A novel mechanism-guided retrieval framework for mangrove chlorophyll content considering leaf area index and spectral resolution based on active learning hybrid model

Abstract

• Revealed pronounced mangrove canopy spectral variability modulated by CHL and LAI. • Identified sensitive spectral domains of CHL across seven spectral resolutions. • Matched simulated sensitive wavelengths with SDGSAT-1 and Sentinel-2 spectral bands. • Developed a mechanism-guided active learning hybrid model for CHL inversion. • Validated the robustness of cross-region transferring inversion of CHL. Mangroves play a crucial role in coastal ecosystem, providing indispensable benefits such as sustaining biodiversity, sequestering carbon, and supporting global productivity. Nevertheless, the long-term survival of these critical habitats is jeopardized by a combination of anthropogenic pressures and natural disturbances. Accurate monitoring of vegetation functional traits, specifically Leaf Area Index (LAI) and chlorophyll content (CHL), is imperative for evaluating the physiological condition of mangrove stands. However, the interaction between LAI and spectral resolution (Spr) in CHL retrieval remains poorly understood. Therefore, this study developed a first-order derivative-weighted spectral angle mapping (FWS) method to quantify the relationships of spectral reflectance across three CHL grades (CHL-Low, CHL-Middle, CHL-High) and three LAI gradients (LAI-Low, LAI-Middle, LAI-High). Our results revealed that spectral reflectance in the 400–800 nm range was negatively correlated with CHL, which stood in contrast to its positive correlation with LAI. We subsequently optimized a mean resample correlation analysis (MRCA) method to identify CHL-sensitive spectral domains, and our results confirmed that these domains were predominantly distributed in the ranges of 466–780 nm (CHL-L), 520–776 nm (CHL-M), and 520–779 nm (CHL-H). To identify optimal wavelengths, we applied the central wavelength matching method (CWMM). Results confirmed that these optimal wavelengths matched the central wavelengths of SDGSAT-1 and Sentinel-2 spectral bands, and we subsequently constructed 84 inversion schemes. This study constructed two mechanism-guided active learning hybrid models (PROSAIL-AL-RF and PROSAIL-AL-NGB). These models achieved robust CHL estimation for the high chlorophyll content grade (CHL-H), with R 2 values ranging from 0.441 to 0.894. Notably, PROSAIL-AL-RF exhibited more robust and consistent accuracy than PROSAIL-AL-NGB in most scenarios. Cross-regional inversion experiments for CHL showed that the hybrid models exhibited reliable performance, with R 2 values of 0.748 for PROSAIL-AL-NGB and 0.740 for PROSAIL-AL-RF. This study elucidates the LAI-Spr interaction mechanisms in CHL inversion and validates the scalability of hybrid models, providing a powerful approach for precisely monitoring the different growth status and stages of mangroves all over the world.

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