Shallow water benthic habitats have been significantly degraded and seriously threatened by intensifying climate changes and anthropogenic stressors. Benthic reflectance () of optically shallow waters (OSWs) is a key parameter for remote sensing of benthic habitats’ composition and health status. The remote sensing reflectance () just above the water surface contains the coupled spectral information of the water column and seabed in OSWs, which makes our effort challenging to separate the signals between seabed and water column properties. Due to too many unknowns of the water column and seabed optical properties, the current approaches are inaccurate or inadequate to retrieve benthic reflectance spectra over a large area from remotely sensed data. In this study, we proposed an enhanced large-scale benthic reflectance (LSBR) retrieval model from satellite data in OSWs with only input of . A new spectral library of benthic reflectance was built upon multi-source reflectance datasets obtained from several open-source field measurements. Then, the specific relationship between and was developed. By combining a semi-analytical benthic reflectance (SABR) model based on radiative transfer simulations and this relationship, water-column chlorophyll concentration () was determined. Moreover, large-scale water depth was estimated by through satellite-derived bathymetry (SDB) technologies. Finally, benthic reflectance was retrieved with the inputs of the satellite-derived , , and water depth. In this way, the LSBR model fulfills the retrieval with only input of satellite-derived . The new model was successfully applied to Sentinel-2/MSI data in the seagrass region of Xincun Bay and the coral region of Huaguang Reef and further verified by using field measurements and results of the along-track benthic reflectance (ATBR) retrieval model, which combined active lidar and passive high-resolution satellite images. Additionally, to gain insights into the long-term evolution of benthic communities, a typical application of the LSBR model for detecting benthic changes was conducted using eight years of Sentinel-2/MSI data in Heron Reef, depicting a similar consistency between the probable deterioration and restoration region and the sea surface temperature (SST) anomalies. The results demonstrated the robustness and accuracy of the LSBR model in retrieving benthic reflectance at large scales. The LSBR model should be helpful for further investigating benthic changes which can enhance the ability to monitor and assess the health status of submerged ecosystems in OSWs.

中文翻译：

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用于光学浅水区水下生态系统遥感的增强型大规模底栖反射率反演模型

浅水底栖栖息地已严重退化，并受到气候变化和人为压力因素的严重威胁。光学浅水区 (OSW) 的底栖反射率 (OSW) 是底栖栖息地组成和健康状况遥感的关键参数。水面上方的遥感反射率 () 包含 OSW 中水柱和海底的耦合光谱信息，这使得我们在分离海底和水柱特性之间的信号方面面临挑战。由于水柱和海底光学特性有太多未知数，当前的方法不准确或不足以从遥感数据中反演大面积的海底反射光谱。在本研究中，我们提出了一种增强型大规模底栖反射率 (LSBR) 反演模型，仅输入 OSW 中的卫星数据。一个新的海底反射光谱库是根据从多个开源现场测量获得的多源反射数据集构建的。然后，发展了 和 之间的具体关系。通过结合基于辐射传输模拟的半解析底栖反射率（SABR）模型和这种关系，确定了水柱叶绿素浓度（）。此外，通过卫星测深（SDB）技术估算了大范围水深。最后，利用卫星来源的 、 和水深的输入来检索海底反射率。通过这种方式，LSBR 模型仅使用来自卫星的输入即可完成检索。新模型成功应用于新村湾海草区和华光礁珊瑚区的Sentinel-2/MSI数据，并通过现场测量和沿轨底栖反射率（ATBR）反演模型的结果进一步验证，结合主动激光雷达和被动高分辨率卫星图像。此外，为了深入了解海底群落的长期演变，使用苍鹭礁八年的 Sentinel-2/MSI 数据进行了 LSBR 模型用于检测海底变化的典型应用，描绘了可能的恶化之间的类似一致性以及恢复区域和海面温度（SST）异常。结果证明了 LSBR 模型在大尺度反演海底反射率方面的稳健性和准确性。 LSBR模型应有助于进一步研究海底变化，从而增强监测和评估OSW水下生态系统健康状况的能力。