Chlorophyll-a concentration is a key indicator for assessing the water quality of aquatic ecosystems, and its remote sensing inversion is crucial for large-scale dynamic monitoring of lakes. In response to the limitations of existing methods, such as their reliance on large amounts of labeled data, being computationally intensive, and insufficient adaptation to multi-band remote sensing characteristics, this paper proposes a semi-supervised regression framework named ForestSimReg based on random forest. The framework enhances the model’s robustness to spectral interference through a spectral band masking augmentation strategy and integrates a dual mechanism combining pseudo-label filtering based on out-of-bag estimation and calibration based on decision path similarity. This effectively suppresses noise, improves pseudo-label quality, and enhances generalization capability with limited labeled samples. Experimental results on the Erhai Lake chlorophyll-a dataset show that: 1) ForestSimReg outperforms mainstream comparison models in terms of R², MAE, and RMSE, achieving anR2