Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

文献类型： 外文期刊

第一作者： Xie, Qiaoyun

作者： Xie, Qiaoyun;Huang, Wenjiang;Xie, Qiaoyun;Dash, Jadunandan;Song, Xiaoyu;Wang, Renhong;Huang, Linsheng;Zhao, Jinling

作者机构：

关键词： Leaf area index;Hyperspectral remote sensing;Vegetation index;Nitrogen and water treatment

期刊名称：ADVANCES IN SPACE RESEARCH （ 影响因子：2.152； 五年影响因子：1.978 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices. (C) 2015 COSPAR. Published by Elsevier Ltd. All rights reserved.

分类号： V1

  • 相关文献

[1]Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data. Kong, Weiping,Huang, Wenjiang,Zhou, Xianfeng,Kong, Weiping,Zhou, Xianfeng,Song, Xiaoyu,Casa, Raffaele. 2016

[2]Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method. Liang, Liang,Zhang, Lianpeng,Lin, Hui,Liang, Liang,Zhao, Shuhe,Liang, Liang,Di, Liping,Deng, Meixia,Qin, Zhihao.

[3]ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL. Liao, Qinhong,Zhao, Chunjiang,Yang, Guijun,Wang, Jihua,Zhang, Dongyan,Liao, Qinhong,Zhang, Dongyan,Coburn, Craig,Wang, Zhijie. 2013

[4]Estimation of Grain Protein Content in Winter Wheat by Using Three Methods with Hyperspectral Data. Xiu-liang Jin,Wang, Ji-hua,Xiu-liang Jin,Xin-gang Xu,Hai-kuan Feng,Xiao-yu Song,Qian Wang,Xiu-liang Jin,Xin-gang Xu,Hai-kuan Feng,Xiao-yu Song,Qian Wang,Xiu-liang Jin,Wang, Ji-hua,Guo, Wen-shan. 2014

[5]Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Bareth, Georg,Lenz-Wiedemann, Victoria I. S.,Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Li, Fei,Lenz-Wiedemann, Victoria I. S.,Chen, Xinping,Gnyp, Martin L.,Li, Fei,Miao, Yuxin,Jia, Liangliang,Koppe, Wolfgang,Hennig, Simon D.,Jia, Liangliang,Chen, Xinping,Zhang, Fusuo,Laudien, Rainer. 2014

[6]Mapping paddy biomass with multiple vegetation indexes by using multispectral remotely sensed image. Gu, Xiaohe,Wang, Yancang,Song, Xiaoyu,Xu, Xingang. 2016

[7]Assessment of Aerial Agrichemical Spraying Effect Using Moderate-Resolution Satellite Imagery. Zhang Dong-Yan,Wang Xiu,Chen Li-ping,Ma Wei,Zhang Dong-Yan,Zhang Dong-Yan,Wang Xiu,Chen Li-ping,Li Bin,Ma Wei,Lan Yu-bin,Lan Yu-bin,Zhou Xin-gen. 2016

[8]REMOTE SENSING OF THE SEASONAL NAKED CROPLANDS USING SERIES OF NDVI IMAGES AND PHENOLOGICAL FEATURE. Shan, Zhengying,Xu, Qingyen. 2013

[9]Assessment of Chlorophyll Content Using a New Vegetation Index Based on Multi-Angular Hyperspectral Image Data. Liao Qin-hong,Zhang Dong-yan,Liao Qin-hong,Zhang Dong-yan,Wang Ji-hua,Yang Gui-jun,Yang Hao,Liao Qin-hong,Zhang Dong-yan,Wang Da-cheng,Craig, Coburn,Wong Zhijie. 2014

[10]Crop growth condition monitoring and analyzing in county scale by time series MODIS medium-resolution data. Yu, Kun,Wang, Zhiming,Sun, Ling,Shan, Jie,Mao, Liangjun. 2013

[11]Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize. Zhao, Ben,Duan, Aiwang,Liu, Zhandong,Chen, Zhifang,Zhang, Jiyang,Xiao, Junfu,Liu, Zugui,Qin, Anzhen,Ning, Dongfeng,Ata-Ul-Karim, Syed Tahir,Gong, Zhihong. 2018

[12]Spectral Reflectance and Vegetation Index Changes in Deciduous Forest Foliage Following Tree Removal: Potential for Deforestation Monitoring. Peng, D.,Hu, Y.,Li, Z..

[13]Comparison of two methods for monitoring leaf total chlorophyll content (LTCC) of wheat using field spectrometer data. Jin, X.,Wang, K.,Li, S.,Jin, X.,Diao, W.,Xiao, C.,Wang, K.,Li, S.,Wang, F.,Chen, B..

[14]Effects of vegetation indices to the spatial changes of desert environment using EOS/MODIS data: A case study to Sangong inland arid ecosystem. Lu, Liping,Qin, Zhihao,Qin, Zhihao,Gao, Maofang,Zhao, Chengyi,Li, Wenjuan. 2006

[15]Comparison of vegetation indices and red-edge parameters for estimating grassland cover from canopy reflectance data. Liu, Zhan-Yu,Huang, Jing-Feng,Wu, Xin-Hong,Dong, Yong-Ping. 2007

[16]New fast detection method of forest fire monitoring and application based on FY-1D/MVISR data. Feng, Jianzhong,Tang, Huajun,Zhou, Qingbo,Chen, Zhongxin,Bai, Linyan,Feng, Jianzhong. 2008

[17]Forecasting of Powdery Mildew disease with multi-sources of remote sensing information. Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yuan, Lin. 2014

[18]Validation of two Huanjing-1A/B satellite-based FAO-56 models for estimating winter wheat crop evapotranspiration during mid-season. Jin, Xiuliang,Yang, Guijun,Xue, Xuzhang,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Jin, Xiuliang,Yang, Guijun,Xue, Xuzhang,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Jin, Xiuliang,Yang, Guijun,Xue, Xuzhang,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Jin, Xiuliang.

[19]Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties. Tao, FL,Yokozawa, M,Zhang, Z,Xu, YL,Hayashi, Y.

[20]Hyperspectral Discrimination and Response Characteristics of Stressed Rice Leaves Caused by Rice Leaf Folder. Liu, Zhanyu,Ding, Xiaodong,Zhou, Bin,Liu, Zhanyu,Cheng, Jia-an,Huang, Wenjiang,Li, Cunjun,Xu, Xingang,Shi, Jingjing. 2012

作者其他论文 更多>>

微信小程序