Kandelia candel is widely distributed in tropical and subtropical region, and plays a key role in maintaining wetland function. Chlorophyll content is necessary for studying productivity and photosynthesis of vegetation, which can also indicates the healthy condition of vegetation living in a stressed environment. Remote sensing techniques offer timely, up-to-date, and relatively accurate information such as biomass, leaf area index and chlorophyll content of wetland vegetation. Although plenty research efforts on estimating chlorophyll content from spectral reectance measurements have been mainly focused on forest and crop ecosystems. However, very limited work has been done at wetland vegetation. The aim of this study was to estimate the chlorophyll content of K. candel based on hyper-spectral remote sensing data. This study was carried out in the Min River Estuary, which is one of the most important estuarine wetlands in Southeast China. The laboratory spectral reflectance of K. candel leaves (front and back) was determined by ASD FieldSpec2500 in April, 2013 and July, 2013, and the leaf chlorophyll content (two dimensional) was measured simultaneously. Thirteen parameters including visible ratios (NPCI and PRI1), visible/NIR ratios (NDVI, Lic2, TCARI, MCRAI and PSSRa) and red edge reflectance-ratio (Vog1, Vog2, Vog3, GM, Catter2 and CI) indices, were used to establish the estimation models. The results showed that the reflectance of leaf back was higher than that of leaf front, especially more obvious in green band and part of the near infrared band (1450-2450 nm). The correlation coefficients between chlorophyll content and most of the parameters were higher when selecting area instead of quality as dimension. And the most of the parameters calculated by leaf front reflectance had higher correlation coefficient with chlorophyll content than that of leaf back. Besides, we also observed that Vog1, Vog2 and Vog3 not only had higher correlation coefficient with chlorophyll content, but were also slightly affected by leaf growth phase and structure. The limitations of using NDVI for estimation of chlorophyll content had been reported in the literature, and NDVI also had lower correlation coefficient with chlorophyll content of K. candel leaves. Consequently, TCARI, Vog1, Vog2 and Vog3 calculated by leaf front reflectance and the chlorophyll content in per unit area were selected to establish the estimation and validation models. The root means square error (RMSE) of estimation models ranged from 4.93 μg/cm² to 10.24 μg/cm², while it ranged from 4.17 μg/cm² to 9.56 μg/cm² in validation models. These results indicated that TCARI, Vog1, Vog2 and Vog3 were the most useful parameters to estimate the chlorophyll content of K. candel during different growth periods. In addition, GM, Carter2, PSSRb generally had higher correlation coefficient with chlorophyll content when they were calculated by leaf back reflectance, which indicated that we should consider using leaf back reflectance when we choose GM, Carter2, PSSRb to estimate chlorophyll content of K. candel leaves. To some extent, it can be concluded that remote sensing technology could play a vital role in the chlorophyll content retrieval of K. candel by laboratory spectral reflectance.