A Comparatively Study of the Capabilities of Different Vegetation Water<br />
Indices in Monitoring Water Status of Wheat

doi:10.6046/gtzyyg.2010.03.20

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Abstract

Based on comparing the capabilities of different water indices in monitoring water status of wheat, the authors selected the best indices for different periods. Using the data of wheat spectra and water status observed in a whole growth season of wheat, the authors calculated five popular water indices, i.e., NDVI, NDWI, GVMI, PWI and WI, and conducted a correlation analysis between these indices and EWT (Equivalent Water Thickness) as well as FMC (Fuel Moisture Content). The Results show that, in the early period of wheat growth, FMC is better than EWT in reflecting water status of the wheat, whereas in the late period, EWT is more suitable. Different periods have different best water indices, and the correlation between indices and water status tends to experience an increase in early periods and decrease in later periods. In the application, therefore, we should choose different indices for different periods.

Keywords Zhao yuan city Remote sensing Environment ERDAS

TP 79

Issue Date: 20 September 2010

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WANG Pu, WU Jian-Jun, NIE Jian-Liang, KONG Fan-Ming, DING Hui-Yan, ZHAO Liu-Hui. A Comparatively Study of the Capabilities of Different Vegetation Water
Indices in Monitoring Water Status of Wheat[J]. REMOTE SENSING FOR LAND & RESOURCES,2010, 22(3): 97-100.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.03.20 OR https://www.gtzyyg.com/EN/Y2010/V22/I3/97

［1］张佳华,郭文娟,姚凤海.植被水分遥感监测模型的研究［J］.应用基础与工程科学学报, 2007(1):45-53.

［2］冯先伟,陈曦,包安明,等.水分胁迫条件下棉花生理变化及其高光谱响应分析［J］.干旱区地理，2004(2):250-255.

［3］薛利红,罗卫红,曹卫星,等.作物水分和氮素光谱诊断研究进展［J］.遥感学报, 2003(1):73-80.

［4］徐希儒. 遥感物理［M］.北京:北京大学出版社, 2005.

［5］Rouse J W, Haas R H, Schell J A, et al. Monitoring Vegetation Systems in the Great Plains with ERTS［C］∥Proceedings of Third Earth Resources Technology Satellite-1 Symposium. Greenbelt , 1974 (351):310-317.

［6］Penuelas J, Filella, Biel C, et al. The Reflectance at the 950~970 nm Region as an Indicator of Plant Water Status［J］. International Journal of Remote Sensing, 1993, 14(10):1887-1905.

［7］Penuelas J. Estimation of Plant Water Concentration by the Reflectance Water Index WI (R900R970) ［J］. International Journal of Remote Sensing, 1997, 18:2869-2872.

［8］Gao. NDWI——A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space［J］. Remote Sensing of Environment, 1996, 58:257-266.

［9］Ceccato P, Gobron N, Flasse S, et al. Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data:Part 1.Theoretical Approach［J］. Remote Sensing of Environment, 2002, 82:188-197.

［10］田永超,曹卫星,姜东,等.不同水氮条件下水稻冠层反射光谱与植株含水率的定量关系［J］.植物生态学报,2005, 29（2）:318-323.

［11］Ceccato P，Gobron N，Flasse S, et al. Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data:Part 2. Validation and Applications［J］.Remote Sensing of Environment,2002，82：198-207.

［12］田永超,朱艳,曹卫星,等.小麦冠层反射光谱与植株水分状况的关系［J］.应用生态学报,2004(11):2072-2076.

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