Fusing multiple existing space‐time land cover products

Land cover (LC) is a critical variable driving many environmental processes, so its assessment, monitoring, and characterization are essential. However, existing LC products, derived primarily from satellite spectral imagery, each have different temporal and spatial resolutions and different LC classes. Most effort is focused on either fusing a pair of LC products over a small space‐time region or on interpolating missing values in an individual LC product. Here, we review the complexities of LC identification and propose a method for fusing multiple existing LC products to produce a single LC record for a large spatial‐temporal grid, referred to as spatiotemporal categorical map fusion. We first reconcile the LC classes of different LC products and then present a probabilistic weighted nearest neighbor estimator of LC class. This estimator depends on three unknown parameters that are estimated using numerical optimization to maximize an agreement criterion that we define. We illustrate the method using six LC products over the Rocky Mountains and show the improvement gained by supplying the optimization with data‐driven information describing the spatial‐temporal behavior of each LC class. Given the massive size of the LC products, we show how the optimal parameters for a given year are often optimal for other years, leading to shorter computing times.