## 5 Conclusions  
In this study, we propose a novel method for the remote sensing inversion of _Suada salsa_ biomass based on the hue angle derived from UAV-acquired visible imagery. Owing to its distinct chromatic characteristics, _Suueda salsa_ can be effectively distinguished from mudflats, water bodies, and other green vegetation such as reeds, resulting in a significant spectral separation in hue angle.  
To establish a quantitative relationship between hue angle and _Suueda salsa_ biomass, drone-based reflectance standard panels (DRSPs) were employed to convert raw pixel values into reflectance and hue angle values. A hue angle threshold of 249.01 was identified for reliably extracting _Suueda salsa_ pixels, demonstrating robust performance even in heterogeneous environments where the species is interspersed with reeds or present at low density.  
A strong exponential relationship was established between hue angle and _Suueda salsa_ biomass, described by the model Biomass = 3.57639 \(\times\) 10\({}^{-15}\)\(\times\)\(e^{0.12201\times\alpha}\), with a coefficient of determination (\(R^{2}\)) of 0.99696, a mean absolute percentage error (MAPE) of 3.616%, and a root mean square error (RMSE) of 0.02183 kg/m\({}^{2}\). The resulting biomass inversion mapsexhibited a high degree of agreement with field measurements, accurately reflecting the spatial heterogeneity of biomass distribution.  
As a foundational species in coastal salt marshes, _Sueda salsa_ plays a critical role in maintaining ecosystem stability and biodiversity. This study demonstrates the utility of low-cost UAV visible imagery in overcoming the spatial resolution and atmospheric constraints associated with satellite-based remote sensing, significantly improving the timeliness and precision of wetland monitoring. In addressing the limitations of conventional vegetation indices such as the NDVI and RVI for species-level classification, we developed and validated a hue angle-based identification method with superior discrimination capability and quantitative accuracy. This approach offers key technical support for the development of high-resolution, intelligent monitoring systems.  
Future research will focus on expanding the application of UAV-based visible imagery in ecological monitoring and advancing toward a standardized and scalable framework for supporting ecosystem assessment, management, and conservation.  
Conceptualization, L.W. and X.W. ([PERSON]); Methodology, L.W.; Investigation, X.W. ([PERSON]) and L.W.; Data Curation, X.S. and S.W.; Validation, [PERSON] ([PERSON]); Formal Analysis, Q.M.; Writing--Original Draft Preparation, L.W. and Q.M.; Writing--Review and Editing, [PERSON] ([PERSON]), X.S., X.W. ([PERSON]) and S.W.; Supervision, L.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Environmental Protection Key Laboratory of Coastal Ecosystem (20220202), the National Key Research and Development Program of China (2019 YFC1407904, 2018 YFC1407605), and the National Natural Science Foundation of China (42076186).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
We would like to thank our colleagues from National Marine Environmental Monitoring Center for their help in field spectral measurements, the results of which were used in this work.  
The authors declare no conflicts of interest.