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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 #320903

Title: Estimation of leaf water content using spectral bands from the commercial satellite, DigitalGlobe WorldView-3

item Hunt, Earle - Ray
item Daughtry, Craig

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/2/2015
Publication Date: 12/23/2015
Citation: Hunt, E.R., Daughtry, C.S. 2015. Estimation of leaf water content using spectral bands from the commercial satellite, DigitalGlobe WorldView-3. International Journal of Remote Sensing APP:9/9/2015 SUBMIT:9/29/2015 PUB:12/23/2015 37:388-402, doi:10.1080/01431161.2015.1128575

Interpretive Summary: The content of liquid water in a vegetation canopy may be determined by satellite remote sensing to help detect plant water stress, determine fire danger rating, and help improve estimates of soil moisture content from microwave sensors. Usually, satellites have only a few bands in the shortwave-infrared region (roughly 1200 to 2500 nm wavelength) where liquid water absorbs radiation. DigitalGlobe, Inc. recently launched a new high-resolution commercial satellite, WorldView-3, which has eight shortwave-infrared bands that may be used for estimating water content. Furthermore, WorldView-3 has a second near-infrared band for which DigitalGlobe’s specifications indicate that it could also be used to estimate water content. This study used leaf reflectance spectra measured in the laboratory and simulations from a leaf-optics model to determine which of the nine WorldView-3 bands would be best for estimating leaf water content. The leaf reflectance spectra were divided into four groups: maize, trees, grasses, and broad-leaved herbaceous crops and weeds. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to differences in leaf water content; tree leaves had higher spectral index values which saturated at lower water contents. Selection of the best index for water content was problematic. Indices that were sensitive to small differences in water content also had large variability caused by differences in leaf structure, whereas indices that were relatively insensitive had small variability. Contrary to expectations, the relative spectral response of WorldView-3’s second near-infrared band, measured leaf data, and model simulations indicated that this band was not sensitive to water content. The high-spatial resolution and the numerous bands of WorldView-3 will help understand differences among plant types in water content, which affect interpretation of medium- and coarse-resolution satellite data from Landsat 8 and the Suomi National Polar-orbiting Partnership, respectively.

Technical Abstract: A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from four groups: maize, trees, grasses, and broadleaved herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength is1200 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860-1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.