Title :
Optical Sensing of Vegetation Water Content: A Synthesis Study
Author :
Ying Gao ; Walker, Jeffrey P. ; Allahmoradi, Mahdi ; Monerris, Alessandra ; Dongryeol Ryu ; Jackson, Thomas J.
Author_Institution :
Dept. of Civil Eng., Monash Univ., Clayton, VIC, Australia
Abstract :
Vegetation water content (VWC) plays an important role in parameterizing the vegetation influence on microwave soil moisture retrieval. During the past decade, relationships have been developed between VWC and vegetation indices 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 for estimating VWC. This work compiles and inter-compares a number of equations developed for VWC derivation from NDVI and NDWI using satellite data and ground samples collected from field campaigns carried out in the United States, Australia, and China. Four vegetation types are considered: corn, cereal grains, legumes, and grassland. While existing equations are reassessed against the entire compiled data sets, new equations are also developed based on the entire data sets. Comparing with existing equations, results show superiorities for the new equations based on statistical analysis against the entire data set. NDWI1640 and NDVI are found to be the preferred indices for VWC estimation based on the availability and the error statistics of the compiled data sets. It is recommended that the new equations can be applied in the future global remote sensing application for VWC map retrieval.
Keywords :
crops; error statistics; moisture; soil; vegetation mapping; Australia; China; NDVI; NDWI1640; United States; cereal grains; corn; error statistics; field campaigns; global remote sensing application; grassland; ground samples; large-scale vegetation water content map retrieval; legumes; microwave soil moisture retrieval; normalized difference vegetation index; normalized difference water index; optical sensing; satellite data; satellite optical sensors; statistical analysis; vegetation indices; vegetation types; vegetation water content derivation; vegetation water content estimation; Equations; Indexes; MODIS; Mathematical model; Remote sensing; Satellites; Vegetation mapping; Estimation; normalized difference vegetation index (NDVI); normalized difference water index (NDWI); vegetation water content (VWC);
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2015.2398034