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REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (4) : 34-40     DOI: 10.6046/gtzyyg.2015.04.06
Technology and Methodology |
Research on inversion model of wheat LAI using cross-validation
REN Zhe1,2, CHEN Huailiang3, WANG Lianxi1,2, LI Ying3, LI Qi1,2
1. Jiangsu Key Laboratory of Atmospheric Environmental Monitoring and Pollution Control, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. School of Environmental Science and Engineering of Nanjing University of Information Science and Technology, Nanjing 210044, China;
3. CMA/Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China
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Abstract  

Leaf area index (LAI) is the key parameter to signify the growth condition and canopy structure of vegetation. Inversion of LAI using remote sensing technology is always one of the hotspots and difficulties in the field of remote sensing. In this paper, the first and second order derivatives of hyperspectral data of wheat were calculated, and several vegetation indices (RVI, NDVI, EVI, DVI and MSAVI) and trilateral variable parameters were built for the analysis. The correlation analysis between the parameters and wheat LAI data was carried out, and the method of cross-validation was used for multiple regression analysis so as to determine the sensitive parameters for wheat inversion of LAI and choosing model type of inversion. At last, the inversion models of all the samples were built by using these sensitive parameters, and their imitative effects were comparatively studied. The results show that the majority of root mean square errors(RMSE)of the inverse models using cross-validation are larger than those of the models which do not use cross-validation. In addition, among all the models built by the sensitive parameters, the cubic regression model of RVI is the optimal model for inversion of wheat LAI with remote sensing data.

Keywords remote sensing      Asiha      Annage      line-ring structure      tone anomaly     
:  TP751.1  
Issue Date: 23 July 2015
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YU Xiaoxia
GAO Jianguo
PAN Yaru
WANG Ruixue
Cite this article:   
YU Xiaoxia,GAO Jianguo,PAN Yaru, et al. Research on inversion model of wheat LAI using cross-validation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 34-40.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.04.06     OR     https://www.gtzyyg.com/EN/Y2015/V27/I4/34

[1] Dobermann A, Pampolino M F.Indirect leaf area index measurement as a tool for characterizing rice growth at the field scale[J].Communications in Soil Science and Plant Analysis, 1995, 26(9/10):1507-1523.

[2] 黄敬峰, 王渊, 王福民, 等.油菜红边特征及其叶面积指数的高光谱估算模型[J].农业工程学报, 2006, 22(8):22-26. Huang J F, Wang Y, Wang F M, et al.Red edge characteristics and leaf area index estimation model using hyperspectral data for rape[J].Transactions of the CSAE, 2006, 22(8):22-26.

[3] Bunnik N J.The Multispectral Reflectance of Shortwave Radiation by Agricultural Crops in Relation with Their Morphological and Optical Properties[D]. Wageningen:Meded.Candbouwhoge School, 1978.

[4] 邢著荣, 冯幼贵, 李万明, 等.高光谱遥感叶面积指数(LAI)反演研究现状[J].测绘科学, 2010, 35(s1):162-164, 62. Xing Z R, Feng Y G, Li W M, et al.The research status of inversion of leaf area index with hyperspectral remote sensing[J].Science of Surveying and Mapping, 2010, 35(s1):162-164, 62.

[5] Yang F, Sun J L, Fang H L, et al.Comparison of different methods for corn LAI estimation over northeastern China[J].International Journal of Applied Earth Observation and Geoinformation, 2012, 18:462-471.

[6] Darvishzadeh R, Atzberger C, Skidmore A, et al.Mapping grassland leaf area index with airborne hyperspectral imagery:A comparison study of statistical approaches and inversion of radiative transfer models[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6):894-906.

[7] 杨贵军, 赵春江, 邢著荣, 等.基于PROBA/CHRIS遥感数据和PROSAIL模型的春小麦LAI反演[J].农业工程学报, 2011, 27(10):88-94. Yang G J, Zhao C J, Xing Z R, et al.LAI inversion of spring wheat based on PROBA/CHRIS hyperspectral Multi-angular data and PROSAIL mode[J].Transactions of the CSAE, 2011, 27(10):88-94.

[8] Yang G J, Zhao C J, Liu Q, et al.Inversion of a radiative transfer model for estimating forest LAI from multisource and multiangular optical remote sensing data[J].IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3):988-1000.

[9] Schlerf M, Atzberger C.Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data[J].Remote Sensing of Environment, 2006, 100(3):281-294.

[10] 陈雪洋, 蒙继华, 朱建军, 等.冬小麦叶面积指数的高光谱估算模型研究[J].测绘科学, 2012, 37(5):141-144. Chen X Y, Meng J H, Zhu J J, et al.Hyperspectral estimation models for leaf area index of winter wheat[J].Science of Surveying and Mapping, 2012, 37(5):141-144.

[11] Nguyen H T, Lee B W.Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression[J].European Journal of Agronomy, 2006, 24(4):349-356.

[12] Mauser W, Bach H. Imaging Spectroscopy in Hydrology and Agriculture Determination of Model Parameters[M].Dordrecht, Netherlands:Kluwer Academic Publishing, 1995:261-283.

[13] 刘东升, 李淑敏.北京地区冬小麦冠层光谱数据与叶面积指数统计关系研究[J].国土资源遥感, 2008, 20(4):32-34, 42.doi:10.6046/gtzyyg.2008.04.08. Liu D S, Li S M.Statistical relationship between LAI indices and canopy spectral data of winter wheat in Beijing area[J].Remote Sensing for Land and Resources, 2008, 20(4):32-34, 42.doi:10.6046/gtzyyg.2008.04.08.

[14] 侯学会, 牛铮, 黄妮, 等.小麦生物量和真实叶面积指数的高光谱遥感估算模型[J].国土资源遥感, 2012, 24(4):30-35.doi:10.6046/gtzyyg.2012.04.06. Hou X H, Niu Z, Huang N, et al.The hyperspectral remote sensing estimation models of total biomass and true LAI of wheat[J].Remote Sensing for Land and Resources, 2012, 24(4):30-35.doi:10.6046/gtzyyg.2012.04.06.

[15] 王秀珍, 黄敬峰, 李云梅, 等.水稻叶面积指数的高光谱遥感估算模型[J].遥感学报, 2004, 8(1):81-88. Wang X Z, Huang J F, Li Y M, et al.The study on hyperspectral remote sensing estimation models about LAI of rice[J].Journal of Remote Sensing, 2004, 8(1):81-88.

[16] Tsai F, Philpot W.Derivative analysis of hyperspectral data[J].Remote Sensing of Environment, 1998, 66(1):41-51.

[17] Jordan C F.Derivation of leaf-area index from quality of light on the forest floor[J].Ecology, 1969, 50(4):663-666.

[18] Rouse J W, Haas R H, Schell J A, et al.Monitoring vegetation systems in the Great Plains with ERTS[C]//Proceedings of Third ERTS Symposium.Greenbelt:NASA SP-351, 1973, 1:309-317.

[19] Liu H Q, Huete A.A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J].IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(2):457-465.

[20] Richardson A J, Wiegand C L.Distinguishing vegetation from soil background information[J].Photogrammetric Engineering and Remote Sensing, 1977, 43(12):1541-1552.

[21] Qi J, Chehbouni A, Huete A R, et al.A Modified soil adjusted vegetation index[J].Remote Sensing of Environment, 1994, 48(2):119-126.

[22] 王秀珍, 王人潮, 黄敬峰.微分光谱遥感及其在水稻农学参数测定上的应用研究[J].农业工程学报, 2002, 18(1):9-13. Wang X Z, Wang R C, Huang J F.Derivative spectrum remote sensing and Its application in measurement of rice agronomic parameters of rice[J].Transactions of the CSAE, 2002, 18(1):9-13.

[23] 谭倩, 赵永超, 童庆禧, 等.植被光谱维特征提取模型[J].遥感信息, 2001(1):14-18. Tan Q, Zhao Y C, Tong Q X, et al.Vegetation spectral feature extraction model[J].Remote Sensing Information, 2001(1):14-18.

[24] Kohavi R.A study of cross-validation and bootstrap for accuracy estimation and model selection[J].Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1995, 2(12):1137-1143.

[25] 范永东.模型选择中的交叉验证方法综述[D].太原:山西大学, 2013. Fan Y D.A Summary of Cross-Validation in Model Selection[D].Taiyuan:Shanxi University, 2013.

[26] 陈拉, 黄敬峰, 王秀珍.不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析[J].遥感学报, 2008, 12(1):143-151. Chen L, Huang J F, Wang X Z.Estimating accuracies and sensitivity analysis of regression models fitted by simulated vegetation indices of different sensors to rice LAI[J].Journal of Remote Sensing, 2008, 12(1):143-151.

[27] 权文婷, 王钊.冬小麦种植面积遥感提取方法研究[J].国土资源遥感, 2013, 25(4):8-15.doi:10.6046/gtzyyg.2013.04.02. Quan W T, Wang Z.Researches on the extraction of winter wheat planting area using remote sensing method[J].Remote Sensing for Land and Resources, 2013, 25(4):8-15.doi:10.6046/gtzyyg.2013.04.02.

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