<i>Suaeda Salsa</i> Hyperspectral Index (SSHI) for mapping <i>S. salsa</i> in coastal wetlands using hyperspectral satellite imagery: case studies in northern coastal China

Suaeda Salsa (S. salsa), a pioneer species with short and red-purplish plants in the intertidal zones, has significant ecological, economic, recreational and tourism values. Timely monitoring of S. salsa is crucial for understanding its dynamics and sustainable management of coastal wetlands. Hyperspectral satellite offers valuable opportunities due to its detailed spectral information. This study proposed a Suaeda Salsa Hyperspectral Index (SSHI) for S. salsa mapping based on hyperspectral satellite imagery. SSHI was developed by considering the large within-class spectral variations of cover types at coastal wetlands, accounting for the spectral correlations in hyperspectral data, and employing dynamic band selection on a per-pixel basis to optimize the separation of S. salsa from other land covers. [Formula: see text], where i∈[499 nm, 611 nm], j∈[628 nm, 851 nm], [Formula: see text]. We applied SSHI on ZY1-02D/E AHSI images over Yellow River Delta (YRD) and Liao River Delta (LRD), China during 2021–2023. To evaluate the performance of SSHI, a simple thresholding method and a random forest (RF) model were used to map S. salsa, respectively. Our results showed that the overall accuracies of S. salsa maps achieved 87.96~89.43% (YRD) and 92.03~93.36% (LRD) using thresholds, and 92.62~94.05% (YRD) and 94.74~94.79% (LRD) using RF. For RF, incorporating SSHI improves S. salsa producer’s accuracies (user’s accuracies) by 1.02~6.85% (2.26~6.25%) compared to those without SSHI, proving the effectiveness of SSHI. The S. salsa maps reveal notable temporal variations, reflecting the impacts of climate change and human activities. Compared to existing hyperspectral indices related to red pigments, SSHI depicts greater separability between S. salsa and non-S. salsa. Meanwhile, SSHI is also applicable to other hyperspectral images such as GF-5B and PRISMA. SSHI has shown good adaptability and robustness for different time periods, regions, and hyperspectral satellite images, indicating that SSHI presents significant potential in accurately and effectively detecting S. salsa to facilitate sustainable coastal wetland monitoring and management.