Spatio-temporal-spectral observation model for urban remote sensing

Taking cities as objects being observed, urban remote sensing is an important branch of sensing. Given the complexity scenes, observation requires data with a high temporal resolution, spatial and spectral resolution. To best our knowledge, however, no satellite owns all above characteristics. Thus, it necessary to coordinate from existing satellites meet needs observation. In this study, we abstracted process proposed spatio-temporal-spectral model, filling gap framework. present four applications elaborate on specific model: 1) spatio-temporal fusion model for synthesizing ideal data, 2) spatio-spectral vegetation biomass estimation, 3) temporal-spectral flood mapping, 4) impervious surface extraction. We believe that although in conceptual stage, can largely benefit by providing new paradigm.