Designing a spectral index to estimate vegetation water content from remote sensing data

Ceccato, Pietro; Flasse, S.; Grégoire, Jean‐Marie

doi:10.1016/s0034-4257(02)00036-6

Cited by 320 publications

(221 citation statements)

References 3 publications

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“…There was no significant difference between the two regression equations even though there were large changes in soil background reflectance and major differences in vegetation type. Furthermore, data from Hunt [7], Davidson et al [9], and Ceccato et al, [10] were included in the analysis, and there were no significant statistical differences among the different datasets in the regression between NDII and canopy EWT (Fig. 2).…”

Section: Resultsmentioning

confidence: 99%

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Remote Sensing of Canopy Water Content During SMEX'04 and SMEX'05 Using Shortwave-Infrared Reflectances

Hunt

Yılmaz

Jackson

2008

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium

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The Soil Moisture Experiments in 2004 and 2005 were conducted to validate algorithms for soil moisture retrievals. One of the key parameters for determination of soil moisture from microwave sensors is the vegetation water content of canopy and stems. We tested if canopy water content could be determined from reflectances in the shortwave-infrared and if the amount of canopy water content was related to the total vegetation water content by allometric equations. The normalized difference infrared index (NDII) was linearly related to canopy water content for all plants up to an equivalent water thickness of 1.0 mm. The biggest factor affecting the estimation of canopy water content was the soil background reflectance. For corn and soybean canopy equivalent water thickness were linearly related to total vegetation water content. However, there may be a separate allometric equation required for each vegetation type.

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Section: Resultsmentioning

confidence: 99%

“…TM band 5, ASTER band 5, MODIS band 6, and AWiFS band 4). Two studies have shown linear relationships between NDII and EWT [9,10], and NDII avoids some problems encountered using a ratio [6,7].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Canopy Water Content During SMEX'04 and SMEX'05 Using Shortwave-Infrared Reflectances

Hunt

Yılmaz

Jackson

2008

IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium

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“…The quantitative relationship between the GVMI and EWT is established according to Ceccato, Flasse, and Gregoire (2002) and via a fitting method and field measurement data formula is expressed as follows:…”

Section: The Retrieval Of Vegetation Water Content Information (Gvmimentioning

confidence: 99%

“…Later, to test on the actual application effect of the formula, Ceccato, Flasse, and Gregoire (2002) and found that significant differences exist between the formula calculation results and the field measurement data, but their regression lines were parallel. Ceccato thought that systematic errors in the sensor and measurement tools led to the generation of this error, and thus they introduced a constant into the formula to reduce the error.…”

Section: The Retrieval Of Vegetation Water Content Information (Gvmimentioning

confidence: 99%

“…Among these indices, the EWT is defined as the ratio of the vegetation water content and its total leaf area (Dawson, Curran, and Plummer 1998), and the EWT can be revised using the leaf area index (Ceccato et al 2001) to describe the vegetation water content more accurately. The FMC is affected by the dry matter in the leaves (Ceccato, Flasse, and Gregoire 2002;. Therefore, this article adopts the coupling of the EWT and the FMC for further study of the vegetation water content.…”

Section: Introductionmentioning

confidence: 99%

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The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing

Gao

Wang

Cao

et al. 2014

GIScience & Remote Sensing

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This article retrieved the vegetation water content equivalent water thickness (EWT) information and the relevant parameters for the land surface from full-band TM remote sensing data. The effects of surface water heat flux and surface covering on the EWT were analyzed via studies of the regional land cover status and the combined EWT with land surface parameters. This article also analyzed the roles and limitations of EWT in drought monitoring combined with classification of the regional drought and regional water stress index (RWSI). From the results, the following conclusions were reported. (1) The spatial distribution of the EWT is closely related to the vegetation, and the EWT is able to monitor the regional water conditions to a certain extent. (2) The distribution of the EWT is affected significantly by the density of vegetation cover, land surface temperature and evapo-transpiration. (3) The correlation between the NDVI (or fractional vegetation cover) and the EWT differs under different vegetation coverage conditions. (4) The evapo-transpiration of the ecological environment is closely tied to the EWT such that the changes in evapo-transpiration affect the EWT significantly. (5) The ability of the EWT to monitor regional drought is conditional, and therefore no significant indication exists that can be used to monitor moderate to severe drought conditions.

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Automatic Near‐Real‐Time Flood Mapping from Geostationary Low Earth Orbiting Satellite Observations

Sun

Goldberg

et al. 2021

Global Drought and Flood

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Designing a spectral index to estimate vegetation water content from remote sensing data

Cited by 320 publications

References 3 publications

Remote Sensing of Canopy Water Content During SMEX'04 and SMEX'05 Using Shortwave-Infrared Reflectances

Remote Sensing of Canopy Water Content During SMEX'04 and SMEX'05 Using Shortwave-Infrared Reflectances

The responses of vegetation water content (EWT) and assessment of drought monitoring along a coastal region using remote sensing

Automatic Near‐Real‐Time Flood Mapping from Geostationary Low Earth Orbiting Satellite Observations

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