Impact of sensor footprint on measurement of directional brightness temperature of row crop canopies

Abstract

A sensor's footprint determines the target that is observed by the sensor, and influences the angular features of the target's directional brightness temperature (DBT) at the field site. This paper describes a new radiative transfer model (FovMod) to simulate the DBT of the row crop canopy by considering the sensor's footprint in the ground measurements. The FovMod firstly divides the sensor's circular or elliptical footprint into a few small cells, and then estimates the components' fractions (e.g., leaves, sunlit soil and shaded soil) in each cell based on the gap probability theory. The canopy's DBT is finally obtained by weighting the components' brightness temperatures and their fractions using a Gaussian point spreading function (PSF) of the sensor's response. Simulation results indicate that a small footprint causes the distribution of the DBT to be strongly dominated by the row direction and a single component's temperature but little influenced by the solar position. On the contrary, a large footprint smoothes the row-space effect and causes the DBT to distribute as a uniform, continuous canopy. Comparison with a previous parallel model shows that if the diameter of the sensor's circular footprint extends to 1.5–2.0 times as large as the total width of the row crop canopy, the footprint effect is minimized, and the ground measured DBT can, theoretically, be used to evaluate the parallel model with negligible error. Finally, validations with a maize canopy demonstrated that the new model performed more accurately than the parallel model to simulate the DBT. Moreover, the FovMod also provides an opportunity to assess the measurement uncertainty caused by some unexpected changes in the sensor's footprint.