Seagrass, a typical aquatic flowering plant, thrives in shallow coastal and estuarine waters, playing an important role in maintaining ecosystem stability and facilitating the carbon cycle. However, it is currently facing a significant decline, necessitating effective monitoring. The coastal zone imager (CZI) onboard the HY-1C/D satellite provides 50-m resolution data and high-frequency observations twice every three days, facilitating satellite remote sensing of seagrass. In this study, we analyze the spectral differences among seagrass water, sand water, and other water types in Swan Lake using numerous CZI images from 2018 to 2023, employing the spectral-index method. We then propose a seagrass remote sensing detection model based on the constructed Blue-Red-NIR index (BRN index) and Δz index. The accuracy validation results show that this model performs well in both qualitative and quantitative assessments. Subsequently, we apply the seagrass remote sensing model to CZI images from 2023 to reveal trends in seagrass distribution based on cumulative pixel area and aggregation density. Overall, we provide a reference for monitoring seagrass resources using domestic satellites, which would be beneficial in broadening their application in marine resource monitoring.

In this study, we employ the spectral-index method to develop a seagrass remote sensing detection model. Firstly, we identify the substrate types for water in Swan Lake using in-situ surveys and star-earth matching technology, categorizing them into seagrass water, sand water, and other water (excluding the first two). Then, we select adequate pure samples for the aforementioned three water types through visual interpretation. Following this, we calculate the normalization of remote sensing reflectance of pixels and analyze the spectral differences between seagrass water, sand water, and other water based on numerous samples chosen from CZI images. Next, we discard sand water pixels, which exhibit higher normalized apparent reflectance than the other two water types in the red band. We further distinguish seagrass water from other water pixels by constructing two indexes, i.e., the BRN index and the Δz index. The BRN index denotes the difference between the green band and the NIR band, while the Δz index represents the difference between the blue band and the green band, and is used for separating seagrass water from other water pixels. Subsequently, the seagrass remote sensing detection model is developed based on these indexes, and a confusion matrix is employed to evaluate its performance.

The accuracy evaluation indicates that the seagrass distribution detected by the seagrass remote sensing detection model closely aligns with that of the false-color images [Figs. 8(a) and 8(e)]. Furthermore, a comparison between the seagrass distributions detected by our model and another model using Landsat images, proposed by Liang et al., demonstrates that our model effectively monitors the majority of seagrass in the central part of Swan Lake [Figs. 8(b) and 8(f)], exhibiting good performance in the cumulative seagrass pixel area. Additionally, the confusion matrix results reveal that the seagrass detection model performs well, with Overall Accuracy (OA) exceeding 80% and F1-Score above 0.85 (Table 1). We then apply this model to CZI images from 2023 to calculate the cumulative pixel area and aggregation density, respectively. It is observed that the cumulative pixel area first increases and then decreases, specifically rising gradually from June to August before declining (Fig. 9), while aggregation density peaks in August (Fig. 10). Moreover, we observe stability with slight fluctuations in the cumulative pixel area from 2018 to 2023 (Fig. 11). Overall, the robust performance of the seagrass remote sensing detection model can be attributed to the normalization process based on numerous CZI images (Fig. 12). Furthermore, sensitivity analyses in turbid waters demonstrate that our model remains stable (Fig. 13). Looking ahead, future research should explore the applicability of the seagrass remote sensing detection model in other regions. Additionally, more high-spatial-resolution domestic satellite images, along with hybrid image decomposition technologies, need to be synthesized to achieve highly accurate seagrass detection.

In this paper, we propose a seagrass remote sensing detection model based on two constructed indexes, the BRN index and the Δz index, developed after analyzing the spectral differences among seagrass water, sand water, and other water using HY-1C/D CZI images. Accuracy evaluations show that this model aligns well with false-color images and seagrass distribution results detected by Landsat images. Moreover, it achieves OA exceeding 80% and an F1-Score above 0.85. When applying this model to CZI images from 2023, we find that the cumulative pixel area of seagrass increases from June to August and decreases after peaking in August. Aggregation density shows a similar trend, first increasing and then decreasing from June to October, peaking in September. We also observe stability with slight fluctuations in the annual changes in cumulative pixel area over the last six years. Our proposed seagrass remote sensing detection model using domestic HY-1C/D CZI satellite data provides a reference for monitoring seagrass resources with domestic satellites, broadening their application in marine resources monitoring.