Vegetation canopy water content (CWC) is an important parameter for monitoring natural and agricultural ecosystems.
Previous studies focused on the observation of annual or monthly variations in CWC but lacked temporal details to study
vegetation physiological activities within a diurnal cycle. This study provides an evaluation of detecting vegetation
diurnal water stress using airborne data acquired with the MASTER instrument. Concurrent with the morning and
afternoon acquisitions of MASTER data, an extensive field campaign was conducted over almond and pistachio orchards
in southern San Joaquin Valley of California to collect CWC measurements. Statistical analysis of the field
measurements indicated a significant decrease of CWC from morning to afternoon. Field measured CWC was linearly
correlated to the normalized difference infrared index (NDII) calculated with atmospherically corrected MASTER
reflectance data using either FLAASH or empirical line (EL). Our regression analysis demonstrated that both
atmospheric corrections led to a root mean square error (RMSE) of approximately 0.035 kg/m2 for the estimation of
CWC (R2=0.42 for FLAASH images and R2=0.45 for EL images). Remote detection of the subtle decline in CWC awaits
an improved prediction of CWC. Diurnal CWC maps revealed the spatial patterns of vegetation water status in response
to variations in irrigation treatment.
The light absorbed by water in soil and plants is readily determined using hyperspectral full-range imagery and field
spectrometers. The full absorption of light can be accounted for by fitting the shape of water absorptions at the same
time as other diagnostic bands using multiple Gaussian functions. This research is particularly important in soils due to
the loss of mineral band depth with the spread of the fundamental water just beyond the SWIR. The relationship of the
albedo lost to band depth, for the same mineral media, is nonlinear. By including water and mineral absorptions in the
same fitting, the accuracy of the mineral abundance estimates are shown substantially improved. In addition,
measurements of absorption change within the soil surface are so sensitive to water content that these bands as indexes
and absorption fitting are excellent predictors of the amount of organic matter. Spectral model is shown for determining
water content based water indexes and the fitted SWIR band as a good predictors of soil biological crust, such as lichen
and cyanobacteria, in hyperarid soils of the Mojave Desert.
Precision agriculture requires high spectral and spatial resolution imagery for advanced analyses of crop and soil
conditions to increase environmental protection and producers' sustainability. GIS models that anticipate crop responses
to nutrients, water, and pesticides require high spatial detail to generate application prescription maps. While the added
precision of geo-spatial interpolation to field scouting generates improved zone maps and are an improvement over
field-wide applications, it is limited in detail due to expense, and lacks the high precision required for pixel level
applications. Multi-spectral imagery gives the spatial detail required, but broad band indexes are not sensitive to many
variables in the crop and soil environment. Hyperspectral imagery provides both the spatial detail of airborne imagery
and spectral resolution for spectroscopic and narrow band analysis techniques developed over recent decades in the laboratory that will advance precise determination of water and bio-physical properties of crops and soils.
For several years, we have conducted remote sensing investigations to improve cotton production through field
spectrometer measurements, and plant and soil samples in commercial fields and crop trials. We have developed
spectral analyses techniques for plant and soil conditions through determination of crop water status, effectiveness of
pre-harvest defoliant applications, and soil characterizations. We present the most promising of these spectroscopic
absorption and narrow band index techniques, and their application to airborne hyperspectral imagery in mapping the
variability in crops and soils.
In water limited environments, the density and water content of plant canopies are highly correlated to available soil moisture. Specific absorption bands for liquid water are identifiable and the variation in their depths can be related to canopy water content using high spectral resolution (hyperspectral) imagery. The spectral absorption feature centered at approximately 980 nm has been widely utilized for estimating equivalent water thickness, a measure of the volume of canopy water if it is equally distributed over the area of the pixel. Although it is affected by canopy structure, it is highly correlated with plant water content, and is independent of reflectance changes due to photosynthetic pigments. This study relates the depth of the 980 nm water band absorption, measured by the continuum removal (CR) technique, to crop water stress, and compares these results to other vegetation and plant stress indicators, NDVI and NDWI.