 陈雪洋,蒙继华,杜 鑫,等.基于环境星CCD数据的冬小麦叶面积指数遥感监测模型研究[J].国土资源遥感,2010,22(2):55-58,62.doi:10.6046/gtzyyg.2010.02.12.
Chen X Y,Meng J H,Du X,et al.The monitoring of the winter wheat leaf area index based on HJ-1 CCD dada[J].Remote Sensing for Land and Resources,2010,22(2):55-58,62.doi:10.6046/gtzyyg.2010.02.12.
[10] Fernandes R A,Miller J R,Chen J M,et al.Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery[J].Remote Sensing of Environment,2004,89(2):200-216.
[11] Fernandes R,Miller J R,Hu B,et al.A multi-scale approach to mapping effective leaf area index in boreal picea Mariana stands using high spatial resolution CASI imagery[J].International Journal of Remote Sensing,2002,23(18):3547-3568.
[12] 唐建民,廖钦洪,刘奕清,等.基于CASI高光谱数据的作物叶面积指数估算[J].光谱学与光谱分析,2015,35(5):1351-1356.
Tang J M,Liao Q H,Liu Y Q,et al.Estimating leaf area index of crops based on hyperspectral compact airborne spectrographic imager(CASI) data[J].Spectroscopy and Spectral Analysis,2015,35(5):1351-1356.
[13] Croft H,Chen J M,Zhang Y,et al.Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground,CASI,Landsat TM 5 and MERIS reflectance data[J].Remote Sensing of Environment,2013,133:128-140.
[14] 肖 青,闻建光.黑河生态水文遥感试验:可见光近红外高光谱航空遥感(2012年7月7日)[DB].黑河计划科学数据中心,2013,doi:10.3972/hiwater.011.2013.db.
Xiao Q,Wen J G.HiWATER:Visible and near-infrared hyperspectral radiometer(7 July,2012)[DB].Heihe Plan Science Data Center,2013,doi:10.3972/hiwater.011.2013.db.
[15] 赵 静,李 云,汪 艳,等.黑河生态水文遥感试验:黑河流域中游LAI2000测量LAI数据集[R].黑河计划科学数据中心,2013,doi:10.3972/hiwater.058.2013.db.
Zhao J,Li Y,Wang Y,et al.HiWATER:Dataset of Vegetation LAI Measured by LAI2000 in the Middle Reaches of the Heihe River Basin[R].Heihe Plan Science Data Center,2013,doi:10.3972/hiwater.058.2013.db.
[16] 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.
[17] 邓孺孺,田国良,柳钦火.基于多次散射的植被-土壤二向反射模型[J].遥感学报,2004,8(3):193-200.
Deng R R,Tian G L,Liu Q H.Bi-directional reflectance model of canopy and soil based on multi-scatterings[J].Journal of Remote Sensing,2004,8(3):193-200.
[1] LI Penglong, DING Yi, HU Yan, LUO Ding, DUAN Songjiang, SHU Wenqiang. A method for rapid UAV images mosaicking based on GPU parallel computing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 57-63.
[2] JINAG Wei, HE Guojin, LIU Huichan, LONG Tengfei, WANG Wei, ZHENG Shouzhu, MA Xiaoxiao. Research on China’s land image mosaicking and mapping technology based on GF-1 satellite WFV data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 190-196.
[3] JINAG Wei, HE Guojin, LONG Tengfei, YIN Ranyu, SONG Xiaolu, YUAN Yiqin, LING Saiguang. Ortho accuracy validation and analysis of GF-2 PAN imagery based on Beidou satellite navigation system and GPS[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 211-216.
[4] HAN Jie, XIE Yong, WU Guoxi, LIU Qiyue, GAO Hailiang, GUAN Xiaoguo. Geo-positioning accuracy analysis for domestic high-resolution satellite imagery[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 100-107.
[5] PAN Hongbo, ZOU Zhengrong, ZHANG Guo, ZHANG Yunsheng, WANG Taoyang. Block adjustment of high resolution satellite image using RFM with the same stripe constraint[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 46-52.
[6] YANG Baolin, LYU Tingting, WANG Shaojun, ZHANG Zhi. Ortho-rectification method for Pleiades satellite images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 25-29.
[7] MA Shibin, YANG Wenfang, ZHANG Kun. Study of key technology of SPOT6 satellite image processing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 30-35.
[8] CAI Guolin, SONG Xudong, ZHANG Aoli, YANG Jun. Development of the embedded spatial data acquisition system based on smart phones[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 182-187.
[9] YANG Qiyong, JIANG Zhongcheng, MA Zulu, SHEN Lina. Application of the GPS real navigation based on remote sensing image to geological survey[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 178-181.
[10] LIN Hao, FAN Jinghui, HONG Youtang, TU Pengfei, GUO Xiaofang. An analysis of height precision in applying single frequency static GPS to landslide monitoring[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 74-79.
[11] YANG Chengsheng, ZHANG Qin, ZHANG Shuangcheng, ZHAO Chaoying. Research on GPS water vapor interpolation by improved Kriging algorithm[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 39-43.
[12] WU Wei, WU Qian-hong, DENG Ji-qiu. Research on Primary Filtering Method for Pre-matching Road Sections Based on Four-level Grid Division[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 26-29.
[13] LIU Bin, SUN Xi-liang, DI Kai-chang, LIU Zhao-qin. Accuracy Analysis and Validation of ZY-3’s Sensor Corrected Products[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 36-40.
[14] LUO Yan-Fei, XU Pan-Lin, LI Ying-Cheng, DAI Hong-Lei. The Application of 3S Technology to Land Supervision[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 12-15.
[15] LIU Ping, YANG Liao, ZHU Chang-Ming, LI Bao-Ming. An Approach to the ADS40 POS Data Processing Method Based on Precise Point Positioning[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 41-44.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech