In low transmission settings approaching malaria elimination, such as the Greater Mekong Subregion, a large proportion of Plasmodium spp. infections are sub-microscopic. Sub-microscopic infections are molecular-detectable low parasite density infections which go undetected, and therefore untreated, using routine field diagnostics. Antibody serosurveillance, has the potential to detect sub-microscopic infections, as both current and recent exposure events. In order understand the potential use of serosurveillance to identify sub-microscopic infections and high-risk populations in low transmission settings, we determined seroprevalence and levels of antibodies specific for Plasmodium spp. antigens by ELISA in 990 participants living in 20 Western Cambodian villages. Within this population sub-microscopic Plasmodium spp. prevalence was at 9.2% (91/990), with few (n=7) detectable by microscopy. Seroprevalence was high, with 77.7% (769/990) of the total cohort being seropositive to Plasmodium falciparum apical membrane antigen 1 (Pf-AMA1). Both Pf-AMA1 levels and seroprevalence varied across villages (p<0.001), with seroprevalence ranging from 65% (28/50) to 100% (50/50). Variation of seroprevalence was quantified using multivariate mixed effects regression which found marked heterogeneity between villages (adjusted ICC Rho [95%CI]) (0.280 [0.147, 0.467]). Risk factors for seropositivity identified from the multivariate models included the presence of sub-microscopic infections and frequency of self-reported malaria history whereby the odds of sero-positivity approximately doubled in those with current or more than 2 historical infections. The odds of sero-positivity also increased with age, but no association was found with sex, overnight forest stays, international travel, or bed net use. This study supports the utility of malaria antibodies as a serosurveillance tool to determine the micro-heterogeneity of malaria transmission in low transmission areas as well as current sub-microscopic infections and historical (clinical) infections. Its application will allow the micro-stratification of malaria risk in a population to enable spatially targeted interventions to advance progress towards the target of malaria elimination in the Greater Mekong Subregion by 2030.