Testing　high-dimensional　data　independence　is　an　essential　task　of　multivariate　data　analysis　in　many　fields.　Typically,　the　quadratic　and　extreme　value　type　statistics　based　on　the　Pearson　correlation　coefficient　are　designed　to　test　dense　and　sparse　alternatives　for　evaluating　high-dimensional　independence.　However,　the　two　existing　popular　test　methods　are　sensitive　to　outliers　and　are　invalid　for　heavy-tailed　error　distributions.　To　overcome　these　problems,　two　test　statistics,　a　Spearman＇s　footrule　rank-based　quadratic　scheme　and　an　extreme　value　type　test　for　dense　and　sparse　alternatives,　are　proposed,　respectively.　Under　mild　conditions,　the　large　sample　properties　of　the　resulting　test　methods　are　established.　Furthermore,　the　proposed　two　test　statistics　are　proved　to　be　asymptotically　independent.　The　max-sum　test　based　on　Spearman＇s　footrule　statistic　is　developed　by　combining　the　proposed　quadratic　with　extreme　value　statistics,　and　the　asymptotic　distribution　of　the　resulting　statistical　test　is　established.　The　simulation　results　demonstrate　that　the　proposed　max-sum　test　performs　well　in　empirical　power　and　robustness,　regardless　of　whether　the　data　is　sparse　dependence　or　not.　Finally,　to　illustrate　the　use　of　the　proposed　test　method,　two　empirical　examples　of　Leaf　and　Parkinson＇s　disease　datasets　are　provided.　©　2023　Elsevier　B.V.