To address the challenges of chlorophyll-a(Chl-a) concentration inversion in Lake Hongze—specifically, the lack of high-resolution remote sensing data support and the poor generalization ability of models developed for other lakes due to Lake Hongze’s high suspended particulate matter (SPM) content.This study utilized long-term in-situ Chl-a concentration data (2018–2023) and concurrent Sentinel-2A remote sensing images. Spectral feature analysis and band correlation analysis were employed to identify the optimal band combination for inversion. Statistical regression models (including linear, quadratic polynomial, exponential, power, and logarithmic functions) and machine learning models (LS-SVM, RF, and BP neural network) were constructed. By comparing the inversion accuracy of different models, the optimal inversion model for Lake Hongze was determined. Additionally, the spatiotemporal variation patterns of Chl-a concentration and influencing factors in different lake regions were analyzed based on inversion results.The findings revealed:(1) Due to its high SPM content, Lake Hongze exhibits elevated reflectance across the visible to near-infrared (VNIR) spectrum. Among spectral indices, the narrow near-infrared NDVI showed the strongest correlation with Chl-a concentration.(2) Among statistical regression models,the exponential function model achieved the highest prediction accuracy (RMSE: 0.0075 mg·L-1, MAE: 0.0058 mg·L-1). Among machine learning models, the LS-SVM model performed best (RMSE: 0.0061 mg·L-1, MAE: 0.0049 mg·L-1), significantly outperforming RF and BP neural network models.The LS-SVM model also surpassed all statistical regression models in accuracy.(3) The LS-SVM inversion results indicated that Chl-a concentrations were higher in the northern Chengzi Lake area and western Lihewa area but lower in the eastern water-transfer zone, with pronounced spatial heterogeneity during the wet season. Temperature and rainfall significantly influenced Chl-a concentrations in Chengzi Lake and Lihewa, whereas water level had minimal impact. Hydrological exchange with inflow/outflow rivers acted as an inhibitory factor for Chl-a in the eastern water-transfer zone.