Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region.
Roy, S.K., Rowlandson, T.L., Berg, A.A., Champagne, C., and Adams, J.R. (2016). "Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region.", International Journal of Applied Earth Observation and Geoinformation, 45(Part B), pp. 212-220. doi : 10.1016/j.jag.2015.10.003 Access to full text
Knowledge of sub-pixel heterogeneity, particularly at the passive microwave scale, can improve the brightness temperature (and ultimately the soil moisture) estimation. However, the impact of surface heterogeneity (in terms of soil moisture, soil temperature and vegetation water content) on brightness temperature in an agricultural setting is relatively unknown. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) provided an opportunity to evaluate sub-pixel heterogeneity at the scale of a Soil Moisture Ocean Salinity (SMOS) or the Soil Moisture Active Passive (SMAP) radiometer footprint using field measured data. The first objective of this study was to determine if accounting for surface heterogeneity reduced the error between estimated brightness temperature (Tb) and Tb measured by SMOS. It was found that when accounting for variation in surface soil moisture, temperature and vegetation water content within the pixel footprint, the error between the modelled Tb and the measured Tbwas less than if a homogeneous pixel were modelled. The correlation between the surface parameters and the error associated with not accounting for surface heterogeneity were investigated. It was found that there was low to moderate correlation between the error and the coefficient of variance associated with the measured soil moisture, soil temperature and vegetation volumetric water content during the field campaign. However, it was found that the correlations changed depending on the stage of vegetation growth and the amount of time following a precipitation event. At the start of the field campaign (following a precipitation event), there was strong correlation between the error and all three surface parameters (r ≥ 0.75). Following a precipitation event close to the middle of the field campaign (during which there was rapid growth in vegetation), there was strong correlation between the error and the variability in vegetation water content (r = 0.89), moderate correlation with soil moisture (r = 0.61) and low correlation with soil temperature (r = 0.26). Highlights: • Investigates influence of sub-pixel heterogeneity in agricultural region. • Accounting for sub-pixel heterogeneity in better modelled Tb. • Tb errors correlated with CV in soil moisture, temperature, vegetation water. • Strength correlations with CV varied through growing season.
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