Remote sensing retrieval of chlorophyll-α in inland waters based on ensemble modeling: a case study on Panjiakou and Daheiting reservoirs

Abstract

Accurate remote sensing retrieval of chlorophyll-α (Chl-α) concentrations in inland waters raises a challenge due to the optical complexity of water constituents. Five Chl-α retrieval models, including single-band, band ratio, three-band, four-band, and partial least square models, were established with the measured spectra and Chl-α concentrations were measured at 36 stations in Panjiakou and Daheiting Reservoirs. To improve the Chl-α retrieval accuracy, three ensemble models, namely, entropy weight-based ensemble model (EW-EM), set pair analysis-based ensemble model (SPA-EM), and Bayesian model averaging-based ensemble model (BMA-EM), were developed for Chl-α retrieval with the weighted average of the five Chl-α retrieval models. All models were evaluated based on random calibration and validation samples. Ensemble modeling improved the Chl-α retrieval accuracy through integrating multiple Chl-α retrieval models. Compared to EW-EM and SPA-EM, BMA-EM could not only improve the Chl-α retrieval accuracy but also provide reliable confidence intervals for Chl-α retrieval. Ensemble modeling has application prospects in remote sensing retrieval of water constituents in inland waters.