Dissolved oxygen (DO) concentration is a key indicator of the health and productivity of an aquatic ecosystem. This paper presents a new method for high-resolution characterization of DO as a function of both space and time. The implementation of a new oxygen optode in an Iver2 autonomous underwater vehicle (AUV) is described, which enables the system to measure both absolute oxygen concentration and percentage saturation. Also described are details of AUV missions in Hopavågen Bay, Norway, which consisted of a series of repeated undulating lawnmower patterns that covered the bay. Through offline postprocessing of data, sensor characteristic models were developed, as well as a 3D lattice time series model. The model was constructed by estimating DO at each 3D lattice node location using a 1D Kalman filter that fused local measurements obtained with the AUV. By repeating model construction for several missions that spanned 24 h, estimates of DO as a function of space and time were calculated. Results demonstrated (1) the AUVs ability to repeatedly gather high-spatial-resolution data (2) significant spatial and temporal variation in DO in the water body investigated, and (3) that a 3D model of DO provides better estimates of total DO in a volume than extrapolating from only a single 2D plane. Given the importance of oxygen within an ecosystem, this new method of estimating the quantity of DO per volume has the potential to become a reliable test for the health of an underwater ecosystem. Also, it can be refined for detecting and monitoring a range of soluble gases and dispersed particles in aquatic environments, such as dissolved O2 and CO2 around production facilities such as fish farms, or dispersed hydrocarbons and other pollutants in fragile ecosystems. © 2012 Wiley Periodicals, Inc.