Land　cover　analysis　has　received　significant　attention　in　remote　sensing-related　fields.　To　take　advantage　of　multimodal　data,　hyperspectral　images　(HSI)　and　light　detection　and　ranging　(LiDAR)　are　often　combined.　However,　it　is　difficult　to　capture　intricate　local　and　global　spectral-spatial　associations　between　HSI　and　LiDAR.　To　exploit　the　complementary　information　of　multimodal　data,　a　spectral-spatial　graph　network　is　proposed　that　integrates　HSI　and　LiDAR　data　into　intricate　local　and　global　spectral-spatial　associations.　Specifically,　the　network　consists　of　a　local　module　and　a　global　module.　The　local　module　uses　convolution　techniques　applied　over　image　patches　to　preserve　the　local　spatial　relationships　available　in　multimodal　data.　The　global　module　constructs　a　spectral-spatial　multimodal　graph,　which　is　used　to　preserve　spectral-spatial　proximity　in　multimodal　data.　Both　the　local　and　global　modules　are　utilized　to　their　utmost　capacity　to　generate　the　final　multimodal　data　representation.　Experiments　on　multimodal　remote　sensing　datasets　reveal　that　the　proposed　network　has　attained　performance　levels　comparable　to　state-of-the-art　methods.