A Review of Remote Sensing on Spartina alterniflora: Status, Challenge, and Direction

Highlights

What are the main findings?

• Remote sensing monitoring of S. alterniflora has evolved from static mapping to dynamic process monitoring and functional ecosystem assessment.
• Large-scale S. alterniflora mapping is effective, but mixed pixels and spectral confusion remain challenges, requiring stronger integration of AI methods.

What is the implication of the main finding?

• Future work should integrate multi-source data, improve sample systems, and enhance short- and long-term dynamic monitoring of S. alterniflora.
• The study provides a framework and insights to support effective S. alterniflora control and coastal wetland management.

Abstract

This review systematically analyzes 215 papers on the remote sensing monitoring of Spartina alterniflora (S. alterniflora) indexed in the Web of Science database to clarify research progress and future development directions in this field. We applied CiteSpace 6.3.R1 to conduct a bibliometric analysis of remote sensing literature on S. alterniflora, summarizing the technical methodologies across three major domains: distribution dynamics, parameter inversion, and ecosystem functions and services. We traced the technological evolution of multi-source remote sensing and artificial intelligence, and explored application prospects in addressing current challenges and supporting precision management. Our research indicates that the primary challenge lies in the complex and diverse spatiotemporal dynamics of S. alterniflora. To achieve timely monitoring of S. alterniflora changes and large-scale ecological impact assessments, it is essential to fully utilize the advantages of multi-source remote sensing big data. Harnessing artificial intelligence technologies to fully exploit the potential of remote sensing data, enhancing multi-source data fusion, and expanding sample libraries are essential to achieve comprehensive monitoring spanning spatial patterns, ecological processes, and ecosystem functions and services. These efforts will provide a scientific basis and decision-making support for the sustainable management of coastal wetlands.

1. Introduction

Coastal wetlands, situated at the interface between land and sea, represent one of the most fragile yet economically promising ecosystems on Earth [1]. Against the context of global change, plant invasions threaten the habitat conditions and biological interactions within coastal wetland ecosystems [2,3]. Among these, the invasion of S. alterniflora has emerged as a prominent threat to coastal ecosystems, particularly in regions such as China [4]. Since its introduction to China in 1979, S. alterniflora has rapidly expanded across coastal regions, causing certain detrimental ecological effects [5]. For example, the rapid expansion of S. alterniflora reduces the food supply available to shorebirds [6]. The severity of biological invasion issues, exemplified by S. alterniflora, has drawn international attention. Targets 6.6, 14.2, 14.5, and 15.8 of the United Nations Sustainable Development Goals underscore the urgency of effectively managing invasive alien species in coastal areas [7]. In response to these international commitments and the severe local impacts, the Chinese government launched a nationwide integrated management initiative in 2023, aiming to eradicate over 90% of S. alterniflora in coastal areas by 2025 [8]. This management initiative places heightened demands on monitoring its distribution and evaluating the effectiveness of control measures.

Compared with traditional field survey methods, remote sensing technology greatly improves monitoring efficiency and reduces labor and material costs by virtue of its efficient observation capacity, broad spatio-temporal coverage, and multi-source data advantages [9,10]. Over the past four decades, scholars have systematically characterized S. alterniflora using optical (multispectral and hyperspectral), textural, and phenological features, alongside radar backscatter and Light Detection and Ranging (LiDAR)-derived signatures. In terms of data applications, Landsat and Sentinel imagery are widely employed for large-scale distribution mapping owing to their long time series, moderate spatial resolution, and free accessibility [11,12,13]. Meanwhile, Unmanned Aerial Vehicles (UAVs) and high-resolution satellite imagery are commonly employed for small-patch identification [14], and LiDAR data is primarily utilized for vegetation height estimation [15,16]. By integrating these diverse capabilities, remote sensing data offers distinct advantages in mapping the spatial distribution of S. alterniflora and has been extensively applied in the inversion of ecological parameters and the assessment of ecosystem functions and services. Nevertheless, the synergistic use of multi-source data is constrained by technical bottlenecks, primarily the lack of reliable methodologies for enhancing spatial details in low-resolution imagery and achieving consistent resolution across heterogeneous data [14]. Furthermore, some monitoring models exhibit limited generalizability when applied across diverse geographic regions or different temporal scales [17,18]. Therefore, a comprehensive review and synthesis of existing research is essential for advancing the development of S. alterniflora remote sensing monitoring.

Previous studies have systematically reviewed the application of remote sensing to invasive alien plants [19], the impacts of S. alterniflora on ecosystem structure and function [20,21,22], and the assessment of its blue carbon services [23]. However, systematic and comprehensive reviews are still lacking that analyze the applicability of remote sensing techniques to this species, explore its potential applications, and address the technical challenges of monitoring under global climate change. Therefore, the paper addresses three core questions: (1) What are the key research areas and global distribution characteristics of remote sensing applications for S. alterniflora? (2) How effective is remote sensing in monitoring S. alterniflora, parameter inversion, and ecosystem assessment? (3) What are the primary challenges and future directions for remote sensing research on S. alterniflora? This paper aims to systematically review the development trends in the application of remote sensing for S. alterniflora, providing methodological references for tracking its distribution dynamics, deriving ecological parameters, and assessing ecosystem functions and services. It further offers decision support for enhancing the scientific management of S. alterniflora by governmental authorities.

2. Materials and Methods

To comprehensively and systematically identify and analyze research on monitoring S. alterniflora using remote sensing data, peer- reviewed research papers were selected from Web of Science using the following search string: (ALL = (“remotely sensed” OR “remote sensing” OR “satellite” OR “earth observation” OR “Landsat” OR “Sentinel” OR “MODIS” OR “UAV” OR “SAR” OR “Hyperspectral” OR “LiDAR”)) AND ALL = (“Spartina alterniflora” OR “S. alterniflora” OR “cordgrass”). Within the timeframe spanning 1983 to 2025 (up to and including April 2025), a total of 400 papers were retrieved. Subsequently, screening was conducted according to the following criteria: (1) Publication type restricted to journal articles (JOURNAL); (2) Research must be based on remote sensing data; (3) The subject of study must be S. alterniflora, excluding research solely involving other species within the Spartina genus. Following screening, a total of 215 valid publications were identified and subjected to bibliometric analysis. Furthermore, key information such as study regions, research periods, remote sensing data types, and modelling algorithms was extracted from these 215 publications. CiteSpace 6.3.R1 software was subsequently employed to conduct further analysis of these publications.

This study systematically analyzed the literature on the remote sensing applications of S. alterniflora from the following four perspectives: (1) Analysis of Research Hotspots. CiteSpace 6.3.R1 software was employed to identify research hotspots and their evolutionary trajectories, which allowed us to delineate research themes and quantify the volume of literature across different time periods for each theme. Chord diagrams were further used to reveal the relationships between research themes and countries, while ArcGIS 10.8 was applied to analyze the global spatial distribution of S. alterniflora study areas. In addition, chord diagrams were used to explore the associations between research themes and remote sensing data, thereby revealing the application preferences for remote sensing data across different fields. (2) Spatial Distribution and Temporal Changes. The number of studies involving different data sources, features, and methods was quantified, and the evolutionary trajectory of this research field was analyzed from the perspectives of data, features, and methodologies. (3) Ecological Parameter Inversion. The applications of different remote sensing data and inversion models across various ecological parameters were compared to identify existing limitations and highlight potential research directions. (4) Ecosystem Function and Service. By systematically reviewing relevant literature, this study summarized both positive and negative impacts of S. alterniflora on ecosystem function and service, and analyzed the applications and potential of remote sensing technology in this field.

3. Results

3.1. Research Hotspots and Global Distribution

Keywords analysis of the 215 reviewed studies using CiteSpace 6.3.R1 software (Figure 1) shows that research has primarily focused on themes such as “classification,” “China,” “dynamics,” “aboveground biomass,” and “blue carbon.” In recent years, terms including “coastal blue carbon,” “random forest,” “Google Earth Engine,” and “time series” have emerged as prominent research foci. Based on these trends, the literature was categorized into three groups: “Distribution and Dynamic Analysis,” “Ecological Parameters,” and “Ecosystem Services and Functions.” This classification provides a clearer overview of research progress on the application of remote sensing techniques to S. alterniflora.

Figure 2A shows that since Hardisky et al. first published on S. alterniflora remote sensing in 1983, research in this field progressed slowly for nearly three decades, with only a few studies published between 2003 and 2012. From 2013 to 2018, the number of studies gradually increased, focusing mainly on ecological parameters and spatial distribution. Since 2019, research has expanded rapidly. Most studies have concentrated on “Distribution and Dynamic Analysis,” while “Ecological Parameters” and “Ecosystem Services and Functions” have also received sustained attention, with the latter slightly exceeding the former in volume. Research preferences differ across countries. For example, studies in China are generally more comprehensive, with a higher proportion focusing on “Distribution and Dynamic Analysis,” representing 63.4% of all Chinese studies. And research in the United States places greater emphasis on “Ecological Parameters”, representing 73.8% of all US studies (Figure 2B).

In terms of spatial distribution, China and the United States are the most frequently studied countries. Research has also been conducted in Argentina, Uruguay, Brazil, and Spain. Study sites are primarily concentrated along the eastern coasts of continents within 25–40° latitude in both hemispheres, with the Yellow River Delta, Yancheng Wetlands, Chongming Island, and Zhangjiang Estuary in China, as well as Sapelo Island in the United States, representing the most extensively studied locations (Figure 2C).

Of the 215 papers reviewed, 82% employed satellite data, 16% utilized drone data, and only 2% utilized aerial photographs (Figure 3). Among the satellite datasets, Landsat was the most widely used, appearing in 83 papers and serving as the primary data source across all three major research domains related to S. alterniflora. Sentinel-2 ranked second, being applied in 35 studies, primarily for monitoring the spatial distribution and dynamics of S. alterniflora. In addition, high-resolution satellite imagery such as SPOT, Pléiades, Gaofen, and WorldView has played a crucial role in the precise identification of small patches. For studies focused on the inversion of ecological parameters and the evaluation of ecosystem service functions, LiDAR constituted the primary data source (Figure 3). Sentinel-1, as a SAR dataset, was employed in 12 papers, mainly for research on “spatial distribution and dynamic analysis” and “ecosystem service functions.” Furthermore, UAVs can be equipped with a variety of sensors, including multispectral, hyperspectral, LiDAR, and SAR. This capability enables flexible data acquisition and provides high-resolution observations that are particularly valuable for small-scale studies.

3.2. Methodologies for Mapping Spatial Distribution and Temporal Changes

3.2.1. Diversification of Remote Sensing Data

In the early stages of research, scholars mapped the extent of S. alterniflora salt marshes using aerial photographs [24], laying the groundwork for subsequent studies based on satellite remote sensing data. Subsequently released Landsat data has become the most widely used dataset in this type of research (Figure 4). Landsat offers extensive coverage and provides nearly 50 years of data records, making it advantageous for large-scale, long-term monitoring [25]. However, its 30-m resolution limits the ability to perform detailed classification of S. alternifora. At finer scales, high-resolution satellite data from the Resource series, WorldView-2, SPOT-6, Gaofen series, GeoEye-1, and Pléiades achieve spatial resolutions ranging from 10 m to sub-meter levels, enabling monitoring of early-stage S. alterniflora invasion in small patches [14,26,27,28]. However, the high cost of such data limits its application at larger scales. Since 2020, Sentinel-2 data has emerged as the second major data source following Landsat, leveraging its advantages of 10-m resolution, 5-day temporal resolution, and free availability (Figure 4). Moreover, Sentinel-2 data features a unique red-edge band, providing superior performance in identifying S. alterniflora. In contrast, hyperspectral data features a greater number of narrower spectral bands, thereby enhancing the separability between S. alterniflora and mud in low-tide areas [29].

Additionally, since S. alterniflora occupies a specific ecological niche in the intertidal zone, elevation information derived from LiDAR data is often used for identifying its distribution range [30,31]. Synthetic Aperture Radar (SAR) technology, also a form of active remote sensing, offers advantages such as all-weather capability, continuous day-and-night operation, and insensitivity to cloud cover [30]. Among these, the open-access Sentinel-1 is the most widely used SAR data (Figure 4), featuring a spatial resolution of up to 10 m and a revisit cycle of 6 to 12 days [32].

In recent years, UAV technology has become increasingly widespread. Equipped with high-resolution multispectral [18], hyperspectral [33], SAR [34], or LiDAR sensors [31], UAV can capture data at sub-meter to centimeter resolution [35]. UAV-based remote sensing provides valuable support for addressing the complex and dynamic challenges of coastal tidal environments [36].

Single data sources have limitations in terms of temporal and spatial resolution, prompting research to gradually shift toward multi-source data integration. Research often combines medium- and high-resolution datasets to enhance spatial resolution and enrich temporal imagery. For example, the integration of Sentinel-2 and WorldView-2 provides high-quality, fine-resolution spatiotemporal image data [17]; the combination of Landsat 8 and Sentinel-2 enhances observation frequency for monitoring the timing of S. alternifora removal [37]. The emergence of multi-source data has greatly enriched the feature dimensions available for classification, but it also poses new challenges regarding how to effectively select and fuse these multidimensional features.

3.2.2. Synergistic Use of Multidimensional Features

The incorporation of multi-source remote sensing data and multi-dimensional classification features significantly enhances the classification accuracy of S. alterniflora. Spectral features remain the foundation of research. The spectral response of S. alterniflora is primarily influenced by biomass, chlorophyll content, cell structure, and water status. For instance, senescent leaves exhibit lower near-infrared reflectance due to disrupted cell structure and reduced water content [38], while salinity stress leads to decreased red-edge slope and NIR reflectance [39]. Therefore, the red, near-infrared, and red-edge bands and their derived indices are widely applied in S. alterniflora mapping. In particular, the multi-temporal vegetation indices constructed based on the red-edge band of hyperspectral imagery showed higher classification accuracy (>91.6%) [40] than the traditional NDVI (91.47%) and EVI (84.78%) [40]. The S. alterniflora Index (SAI), developed based on Sentinel-2 data, is specifically dedicated to mapping the distribution of S. alterniflora. The SAI has not only improved the detection accuracy of submerged vegetation [41], but its applicability to Landsat data has also been demonstrated [42]. Texture features provide a new dimension for invasion monitoring. The incorporation of texture features markedly enhanced the accuracy of early-stage invasion detection for S. alterniflora [43]. Texture information based on CLBP and GLCM can compensate for the limitations of spectral features, significantly improving classification accuracy [25,44].

Radar signatures reveal information about the structural and dielectric properties of S. alterniflora [45]. The VV/VH polarization combination effectively distinguishes coastal wetland plant species [46]. Further research indicates that incorporating coherence metrics from SLC data, particularly the annual average VH coherence coefficient, can enhance classification accuracy by over 8.854% [47]. In recent years, research has begun to delve into the potential of time-series InSAR, where coherence and phase are highly sensitive to subtle structural changes in S. alterniflora and water bodies. These characteristics demonstrate the high discriminative capability of InSAR features in complex coastal environments [48]. However, radar scattering mechanisms are complex and less interpretable, while SAR data sources are relatively limited compared to optical data sources.

Beyond static features, the phenological features offer crucial insights for dynamic monitoring. Early studies captured differences between S. alterniflora and native vegetation through NDVI time series [49,50]. Subsequent research developed more sophisticated parameter extraction methods based on Ppf-CM, PVI, and pixel differential time series [41,51,52]. These approaches not only enhance the robustness of phenological features but also reinforce their central role in S. alterniflora mapping (Figure 4). However, phenological features are highly sensitive to tidal dynamics during extraction, thus necessitating the development of more robust time-series preprocessing methods, such as tidal filter [53] and Tide Gap Filling algorithm [37], to recover the true vegetation signal. Moreover, phenological features exhibit significant spatial heterogeneity across latitudes [54], which can constrain the classification accuracy of large-scale models.

Multimodal big data from remote sensing supports the construction of multidimensional classification features for S. alterniflora. Combining the temporal and spectral characteristics of Sentinel-2 with the spatio-temporal features of Sentinel-1 can significantly enhance classification accuracy [55,56]. DEM data derived from lidar is also used as auxiliary information, aiding in the accurate classification of S. alterniflora [29]. However, redundancy exists within multidimensional features, and employing recursive feature elimination (RFE) for optimized selection has become a critical step in enhancing both accuracy and interpretability [55,56].

3.2.3. Optimization of Classification Algorithms

In the face of increasingly complex multi-source data and high-dimensional features, traditional classification algorithms are gradually proving inadequate, driving the widespread adoption of machine learning and deep learning algorithms. Traditional visual interpretation or historical record retrieval methods are typically laborious and time-consuming, proving inadequate for handling large datasets and complex scenarios. With the rapid advancement of remote sensing classification algorithms, research into the classification and mapping of S. alterniflora is increasingly characterized by large-scale, long-term time series, high precision, and intelligent approaches. Intelligent extraction methods utilizing remote sensing are categorized into pixel-based approaches and Object-based image analysis (OBIA). Pixel-based methods rely on independent pixel features. However, OBIA treats groups of multiple homogeneous pixels as processing units, integrating spectral data with spatial information such as morphology and texture, making it suitable for scenarios with significant intra-class variation in high-resolution imagery. Common techniques include multi-scale segmentation in eCognition and the SNIC algorithm within the Google Earth Engine (GEE) platform [57,58].

Pixel-based and object-oriented classifiers have been continually refined, with steadily improving performance. Early approaches predominantly employed maximum likelihood estimation [31,59], followed by decision trees (DT) [26,60], support vector machines (SVM) [61], random forests (RF) [10,62], artificial neural networks (ANN) [46], and gradient boosting algorithms (XGBoost) [51] gradually becoming mainstream (Figure 4). OBIA combined with SVM can be applied to large-scale mapping [25], but SVM is a linear classifier which is sensitive to parameter selection, and the training process may be more complex. RF demonstrates robustness under high-dimensional features and big data, and resolves the overfitting issue present in SVM [41]. XGBoost also demonstrates advantages in handling complex data [46], often achieving performance comparable to RF, with only modest differences between the two in most applications [63]. However, due to the complex scale effects present in high-resolution imagery, OBIA faces challenges because it relies on manually preset segmentation scales. The recently emerging scale-free classification framework provides an effective approach to solving the scale selection dilemma for S. alterniflora in coastal wetlands [64]. Moreover, the One-Class Classification (OCC) approach offers novel solutions to the problem of sample scarcity [27].

In recent years, deep learning algorithms represented by Convolutional Neural Networks (CNNs) have emerged as a focal point in the remote sensing monitoring of S. alterniflora. Compared to traditional shallow machine learning algorithms, deep learning shows greater advantages in automated processing, feature extraction, and data fitting, particularly in complex environments. Deep learning models are exhibiting diverse developmental trends (Figure 4). Super-Resolution Convolutional Neural Networks and Fast Super-Resolution Convolutional Neural Networks network architectures enhanced the early detection capability of small patch invasions [14]; the tiny-scene embedding network (TSE-Net) effectively mitigated spectral confusion between S. alterniflora and native vegetation [65]; in polarized SAR image classification, AlexNet and VGG16 achieve accuracies ranging from 94.93% to 96.54%; MRCNN demonstrates strong recognition capabilities in small-scale or single-temporal images [66].

However, deep learning still faces the curse of dimensionality when processing high-dimensional remote sensing data. The dimension reduction strategy combining RFE with principal component analysis has been demonstrated to significantly enhance model performance [47]. In multi-temporal dynamic monitoring, distribution bias caused by high sample acquisition costs and unstable image quality poses a major challenge. Transfer learning offers a potential solution to this issue. For instance, IDPNet achieves effective cross-year multi-source data transfer through unsupervised domain adaptation [67], while DeepLabv3+ demonstrates high temporal transferability [68].

Overall, recent advances in remote sensing technology have significantly enhanced the monitoring of S. alterniflora distribution and dynamics. Diverse data sources, multidimensional features, and improved algorithms collectively enhance classification accuracy and robustness.

Table 1. Comparison of remote sensing data and classification algorithms for S. alterniflora. COC is computational cost, CAC is capital cost and SAC is sample cost. * stands for low level, ** stands for moderate level and *** stands for high level.

	Typical Data Source/Classifier	Feature Extraction	Advantages	Limitations	Application Scenarios	Cost Demands
Medium-resolution satellite	Sentinel-2, Landsat	Spectral, spatial and phenological features	Data is readily accessible, with broad coverage and extensive temporal scope.	Affected by cloud cover and mixed pixels	Large-scale, long-term continuous change monitoring	COC (**) CAC (*)
High-resolution satellite	GaoFen, WorldView, SPOT-6	Spectral and spatial features	Achieve sub-meter spatial distribution identification.	Difficult to conduct large-scale monitoring	Early-stage patch identification in the initial invasion phase	COC (***) CAC (***)
Hyperspectral data	Zhuhai-1	Spectral and red-edge features	Spectral resolution can reach the nanometer level.	Large data volume with redundant information	S. alterniflora detailed mapping	COC (***) CAC (***)
SAR data	Sentinel-1, GF-3	Polarization, texture and phenological features	All-weather, day-and-night observation; strong penetration capability	Complex explanation; noise interference	Dynamic change monitoring; adaptable to environments with frequent cloud cover and tidal fluctuations	COC (***) CAC (**)
LiDAR		Elevation information	Acquire high-precision 3D data	Limited temporal and spatial coverage	Habitat classification; auxiliary data	COC (***) CAC (***)
UAV	Multispectral, Hyperspectral	Spectral, spatial and phenological features	High spatial resolution and strong flexibility	Limited temporal and spatial coverage	Small-scale high-precision mapping	COC (***) CAC (***)
Traditional statistical methods	MLC		Theoretically mature and computationally straightforward	Assume normal distribution; limited precision	Low-dimensional feature classification for early mapping of S. alterniflora distribution	COC (*) SAC (**)
Shallow machine learning	DT		Highly interpretable and easy to implement	Prone to overfitting; poor generalization capabilities	Low-dimensional feature classification	COC (*) SAC (**)
	SVM		Suitable for high-dimensional features, with high accuracy	Parameter tuning is complex, and computation slows down as the sample size increases.	Spectral + texture features, Object-based image analysis	COC (*) SAC (*)
	RF		Highly robust, capable of handling high-dimensional/ multi-source features, and highly accurate.	Black box problem	Large-scale distribution mapping, multi-source feature fusion	COC (**) SAC (**)
	XGBoost		Highly efficient, suitable for large-scale data, and capable of handling strong nonlinear relationships.	Numerous parameters; complex parameter tuning	Precision mapping	COC (***) SAC (***)
Deep learning	CNN, Transformer		Automatic feature extraction, suitable for high-dimensional complex data, with high accuracy.	Black box problem; difficult to run on cloud platforms	High-resolution image classification for complex tidal flat environment monitoring	COC (***) SAC (***)
	Transfer learning		Mitigate the issue of insufficient samples, enabling cross-regional and cross-temporal transferability.	Limited model generalization	Long-term monitoring of time series data; cross-regional mapping	COC (***) SAC (*)

serves to summarize the advantages, limitations, application scenarios, and cost requirements of different remote sensing data sources and classification algorithms.

3.3. Ecological Parameter Retrieval

3.3.1. Key Ecological Parameters for Quantitative Retrieval of S. alterniflora

The ecological parameters of S. alterniflora serve as crucial foundations for understanding its physiological state, constructing productivity models, and evaluating ecological effects, while also forming the basis for carbon cycle and climate change research. Quantitative inversion parameters primarily encompass two categories: biophysical parameters (such as biomass, vegetation cover (FVC), and leaf area index (LAI)) and biochemical parameters (such as chlorophyll and water content).

Among these parameters, biomass was the earliest indicator to be studied [69] and has attracted the most research attention (Figure 5). Existing research has primarily focused on aboveground biomass (AGB), revealing its spatiotemporal variations by comparing different health states and seasonal differences [70]. Related work also addresses the impact of canopy height on biomass estimation [71] and the underlying mechanisms [72]. However, due to limitations in field sampling, studies on belowground biomass (BGB) remain relatively scarce. Currently, BGB estimation primarily employs indirect methods, which involve establishing biomass allocation relationships based on spectral characteristics and AGB for prediction [73]. However, the fundamental limitation of optical signals lies in their inability to penetrate soil or water bodies. L-band SAR, with its longer wavelength, can capture subsurface information in wetland environments, potentially opening new avenues for accurate biomass estimation. Building upon LAI inversion techniques, researchers have proposed the emergence leaf area index (ELAI) to mitigate tidal interference [74]. Moreover, effective exploration has been conducted on FVC and canopy height inversion [75,76].

With the continuous advancement of remote sensing technology and modelling techniques, research has gradually expanded to include biochemical parameters. Canopy chlorophyll content is widely used to characterize photosynthetic capacity and physiological status [77], while leaf functional traits are increasingly receiving attention [78], reflecting adaptive strategies of S. alterniflora to environmental changes [79].

3.3.2. Remote Sensing Data Sources for Ecological Parameter Retrieval

Compared to traditional field experiments, remote sensing-based methods for deriving ecological parameters offer greater efficiency and non-destructive operation. Early research primarily relied on spectral radiation data. In recent years, the potential of active remote sensing has gradually become apparent, and multi-source data fusion has emerged as the key to enhancing accuracy and applicability.

Statistical results indicate that medium-resolution multispectral satellite data, UAV-based LiDAR data, and high-resolution satellite data represent the three most widely used data sources (Figure 5). Among medium-resolution datasets, Landsat imagery was the earliest applied to biomass estimation of S. alterniflora [69]. Vegetation indices constructed from multispectral bands can be used to estimate biomass and distinguish plant health status [35,80]. The feasibility of the near-infrared vegetation reflectance index derived from Sentinel-2 data in predicting ELAI has been further validated [74]. High-resolution satellites demonstrate greater applicability in salt marsh environments where mixed pixels are prevalent by capturing finer spatial heterogeneity [81].

Hyperspectral data can capture subtle features that cannot be distinguished by multispectral data. The combination of multiple indicators outperforms a single index, with average R² increasing by 7.5–10.78% and RMSE decreasing by 10.38–16.56% [82,83]. Combining UAV-hyperspectral imagery with the FIRST algorithm significantly improves LAI inversion accuracy, with R² values increasing by up to 41.7% [84]. A significant phenological disparity in biomass has been observed between S. alterniflora and other vegetation [70]. However, the biomass exhibits significant phenological variations, with the lowest estimation accuracy occurring in November (R² = 0.57, RMSE = 228.42 g/m²) [85].

In terms of active remote sensing applications, HV backscatter intensity is significantly correlated with the live/dead biomass ratio [35]. In canopy structure parameter inversion, the HV/VV ratio alone explained 49% of the variance in LAI, while the overall model explained 77% [34]. Airborne LiDAR outperforms Digital Aerial Photogrammetry (DAP) in vegetation structure reconstruction [15], and when combined with RGB or hyperspectral data, significantly improves biomass estimation [71,86].

3.3.3. Models and Approaches

The remote sensing data sources for ecological parameter inversion of S. alterniflora have been continuously enriched, while the inversion models have also undergone continuous evolution (

Table 2. Summary of biological parameters, remote sensing inversion algorithms and related references. “E” is the abbreviation for empirical model, “P” is the abbreviation for physical model and “ML” is the abbreviation for machine learning.

Type	Model	Data	Object	Reference
E	Linear regression	Landsat-4	Live and dead biomass	[69]
	Simple and multiple linear regression	LiDAR	Vegetation height Planimetric obscuration Stem density Biomass density	[87]
	Mixed-effects models	WorldView-2	Peak green biomass	[81]
	Multiple linear regression models	Sentinel-2	AGB	[88]
	Linear regression	Sentinel-2	ELAI; eddy covariance carbon fluxes	[74]
P	PROSAIL	High-spatial resolution hyperspectral imagery	AGB	[89]
	Soil-Canopy-Observation of Photochemicals and Energy fluxes (SCOPE)	Landsat-8, Sentinel-2, and RapidEye	LAI, leaf chlorophyll content (C (ab)), and fraction of absorbed photosynthetically active radiation (fPAR)	[90]
ML	RF	Landsat-7 and Landsat-8	AGB	[91]
	BERM (Belowground Ecosystem Resiliency Model)	Landsat-8	BGB	[92]
	Ensemble learning model (ELM)	Landsat-8	Fractional cover	[93]
	Optimized support vector regression (OSVR); optimized random forest regression (ORFR); optimized extreme gradient boosting regression (OXGBoostR)	UAV hyperspectral imagery	LAI	[84]
	Multivariate stepwise regression (MSR), BP neural network (BP), RFR models	UAV hyperspectral imagery	AGB	[85]
	RF, SVM, XGBoost, and back propagation neural network (BPNN)	Hyperspectral data and FS4 portable ground object spectrometer	Leaf functional traits: Moisture content (MC), Soil plant analysis development (SPAD), Specific leaf area (SLA), Total nitrogen (TN), Total phosphorus (TP), Total carbon (TC)	[78]
	RF, SVM, XGBoost	Sentinel 2 and Landsat-5	Soil organic carbon (SOC)	[94]
	RF, SVM, XGBoost	Multispectral imagery and LiDAR data	SOC	[95]

). According to whether physical mechanisms are incorporated, inversion models are generally categorized into empirical models, which rely on statistical relationships derived from observational data, and physical models, which are based on physical principles.

Empirical models establish statistical relationships between vegetation parameters and remote sensing signals, providing a straightforward approach for deriving biophysical parameters of S. alterniflora (

Table 3. Common estimation models for ecological parameter retrieval and their features.

Method Type	Estimation Model	Features
Empirical model	Regression models	Wide applicability, simple operation, and easy to implement.
	Mixed-effects models	Consider the spatial dependence and hierarchical data structure of samples.
	Machine learning	Strong nonlinear fitting ability and relatively high accuracy, but prone to overfitting.
Physical model	Radiative transfer model	Parameters have a clear physical meaning.
	PROSAIL model	Model has practical significance, includes many parameters, theoretical explanation is difficult.
	CASA model	Strong applicability, fewer parameters, with practical ecological meaning.

). Early studies predominantly employed linear regression, later expanding to include stepwise regression [34], polynomial regression [86], and mixed-effects models [81], thereby enhancing the ability to capture nonlinear and spatial dependencies. Different statistical models have distinct data assumptions and application scenarios. For example, while traditional regression models feature a simple structure and easy construction, they require data to satisfy strict assumptions such as independent residuals and homogeneous variance. In contrast, mixed-effects models are better suited to handle sampling data with nested structures, but their extrapolation ability and generalizability still require further validation.

With the rapid advancement of artificial intelligence technologies, machine learning models have significantly enhanced nonlinear modelling capabilities and robustness to noise. Research indicates that ANN outperform single exponential models in chlorophyll inversion [57]. The Belowground Ecosystem Resiliency Model (BERM), based on XGBoost achieves an R² of 0.62–0.77 for estimating BGB [96]. Among various algorithms, RF is the most widely applied and demonstrates stable performance [78,94], while XGBoost achieves higher accuracy in SOC prediction [95]. For example, in the biomass prediction model for S. alterniflora constructed using UAV imagery, the RF model demonstrated the highest accuracy (R² = 0.64, RMSE = 0.321 kg/m²), outperforming both the XGBoost and Bayes Regression Tree models [97]. Furthermore, ensemble learning methods (ELM) can further enhance the accuracy of salt marsh vegetation cover and biomass inversion [84,93].

In contrast, physical models simulate canopy–radiation interactions, thus explaining vegetation reflectance more fundamentally (Table 3). The PROSAIL model has been successfully applied to biomass inversion in S. alterniflora salt marshes (R² = 0.73) [89], while the PROSAIL-D model demonstrates higher accuracy in chlorophyll estimation [98]. The DART and SCOPE models can be used to optimize parameter retrieval, enhancing the reliability of LAI and photosynthetic parameter estimation [90,99]. In large-scale applications, CASA is widely used for productivity and carbon stock assessments due to its ease of parameterization [100,101]. In addition to data and model quality, sufficient in situ observations are essential. Generative adversarial networks with constraint factor models (GAN-CF) are advantageous for addressing the issue of sample scarcity in biomass estimation [88].

3.4. Ecosystem Function and Service

S. alterniflora constitutes a vital component of coastal blue carbon ecosystems, exhibiting dual ecological effects. On the one hand, as a potential biofuel resource, it possesses carbon sequestration capabilities [102] and to some extent serves to resist wave erosion and protect coastlines [103,104]. On the other hand, it also exerts adverse effects on local climate and biodiversity. The dynamic monitoring and assessment of S. alterniflora ecosystem functions and services using remote sensing technology has become a hot topic in recent years.

3.4.1. Productivity and Carbon Storage Monitoring

S. alterniflora possesses high biomass and vegetation density, constituting a significant component of coastal wetland carbon sinks [99]. Traditional productivity and carbon stock assessments rely on wetland sampling and literature reviews [102], whereas advances in remote sensing technology have significantly enhanced estimation efficiency. The MOD17 GPP product provided by MODIS serves as the primary data source for studies on total primary productivity and net primary productivity in S. alterniflora salt marshes [100,105]. However, the MOD17A2 model based on Light Use Efficiency fails to adequately account for the seasonal dynamics of S. alterniflora and the impact of tidal environments on gross primary production (GPP). Process-based vegetation models and NDWI-based GPP models demonstrate superior performance in predicting GPP and CO₂ flux [106].

In carbon stock estimation, soil carbon requires high spatial resolution remote sensing data, and thus Sentinel-2 and UAV imagery (with resolutions finer than 10 m) are widely employed [107,108]. Meanwhile, Sentinel-1 data, owing to its strong penetration capability, has also become a crucial input for carbon stock estimation models [109]. By inputting land use/land cover maps derived from remote sensing data along with relevant carbon parameters into the InVEST blue carbon model, dynamic simulation of carbon stocks can be achieved [110]. However, this approach demands a substantial quantity and high quality of carbon stock samples, and the availability of such samples significantly constrains the accuracy and coverage of spatial carbon stock mapping. In recent years, machine learning and deep learning have effectively improved the accuracy of carbon stock mapping and mitigated the issue of insufficient samples [94].

However, the carbon effects of S. alterniflora expansion are not uniformly positive. The invasion may reduce carbon storage and increase methane emissions, thereby weakening the function of wetlands in mitigating climate warming [111]. Therefore, S. alterniflora may function both as a carbon sink and a carbon source, while remote sensing plays a crucial role in the comprehensive assessment of regional carbon cycles.

3.4.2. Coastal Protection and Hydrological Regulation

S. alterniflora plays a significant role in coastal protection and hydrological regulation. Research indicated that S. alterniflora can reduce wave energy and promote sediment deposition, thereby enhancing coastal stability and resilience against storm surges [112,113]. Quantitative analysis based on indicators such as NDVI, MNDWI, and FVC, along with the DSAS and EISW models, indicates that a S. alterniflora distribution belt width of approximately 700 m is required to effectively mitigate erosion and stabilize the shoreline [114].

Advances in remote sensing classification reveal the coupled effects of S. alterniflora invasion and sedimentary environments. While the invasion initially increases sediment nutrients, it subsequently significantly reduces total carbon, nitrogen, and phosphorus content in sediments [115,116]. High-resolution imagery and GIS network analysis further reveal the interaction between invasion patterns and tidal channels. Increased S. alterniflora density correlates with slowed tidal channel development [105], and highly efficient drainage and structurally meandering tidal channels further facilitate S. alterniflora expansion [117,118]. Simulation results combining airborne LiDAR and multibeam bathymetry data also indicate that salt marsh vegetation dominated by S. alterniflora plays a crucial role in regulating water flow velocity and drainage patterns [119].

3.4.3. Biodiversity and Habitat Provision

The strong competitive ability of S. alterniflora leads to habitat reduction for native dominant species and triggers regional declines in species diversity. Utilizing high-resolution imagery from WorldView, GF-2 and unmanned aerial vehicles enables the identification of its spatial patterns with vegetation such as Scirpus mariqueter, Suaeda salsa and mangroves at a fine scale, thereby facilitating inference of habitat evolution processes [8,18]. Relevant studies have demonstrated a reciprocal constraint between S. alterniflora and mangroves. Using medium- to high-resolution data such as Sentinel-2 and Landsat on the GEE platform, researchers quantified the relationship between mangrove fragmentation and S. alterniflora invasion. The results revealed that mangroves with small sizes, large edge proportions, and regular boundary shapes were more susceptible to invasion, while S. alterniflora, to some extent, suppressed mangrove expansion [120,121]. In comparison, drone imagery enables more detailed assessment of invasion risks, revealing that mangrove vegetation cover exceeding 65% significantly suppresses S. alterniflora invasion [18].

In recent years, remote sensing data has also provided robust support for assessing the impact of S. alterniflora expansion and degradation on the quality of bird habitats [122]. However, most such studies rely on remote sensing to reveal the impact of S. alterniflora on native vegetation distribution, thereby inferring its effects on birds, without analyzing the direct responses of bird populations. Using long-term Landsat image series, researchers dynamically monitored the relationship between S. alterniflora and shorebird habitats, and found that its invasion weakened the positive effects of protected areas on shorebird habitats [114]. However, recent long-term monitoring of S. alterniflora indicates that as its coverage decreases, bird species richness and abundance first increase and then decline [123].

To further clarify the research paradigm and the specific role of remote sensing in assessing these ecosystem services, we summarize the relationship between data acquisition, the quantitative role of remote sensing products, and the necessary integration of field-based validation in

Table 4. Integration of remote sensing and field data for assessing S. alterniflora ecosystem services.

Function Assessed	RS Input/Application	Critical Integration Required
GPP Estimation	Direct use of MODIS GPP products.	Integration with process/ecosystem models for simulation.
Carbon Storage	Generation of precise distribution maps.	Essential integration of field soil data and deep learning models/ process models.
Tidal Channel Impact	RS mapping of S. alterniflora/channel distribution.	Quantification via structural relationship models using extracted morphological parameters.
Biodiversity Impact	RS mapping of S. alterniflora/native vegetation.	Quantification of spatial structure to assess impact.
Bird Habitat Impact	RS mapping of S. alterniflora/native vegetation.	Integration with field bird observation data (richness/abundance).

.

4. Discussion

4.1. Driving Forces of S. alterniflora Remote Sensing Applications

A statistical analysis of 215 publications reveals a significant growth trend in remote sensing applications for S. alterniflora over the past two decades, with research themes gradually expanding from distribution monitoring to mechanism analysis and ecosystem service assessment. As shown in Figure 6, this evolution has been primarily driven by the combined expansion of data sources and methodological advancements.

Early studies relied on aerial imagery and medium-resolution data such as Landsat [24,124], employing methods primarily based on visual interpretation, maximum likelihood classification, and simple regression models [31,125], with a relatively limited scope of topics. Since 2010, the application of high-resolution satellites [26,76], LiDAR [87], and hyperspectral sensors [71] has greatly enriched spatial and spectral information. Classification methods have progressively evolved to include DT [26], SVM [126], and physical models [127], significantly improving accuracy while simultaneously advancing biomass and LAI inversion.

Entering the 2020s, the advent of open-access Sentinel-1/2 imagery [128], high-resolution UAV imagery [129], cloud computing platforms such as GEE [9], and the widespread application of machine learning and deep learning have propelled research into a new era of multi-source data fusion and intelligent processing. In terms of research methodology, multi-temporal imagery supported phenological feature extraction [51], deep learning enhanced complex scene recognition [14], and transfer learning mitigated the issue of insufficient samples [47,67]. Research scope was further expanded to include BGB [92], functional traits [78], and carbon stock estimation [130].

Overall, research on S. alterniflora remote sensing has evolved from being constrained by data and methodology to becoming data-driven, and finally to being driven by the synergistic integration of data and methodology.

4.2. Challenges and Prospects of Spatial and Dynamic Monitoring

In recent years, remote sensing technology has driven significant advances in monitoring the distribution and dynamics of S. alterniflora, and research is shifting to large-scale, long-term monitoring [13,131]. Medium-to-high resolution data such as Sentinel-1/2, Landsat, and the Gaofen series can meet certain identification requirements. However, under mixed vegetation and tidal interference, mixed pixels still limit mapping accuracy. Deep learning, with its powerful feature extraction capabilities, enables the development of targeted processing strategies, but relies on large-scale labeled samples. Therefore, alongside advancing transfer learning, there is an urgent need to establish a large-scale remote sensing interpretation dataset for S. alterniflora.

The expansion-removal-regeneration-retreat cycle of S. alterniflora constitutes a complex multistage process. However, current research lacks long-term tracking of its complete life cycle, with particularly insufficient quantitative assessments of land cover changes following removal. High-resolution remote sensing enhances extraction accuracy [17], but interannual monitoring struggles to capture seasonal variations and harvesting events, whereas high-frequency time series offer distinct advantages [131]. Future global long-term mapping requires further development of noise filtering algorithms adapted to intertidal environments and enhanced research on multimodal data fusion to support multi-source big data applications.

4.3. Challenges and Prospects of Structural and Functional Monitoring

Remote sensing shows significant potential in estimating AGB, LAI, and carbon storage of S. alterniflora, but research on biomass and leaf functional traits remains insufficient (Table 2). Carbon flux estimation and productivity mapping provide new insights into the role of S. alterniflora in blue carbon ecosystems [123]. Although the dual effects of S. alterniflora on the carbon cycle have garnered increasing attention [22,132], existing research remains fragmented and lacks comprehensive analysis across scales and regions. The core challenge in large-scale ecological parameter inversion lies in achieving high-precision modeling that accounts for varying climatic drivers and geographical conditions. Due to varying sample collection conditions across different regions and the spatiotemporal dynamics of the relationship between remote sensing signals and ecological parameters, the model struggles to achieve cross-regional generalization [133].

In terms of algorithms and models, machine learning and deep learning offer strong nonlinear modeling capabilities, but the “black box” problem limits their interpretability [134]. Therefore, it is necessary to integrate data-driven artificial intelligence methods with expert knowledge, incorporating physical model constraints into the learning process to enhance model interpretability and deepen our understanding of the relationships between remote sensing data, vegetation structure, and leaf physiological characteristics.

Future research should explore the ecological effects of Spartina dynamics based on large-scale mapping. For instance, using the hydrological sensitivity of SAR to reveal its hydrological regulation role; extracting water quality parameters (such as suspended solids, chlorophyll-a, dissolved organic matter) to assess the impacts of expansion and retreat on nearshore waters; and combining high-resolution imagery, landscape indicators, and ecological models to quantify the effects of patch dynamics on habitat quality and biodiversity, thereby providing decision support for conservation and restoration.

4.4. Governance Framework Based on Remote Sensing

In 2023, the Chinese government issued the “Special Action Plan for the Prevention and Control of Spartina Alterniflora (2022–2025)”, launching an unprecedented large-scale eradication campaign against S. alterniflora across coastal regions nationwide. This initiative has generated new demands within the field of remote sensing monitoring. Traditional studies have mainly focused on mapping invasion ranges and monitoring the spread process. However, with the large-scale implementation of control actions, the tasks of remote sensing have expanded to accurately tracking the timing of S. alterniflora removal, quantifying the cleared areas across regions, and further evaluating the spatiotemporal effectiveness of different management measures [135]. Furthermore, attention must be paid to the chain reaction triggered by the removal of S. alterniflora. The rate of native vegetation recovery post-removal, the risk of Spartina regrowth, the reshaping of wetland landscape patterns, and the dynamic changes in carbon sink functions all constitute scientific issues requiring urgent consideration [8].

This complex evolutionary process necessitates high spatio-temporal resolution and multi-source remote sensing monitoring to achieve dynamic, continuous and cross-scale quantitative assessment. Therefore, future research should place greater emphasis on leveraging the long-term observational advantages of Sentinel, Landsat, and high-resolution satellite series, combined with UAVs and ground surveys, to achieve systematic tracking of clearance effectiveness and ecological feedback. At the methodological level, the rapid advancement of machine learning and deep learning algorithms presents new opportunities for monitoring governance effectiveness. By constructing specialised classification models for clearance identification, sensitivity and discriminative power regarding small-scale changes can be significantly enhanced [135]. This approach not only quantifies “the extent of clearance” but also reveals “ecological changes following clearance”. Furthermore, by integrating night-time remote sensing data with socio-economic information, it is possible to examine the relationship between various governance measures and human activities, thereby enabling a multidimensional comprehensive assessment of governance effectiveness.

Remote sensing technology holds the potential to serve as a core supporting force across all stages of the process: invasive species identification, risk assessment, governance implementation, effectiveness evaluation, and ecological restoration. Future applications should be strengthened in the following aspects: During the early stages of management, remote sensing technology should be used to identify high-risk areas for S. alterniflora invasion, providing a reference for management actions. In the mid-term of management, the continuous change monitoring capability of remote sensing technology can be used to identify removal areas and compare the effectiveness of different management measures, which enables quantitative evaluation of governance outcomes. In the later stages of S. alterniflora management, long-term monitoring of vegetation recovery and ecological function changes can provide scientific decision support for ecological restoration. Therefore, the unique advantages of remote sensing in cross-scale monitoring, comprehensive assessment and scientific decision-making should be fully leveraged to achieve the sustainable management of S. alterniflora and the long-term conservation of coastal wetlands.

5. Conclusions

This paper analyzed 215 peer-reviewed articles on remote sensing applications for S. alterniflora, summarizing key advances in distribution and dynamics monitoring, ecological parameter inversion, and ecosystem assessment. We found that Relevant research has become increasingly global in scope yet remains unevenly distributed, with the focus shifting from static mapping to dynamic process monitoring and functional assessment. Remote sensing technology has demonstrated remarkable effectiveness in large-scale mapping and fine-scale identification. Multi-source data and models effectively support the inversion of parameters such as biomass and LAI, as well as carbon stock estimation, and can quantitatively assess their impacts on coastal protection, hydrological regulation, and biodiversity. However, challenges remain, including the accurate detection of mixed and flooded pixels, insufficient intra-annual dynamic monitoring, and limited inversion of underground biomass and leaf functional traits. Future efforts should focus on enhancing both short-term and long-term dynamic monitoring, strengthening the integration of multi-source data and model coupling, establishing sample databases, and expanding impact assessments at the ecosystem scale. This study provides valuable insights for future research directions and coastal wetland management practices.