This paper is an extension of a previous work that examined a decentralized approach to evaluate the uncertainty of estimating a spatial process using guided model-based multi-agent exploration. The model is a superposition of fixed kernel functions, with each kernel playing the role of a feature. The measurements, collected by the agents, are then used to collectively estimate the weights of the features under sparsity constraints and derive the corresponding spatial uncertainty distribution to optimally guide the agents to reduce the uncertainty. This paper extends these results in several respects. First, we investigate different coordination strategies, which all aim to efficiently optimize the exploration criterion in a distributed multiagent setting. Second, we compare different features, specifically radial basis functions (RBFs), Lanczos kernels, Legendre polynomials, and discrete cosine functions. Third, we conduct hardware-in-the-loop experiments to validate the proposed coordination strategies using real robots. Results show that the coordination strategy together with the selected feature has a significant influence on the exploration performance.