Suaeda salsa spectral index for Suaeda salsa mapping and fractional cover estimation in intertidal wetlands

Suaeda Salsa (S. salsa), with short and red-purplish plants, is a typical pioneer saltmarsh species in the intertidal wetlands of temperate East Asia. It has important ecological, economic, and recreational values. In the past few decades, S. salsa has severely degraded in coastal China, which has brought widespread attention from regional and local governments. As a result, extensive S. salsa restoration projects have been initiated in recent years. It is urgently needed to develop satellite-based methods for both S. salsa mapping and fractional cover (FC) estimation because degradation and recovery of S. salsa are manifested by changes in both area and plant cover. However, accurate mapping and FC estimation of S. salsa are challenging because (1) S. salsa in intertidal areas have low FC and (2) heterogeneous soil backgrounds in wetlands greatly impact the spectral reflectance observed by satellites. To address these issues, this study proposed a new Suaeda Salsa Spectral Index (SSSI) to support accurate detection and FC estimation of S. salsa. The SSSI was designed based on the laboratory spectral measurements by considering variations in wetland soil moisture and by taking advantage of the reddish color of S. salsa. It consists of two components, one of which utilized blue, green and red bands to separate S. salsa from green vegetation, and the other component utilized a modification of the Soil Adjusted Vegetation Index (SAVI) to reduce the impact of soil background and maintain a linear relationship with S. salsa FC. SSSI was then applied on Sentinel-2/GF-1 images over the Yellow River Delta (YRD) and Liao River Delta (LRD), China. Based on SSSI, a simple thresholding approach was used to identify S. salsa, and a linear regression model was used to estimate FC. With reference datasets provided from field investigations, Unmanned Aerial Vehicle multispectral images and high-spatial resolution satellite images, our results show that the SSSI was able to detect low-coverage S. salsa (FC > 20 % in YRD and FC > 10 % in LRD), and the S. salsa maps had an overall accuracy over 94%. The SSSI-FC models achieved good estimation accuracies (R2 = 0.77 ∼ 0.86, RMSE = 7.55 % ∼ 9.79 %). Compared to the Normalized Difference Vegetation Index (NDVI) and SAVI, SSSI alleviated the impacts from soil backgrounds and provided better S. salsa FC estimations, particularly for low-coverage S. salsa. SSSI has great potential in supporting continuous monitoring of S. salsa dynamics and evaluating the effectiveness of S. salsa restoration projects.