Root growth dynamics of rice (Oryza saliva L.) under submerged soil conditions are poorly understood. Two field experiments were conducted in 1986 to compare root growth of rice at two locations on different soils. The rice cultivar Bond was seeded in leveed plots on a Captina silt loam (fine‐silty, siliceous, mesic Typic Fragiudults) at Fayetteville, AR, and on a Crowley silt loam (fine, montmorillonitic, thermic Typic Albaqualfs) at Stuttgart, AR. Plots contained rectangular Plexiglass root observation tubes oriented at a 45° angle from the soil surface and extending 0.57 m (Captina) and 0.44 m (Crowley) below the soil surface. Root recordings were taken during vegetative and reproductive growth. Root growth was most rapid during vegetative growth at both locations, with maximum root length occurring at panicle initiation. Root lengths either plateaued or declined during reproductive growth. Root lengths were greater on the Captina soil than on the Crowley soil. The pattern of root length densities (RLD) was similar between soils; however, RLDs were generally lower on the Crowley soil. During early stages of rice growth, a greater percentage of rice roots was measured in the upper 20 cm of the Crowley soil. By harvest, about 50% of the rice roots occurred in the top 20 cm in both soils. Harvesting appeared to reduce the percentage of roots found below 20 cm on the Crowley soil, and to have little effect on the Captina soil. Results suggest that rice root growth can vary dramatically between soils, and is influenced by soil profile characteristics, soil moisture, air temperatures, and growth stage.

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Quantitative X-ray radiographic imaging systems that utilize a charged couple device (CCD) camera connected to a thick, monolithic scintillator can exhibit blur that varies spatially across the field of view, especially for thick scintillators used in pulse-power radiography of dynamically compressed objects. A three-point approach to estimating and accounting for this effect is demonstrated by (a) using a local estimation technique to measure the effect of blurring a calibration object at key locations across the field of view, (b) combining each of the local estimates into a spatially varying blurring function via partitions of unity interpolation, and (c) resolving the effects of that blur on the image by solving an ill-posed inverse problem using a spatially varying regularization term. The technique is demonstrated on synthetic examples and actual radiographs collected at the Naval Research Laboratory's (NRL) Mercury pulsed power facility.