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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #312238
Title: Optical sensing of vegetation water content: A synthesis study

Author
item GAO, Y. - Monash University
item WALKER, J. - Monash University
item ALLAHMORADI, M. - University Of Melbourne
item MONERRIS, A. - Monash University
item RYU, D. - University Of Melbourne
item Jackson, Thomas

Submitted to: IEEE Journal of Selected Topics in Applied Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2015
Publication Date: 10/1/2015
Publication URL: http://handle.nal.usda.gov/10113/61476
Citation: Gao, Y., Walker, J., Allahmoradi, M., Monerris, A., Ryu, D., Jackson, T.J. 2015. Optical sensing of vegetation water content: A synthesis study. IEEE Journal of Selected Topics in Applied Remote Sensing. 8:1456-1464.

Interpretive Summary: New and more robust equations were developed for estimating vegetation water content (VWC) from satellite indices that use optical remote sensing by synthesizing studies conducted over a variety of land cover types. VWC is an important variable in climatic, agricultural and forestry applications. In passive microwave remote sensing, VWC plays an important role in soil moisture retrieval by parameterizing the effects of vegetation on the observed land surface emission. The set of recommended equations will be of value in soil moisture remote sensing using current and future satellites that provide information for agricultural weather and climate forecasts.

Technical Abstract: Vegetation Water Content (VWC) plays an important role in parameterizing the vegetation influence on microwave soil moisture retrieval. During the past decade, researchers have developed relationships between VWC and vegetation indices available from satellite optical sensors in order to create large-scale VWC maps based on these relationships. Among existing vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) have been most frequently used as input to estimate VWC. This work compares the performance of a number of models developed for VWC derivation from NDVI or NDWI, using satellite data and ground samples collected from field campaigns carried out in the U.S., Australia and China. Four vegetation types are considered: a) corn; b) cereal grains; c) legumes and d) grassland. While the results show similarities among the models, developed independently from different field campaigns for the same type of vegetation, there are also some significant differences. NDWI1640 and NDVI are found to be the two preferred vegetation indices for VWC estimation based on the error statistics and the availability of historical data sets used in this study. An equation to estimate VWC is recommended for each vegetation category based on the extended data set compiled from the different campaigns.