Recently, WANG Yuxin, a master's student in our laboratory, published a research paper titled "Satellite retrieval of benthic reflectance by combining lidar and passive high-resolution imagery: Case-I water" in the top journal Remote Sensing of Environment. The corresponding author is researcher HE Xianqiang.
In the context of global warming, the health of shallow-sea ecosystems such as coral reefs and seagrass beds has received increasing attention. Satellite remote sensing is an important means of monitoring the health of shallow-sea ecosystems over a wide range and over a long period of time. The traditional method of retrieving the spectral reflectance of shallow seabed based on passive high spatial resolution multispectral and hyperspectral optical satellite images requires simultaneous retrieval of water depth, water optical properties, and substrate reflectivity, resulting in many unknowns and great uncertainty. Active spaceborne lidar satellites can effectively detect shallow water depth and underwater light attenuation coefficient in clean sea areas. However, since there is currently only one visible light channel, multispectral and hyperspectral substrate reflectance cannot be obtained. Therefore, there are still certain difficulties in accurately retrieving substrate spectral reflectance from active or passive remote sensing alone.
To address this problem, this paper proposes a semi-analytical remote sensing model of spectral reflectance of shallow seabed material that combines active spaceborne lidar and passive high spatial resolution satellite images without prior knowledge of water depth and water optical properties. Based on the two-flow underwater radiation transfer theory, the theoretical relationship between substrate reflectance, remote sensing reflectance, pure water column reflectance, water diffuse attenuation coefficient and water depth was established, and a water column reflectance lookup table was constructed. Further implemented the substrate spectral reflectance inversion algorithm that can be practically applied to satellite data. The basic process (Figure 1): 1) Obtain the remote sensing reflectance of the water surface in each band based on passive high-resolution satellite image inversion; 2) Based on lidar Obtain the water depth and 532nm water diffuse attenuation coefficient; 3) Estimate the water chlorophyll concentration based on the 532nm water diffuse attenuation coefficient; 4) Use the chlorophyll concentration to estimate the water diffuse attenuation coefficient in each band; 5) Based on the water depth, the water diffuse attenuation coefficient in each band, and each band Pure water column reflectivity and water surface remote sensing reflectance are inverted using the established semi-analytical model to obtain the substrate reflectivity in each band along the track.
The Hydrolight simulation data set was used to verify the accuracy of the model, which proved that in a typical clean shallow water environment, the correlation coefficient between the inversion results and the real substrate reflectance can reach more than 0.9. In addition, based on the spaceborne lidar ICESat-2, the high-resolution multispectral satellite Sentinel-2, and the domestic hyperspectral satellite Zhuhai-1, the paper conducted substrate spectral reflectance reflections on the seagrass bed in Lingshui, Hainan and Huaguang Reef in the South China Sea. The results show that the algorithm can effectively retrieve the spectral reflectance of seagrass and coral reef substrates.
This algorithm makes full use of the respective advantages of active and passive remote sensing, implements the fusion application of the two detection methods, and provides a scientific basis for the development of optical active and passive integrated detection satellites. At the same time, this algorithm is of great significance for monitoring the health status of the global shallow seabed ecological environment and estimating carbon storage.
Figure 1. Flow chart of substrate reflectivity inversion algorithm
Wang, Y., He, X., Bai, Y., Wang, D., Zhu, Q., Gong, F., Yang, D., Li, T., 2022. Satellite retrieval of benthic reflectance by combining lidar and passive high-resolution imagery: Case-I water. Remote Sensing of Environment 272, 112955.
