Existing vegetation radiative transfer models seldom consider the effects of rice panicles on canopy spectrum. An ear-leaf model was established by adding a rice ear module to 4SAIL-RowCrop model. The geometrical optical model of the rice panicle was developed by assuming that the rice panicle is an upright cylinder. The rice panicle component was considered to be part of the canopy, and seven rice canopy scene components were defined including canopy without ear, the first and second kind of sunlit soil, the first and second kind of shaded soil, sunlit ear, and shaded ear. Thus, the directional radiation of the spike layer was described using the geometrical optical model. Reflectance and transmittance of the rice canopy were calculated using the 4SAIL model. The reflection and radiation characteristics of the soil layer were modeled using 4SAIL-RowCrop. Then, the row-sowed rice canopy scene reflectance was simulated by iterating reflectivity of the seven components in proportion. The ear-leaf model has good simulation accuracy for forward and backward reflectance with the mean R2, relative root mean square error, and RE values between the simulated and measured spectra of typical bands of 0.91, 19%, and 10%, respectively. Thus, the ear-leaf model provides a more technical support for the simulation of canopy reflectance and retrieval of growth parameters in rice.
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.