Evaluation and Driving Analysis of Eco-Environmental Quality in Guangdong Province Based on an Improved Water Benefit-Based Ecological Index

Abstract

As Guangdong is a pivotal province in China’s national forest city initiative, examining the spatiotemporal evolution and key drivers of eco-environmental quality (EEQ) in Guangdong is essential for advancing regional sustainable development. To address the complexity of EEQ assessments in areas that are characterized by dense hydrological networks, extensive vegetation cover, and rapid urban expansion, the Google Earth Engine platform was utilized in this study, and remote sensing indices with heightened sensitivity to vegetation and moisture dynamics—namely, the kernel normalized difference vegetation index and the kernel normalized difference moisture index—were introduced to develop an improved water benefit-based ecological index (ImWBEI). Through an integrated analytical framework incorporating Theil–Sen trend analysis, Mann–Kendall significance testing, Hurst exponent analysis, an optimal parameter-based geographical detector, and a coupled coordination degree model, this research systematically evaluated the spatiotemporal patterns, future trends, driving mechanisms, and coordination with urbanization of the EEQ in Guangdong from 2000 to 2021. The results demonstrated that the ImWBEI enhanced the detailed characterization of complex underlying surfaces, such as urban built-up areas and land–water transition zones. Throughout the study period, the EEQ in Guangdong displayed a stable spatial distribution characterized by higher values in the north and lower values in the south. Concurrently, the EEQ significantly improved at a rate of 0.0092 per year. Hurst index analysis indicated that this trajectory would likely persist, with the future trend dominated by a pattern of weak persistent improvement. The comprehensive urbanization index was identified as the most critical factor influencing the spatial differentiation of the EEQ in Guangdong. Although notable north–south disparities were observed in the coordination between the EEQ and comprehensive urbanization, the provincial-level coupled coordination consistently improved. Consequently, this work yielded actionable insights and a replicable framework for ecological monitoring and coordinated development in similar water–forest integrated urban regions. It was particularly relevant for informing ecological restoration prioritization and development restriction decisions in critical land–water transition zones—areas where the ImWBEI demonstrated enhanced sensitivity.

1. Introduction

The synergistic relationship between economic growth and eco-environmental quality (EEQ) has become a central focus in sustainability science worldwide [1,2,3]. Guangdong Province, one of China’s most economically vibrant and highly open regions, has experienced rapid industrialization and urbanization while concurrently confronting growing pressures from resource depletion and constrained environmental capacity [4,5]. Historically, its extensive growth model, driven primarily by scale expansion, contributed to declines in the ecological carrying capacity in certain areas, exacerbating tensions between economic development and environmental protection [6]. In response, in recent years, Guangdong has pursued a green transition in its development pathway, prioritizing ecological quality improvement while sustaining robust economic growth. To date, the province has established the country’s largest network of national forestland cities and is advancing the development of a comprehensive regional forestland city cluster. This shift reflects an ongoing transition from scale-driven expansion towards quality-oriented and ecologically integrated development [7,8]. As a leading province in China’s overall economic competitiveness, Guangdong’s integrated development model offers significant illustrative value [9,10,11]. In this context, scientifically rigorous monitoring and assessment of the spatiotemporal evolution of the EEQ in Guangdong—coupled with a systematic analysis of its driving factors—are essential for understanding the dynamics within regional economic–environmental systems and for informing evidence-based policies for ecological civilization and sustainable development [12].

Assessing the regional EEQ is fundamental to ecological research and environmental management. Traditional methods, which are largely dependent on statistical data, are often constrained by low spatial resolution and infrequent updates, limiting their applicability for large-scale, high-frequency dynamic monitoring [13]. Advances in Earth observation systems have established remote sensing as a central approach for dynamic monitoring and integrated assessment of regional EEQ. Globally, a diverse array of remote sensing-based indices and methodological frameworks has been developed to monitor and assess EEQ and its components. Current remote sensing-based assessment approaches primarily follow two paradigms. The first employs single-factor indices, such as the normalized difference vegetation index (NDVI), land surface temperature (LST), and leaf area index (LAI) [14,15,16]. Although straightforward, such indices often fail to capture the integrated state of EEQ—a complex outcome of multiple natural and anthropogenic factors [17]. The second paradigm uses multi-indicator comprehensive evaluation frameworks. For instance, the ecological index (EI), introduced by China’s former Ministry of Environmental Protection, integrates measures of biological abundance, vegetation cover, water network density, land stress, and pollution load and has been widely applied in annual ecological assessments at the county level and above [18,19,20]. Similar frameworks, such as ecological security evaluations based on pressure–state–response models and integrated assessments of habitat quality and ecosystem services, have also been extensively adopted. Nevertheless, these comprehensive systems exhibit several practical limitations, including difficulties in acquiring baseline data (e.g., biodiversity inventories), subjectivity in weighting methods, such as expert scoring and the analytic hierarchy process, and restricted spatial granularity and visual expressiveness when administrative units serve as the primary evaluation scale.

To overcome these shortcomings, researchers have developed remote sensing-driven multi-indicator fusion methods. Among these, the remote sensing-based ecological index (RSEI) couples four key indicators—greenness, wetness, dryness, and heat—via principal component analysis. This approach reduces the reliance on subjective weighting and enables rapid, objective, and spatially explicit assessments of regional EEQ values, contributing to its widespread adoption. However, in standard RSEI construction, water pixels are typically masked or labeled as unclassified. As a result, in regions with dense river networks and numerous lakes, the RSEI does not adequately represent the critical role or spatial heterogeneity of aquatic ecosystems. The aquatic environment is a key determinant of urban ecological health and sustainable development, underscoring the importance of quantifying and spatializing its ecological benefits [21]. In the field of water body assessment, indices such as the normalized difference water index (NDWI) and the index of spatial water stress (IWS) [22] have been used to map surface water dynamics. Integrated approaches combining remote sensing and local ecological knowledge have also supported water resource planning in the Chewaucan River Basin, Oregon, USA [23]. Moreover, an improved nonlinear remote sensing ecological index (NRSEI) [24], which integrates the temperature vegetation dryness index (TVDI) with kernel principal component analysis, has shown enhanced sensitivity to ecological changes in wetlands. To address the need for incorporating water-related benefits, Jiao et al. [25] proposed the water benefit-based ecological index (WBEI). Focused on surface potential water abundances and using information entropy to determine the indicator weights, the WBEI quantifies the contribution of the water environment to the regional EEQ, with validation in case studies, such as Qingdao and Erhai [26]. Building on this foundation, the improved water-benefit-based ecological index (IWBEI) [27] incorporated air quality indicators and demonstrated superior performance in assessing the EEQ of farmlands and wetlands. Concurrently, methodological advancements in remote sensing indices have progressed substantially. For example, compared with traditional linear indices, the integration of kernel functions to model the nonlinear relationships between remote sensing reflectance values and ecological parameters has been shown to increase sensitivity, noise resilience, and practical applicability. In this context, Luo et al. [28] replaced the ratio vegetation index (RVI) within the WBEI framework with the kernel normalized difference vegetation index (kNDVI), resulting in a more robust amended water benefit-based ecological index (AWBEI). Similarly, the enhanced water benefit-based ecological index (SWBEI) [21] was developed for arid regions by integrating the kNDVI with a salinity index. The emergence of nonlinear remote sensing indices—such as the kNDVI and the kernel normalized difference moisture index (kNDMI)—presents promising opportunities to improve the accuracy of comprehensive EEQ assessments. However, despite the demonstrated sensitivity of nonlinear indicators such as kNDVI and kNDMI in monitoring ecological dynamics, their full potential for capturing water–vegetation synergies in heterogeneous landscapes—particularly in regions characterized by dense hydrological networks and rapid urbanization—remains underexplored. Further research is needed to more fully integrate these nonlinear indicators to comprehensively capture the complex relationships between water-related benefits and EEQ comprehensively.

Guangdong Province represents a typical region with a dense water network, characterized by well-developed river systems, an extensive coastline, and abundant wetland resources. Aquatic ecosystems play fundamental and connective roles in material cycling, energy flow, and biodiversity maintenance in such landscapes [29,30]. Therefore, the systematic incorporation of aquatic ecological factors is essential for a comprehensive assessment of the regional EEQ in Guangdong. Furthermore, the spatiotemporal dynamics and future evolutionary trajectory of the province’s EEQ remain to be systematically quantified. Existing studies still exhibit limitations in accurately quantifying aquatic ecological benefits and elucidating the complex drivers underlying the spatial heterogeneity in EEQ. The main contributions of this study are as follows.

• An improved water benefit-based ecological index (ImWBEI) was developed by integrating internationally recognized kernel-based indices (kNDVI, kNDMI) that exhibit heightened sensitivity to moisture and vegetation activity. This improved composite index was designed to better represent regional hydro-ecological conditions and to capture synergistic water–vegetation effects.
• Long-term dynamic monitoring and spatial pattern analysis of EEQ were performed. Using the ImWBEI, the spatial differentiation and changing trends of the EEQ in Guangdong from 2000 to 2021 were analyzed, and a data-informed basis was established for understanding regional ecological processes.
• Driving mechanisms and coupling relationships were systematically examined. The independent and interactive effects of climate, topography, and urbanization on spatial heterogeneity were quantified. The coupling coordination degree model was further applied to dynamically assess the interaction between the EEQ and key factor subsystems.

By systematically revealing the evolutionary patterns, driving factors, and human–environment coupling of the EEQ in Guangdong, this study aims to provide a scientific basis and decision-making support for balancing ecological conservation with high-quality regional development.

2. Materials and Methods

2.1. Study Area

Guangdong Province (20°09′–25°31′ N, 109°45′–117°20′ E) is located on the southern coast of China, covering a land area of approximately 179,800 km². Bordered by the South China Sea to the south and adjacent to the Hong Kong and Macao Special Administrative Regions, it serves as a crucial gateway for international exchange. Topographically, the terrain descends from north to south, transitioning from mountainous and hilly regions in the north to predominantly plains and terraces in the south. Located within the East Asian monsoon zone, the climate ranges from central subtropical in the north to southern subtropical and tropical in the south. The mean annual temperature varies between 19 °C and 24 °C and generally increases southwards. Precipitation is highly seasonal, with most rainfall occurring from April to September, yielding an annual average of approximately 1789 mm. The annual sunshine duration ranges from 1500 to 2300 h and increases from north to south. The province possesses a dense river network, with more than 500 rivers having catchment areas exceeding 100 km². Forest coverage is extensive, reaching 53.39%, which underpins a relatively sound ecological baseline, high biodiversity, and significant ecological water demand. Functionally, Guangdong comprises several core regions with distinct developmental and ecological roles. The Pearl River Delta (PRD), in the central-southern part, is a global manufacturing and innovation hub, closely integrated with Hong Kong and Macao within the Guangdong–Hong Kong–Macao Greater Bay Area. To the north, the mountainous region functions as a critical ecological conservation area, designated as the province’s primary water source protection zone. Flanking the PRD to the east and west, the coastal areas of eastern and western Guangdong are designated as strategic growth poles to rebalance regional development and enhance connectivity within the Greater Bay Area.

2.2. Data Acquisition and Processing

MODIS products provide stable, continuous global observations since 2000, offering surface reflectance and temperature data across multiple bands from the visible to the thermal infrared spectrum. These data are suitable for constructing indicator systems that reflect multidimensional ecological characteristics, such as vegetation, moisture, and thermal environments. In this study, the Google Earth Engine (GEE) platform (https://earthengine.google.com) (accessed on 2 August 2025) was used to acquire and process surface reflectance (MOD09A1) and land surface temperature (MOD11A2) products for Guangdong Province for 2000 to 2021 to calculate the ImWBEI. Both MOD09A1 and MOD11A2 have temporal resolutions of 8 days, with native spatial resolutions of 500 m and 1000 m, respectively. To ensure spatial consistency, MOD09A1 data were resampled to a 1 km spatial resolution using the bilinear interpolation method within the GEE, aligning them spatially with the MOD11A2 data.

Changes in EEQ are influenced by both natural factors and anthropogenic activities. In this study, meteorological factors, topographic factors, soil type, and a comprehensive urbanization index were selected for geographical detector analysis to identify the drivers of the spatial differentiation in the EEQ. The meteorological data, including the average temperature (T), cumulative precipitation (CP), and potential evapotranspiration (PE), were obtained from the National Earth System Science Data Center (https://www.geodata.cn) (accessed on 10 June 2025). Topographic factors—elevation (E), slope (S), and aspect (A)—were derived from the terrain dataset for the year 2000 released by the National Qinghai-Tibet Plateau Science Data Center (https://data.tpdc.ac.cn) (accessed on 19 September 2025). The soil type (ST) data were acquired from the Resource and Environmental Science and Data Platform (https://www.resdc.cn) (accessed on 21 September 2025). To systematically evaluate the impact of urbanization on the EEQ, population density, built-up land area, and nighttime light intensity were chosen as proxy indicators for demographic, land-use, and economic urbanization, respectively. A comprehensive urbanization index (CU) was constructed through range normalization and equal-weight aggregation. Population density data were sourced from the LandScan Global Population Dynamics Dataset published by Oak Ridge National Laboratory, U.S. Department of Energy (https://landscan.ornl.gov) (accessed on 25 September 2025). The built-up land area was extracted from the annual high-resolution land cover product for China developed by Yang et al. [31]. Nighttime light intensity data were drawn from the long-time-series satellite-based light dataset compiled by Wu et al. [32], which was accessed via the Harvard Dataverse platform (https://data.harvard.edu) (accessed on 1 October 2025). All the datasets were uniformly resampled to a spatial resolution of 1 km to ensure analytical consistency.

2.3. Development of the ImWBEI

To address the recognized limitations of the traditional WBEI—including its potential insensitivity to vegetation canopy water content and susceptibility to spectral saturation in regions characterized by high vegetation density—this study developed an improved water benefit-based ecological index (ImWBEI). The core advancement of the ImWBEI involves the integration of remote sensing indicators that exhibit enhanced sensitivity and greater resistance to saturation for both vegetation and moisture. This formulation enables a more precise characterization of the spatial heterogeneity in key ecological parameters, thereby supporting a more robust and comprehensive assessment of the regional EEQ.

Specifically, the ImWBEI framework synthesizes five core indicators across three complementary dimensions: moisture, vegetation, and environmental stress. For the moisture component, the kNDMI and the surface potential water abundance index (SPWI) were employed. The kNDMI uses a kernel function to model the nonlinear relationship between near-infrared and shortwave infrared reflectance, yielding higher sensitivity to variations in vegetation canopy water content and soil moisture and effectively capturing continuous moisture gradients that are associated with plant physiological stress [33]. The SPWI [34] refines conventional moisture indices by incorporating a blue-band correction. This multiband synergistic approach improves both the stability and the discriminatory power of water-related information retrieval across diverse land cover types.

$$kNDMI=1-{\displaystyle \frac{3-2k\left({\rho }_{BLUE},{\rho }_{RED}\right)-2k\left({\rho }_{BLUE},{\rho }_{SWIR2}\right)+k\left({\rho }_{RED},{\rho }_{SWIR2}\right)}{3+2k\left({\rho }_{BLUE},{\rho }_{RED}\right)+2k\left({\rho }_{BLUE},{\rho }_{SWIR2}\right)+k\left({\rho }_{RED},{\rho }_{SWIR2}\right)}}$$

(1)

$$SPWI={\displaystyle \frac{{\rho }_{NIR}-{\rho }_{SWIR2}+{\rho }_{BLUE}}{{\rho }_{NIR}+{\rho }_{SWIR2}+{\rho }_{BLUE}}}$$

(2)

where ρ_BLUE, ρ_RED, ρ_NIR, and ρ_SWIR2 denote the spectral reflectances of the blue, red, near-infrared, and shortwave infrared-2 bands, respectively; and k designates the kappa coefficient.

For vegetation characterization, the kNDVI was employed. This index effectively mitigates the nonlinear saturation issue that is inherent in the traditional NDVI through a kernel function technique. It demonstrates stronger theoretical linkages and stronger correlations with key vegetation ecological parameters, thereby providing a more accurate representation of the growth status and ecosystem vitality of dense vegetation [35].

$$kNDVI=\tanh \left({\left({\displaystyle \frac{{\rho }_{NIR}-{\rho }_{RED}}{2\sigma }}\right)}^2\right)$$

(3)

where ρ_RED and ρ_NIR represent the spectral reflectances in the red and near-infrared bands, respectively; and σ is an adjustable length-scale parameter introduced to capture the nonlinear sensitivity of the NDVI to vegetation density.

Furthermore, to comprehensively characterize the stress effects of anthropogenic activities on the ecological environment, the normalized difference built-up and soil index (NDBSI) and LST were incorporated. The NDBSI synthetically reflects the spectral characteristics of bare soil and impervious surfaces, effectively capturing land cover changes and surface drying effects that are induced by anthropogenic activities. It serves as a key indicator for assessing ecological stress during urbanization [36]. The LST directly reflects the surface thermal environment. Its spatial distribution and intensity can effectively reveal the urban heat island effect and the degree of thermal stress on ecosystems. The MOD11A2 product used in this study provides a consistent and reliable data foundation for long-term thermal environment analysis [37].

$$IBI={\displaystyle \frac{2{\rho }_{SWIR1}/\left({\rho }_{SWIR1}+{\rho }_{NIR}\right)-\left[{\rho }_{NIR}/\left({\rho }_{NIR}+{\rho }_{RED}\right)+{\rho }_{GREEN}/\left({\rho }_{GREEN}+{\rho }_{SWIR1}\right)\right]}{2{\rho }_{SWIR1}/\left({\rho }_{SWIR1}+{\rho }_{NIR}\right)+\left[{\rho }_{NIR}/\left({\rho }_{NIR}+{\rho }_{RED}\right)+{\rho }_{GREEN}/\left({\rho }_{GREEN}+{\rho }_{SWIR1}\right)\right]}}$$

(4)

$$SI={\displaystyle \frac{\left({\rho }_{\mathrm{SWIR}1}+{\rho }_{RED}\right)-\left({\rho }_{NIR}+{\rho }_{BLUE}\right)}{\left({\rho }_{\mathrm{SWIR}1}+{\rho }_{RED}\right)+\left({\rho }_{NIR}+{\rho }_{BLUE}\right)}}$$

(5)

$$NDBSI=\left(IBI+SI\right)/2$$

(6)

where ρ_BLUE, ρ_GREEN, ρ_RED, ρ_NIR, and ρ_SWIR1 correspond to the spectral reflectances in the blue, green, red, near-infrared, and short-wave infrared-1 bands, respectively; IBI and SI denote the index-based built-up index and the soil index, respectively.

To avoid potential bias from subjective weight assignments, an entropy-based weighting method was applied to objectively determine the relative importance of each indicator within the evaluation system. This approach computes weights according to the inherent dispersion of each indicator’s data, thereby improving the scientific soundness and comparability of the evaluation outcomes. All indicators were normalized prior to analysis to remove scale effects [38]. The five normalized indicators were then integrated into a composite ImWBEI using a weighted linear combination.

$$\mathit{Im}WBEI=w_1\times N_{SPWI}+w_2\times N_{kNDMI}+w_3\times N_{NDBSI}+w_4\times N_{kNDVI}+w_5\times N_{LST}$$

(7)

where w₁, w₂, w₃, w₄, and w₅ denote the proportion weights assigned to the SPWI, kNDMI, NDBSI, kNDVI, and LST, respectively, within the ImWBEI framework. N_SPWI, N_kNDMI, N_NDBSI, N_kNDVI and N_LST represent the normalized values of the corresponding indicators.

The calculated ImWBEI values were classified into five grades at intervals of 0.2 to facilitate a clearer interpretation of regional EEQ patterns and spatial heterogeneity: poor [0–0.2), low [0.2–0.4), moderate [0.4–0.6), good [0.6–0.8), and excellent [0.8–1].

To further evaluate the applicability and improvement of the proposed ImWBEI in Guangdong Province, a comparative analysis was conducted against the original WBEI, the widely used RSEI, and the MOD09A1 surface reflectance imagery. This systematic comparison was designed to assess the relative strengths and limitations of each index in characterizing the regional EEQ.

2.4. Trend Analysis

2.4.1. Theil–Sen Median Method and Mann–Kendall Test

To examine the changing trends in the EEQ from 2000 to 2021, this study employed the Theil–Sen median (Sen) method for trend estimation. As a robust non-parametric statistical approach, this method is insensitive to outliers and suitable for trend analyses of long-term remote sensing data. It is often coupled with the Mann–Kendall (MK) non-parametric test [39]. The trend (β) is calculated as follows:

$$\beta =median\left({\displaystyle \frac{ImWBE{I}_j-ImWBE{I}_i}{j-1}}\right),2000\le j\le i\le 2021$$

(8)

where ImWBEI_i and ImWBEI_j represent the ImWBEI values for the i-th year and j-th year, respectively. A trend is considered an improvement when β > 0.0005, a decrease when β < −0.0005, and stability when −0.0005 ≤ β ≤ 0.0005.

To determine the statistical significance of the trend, the Mann–Kendall test was further applied to compute the Z statistic. At a significance level of α = 0.05, a pixel’s trend was considered statistically significant if |Z| > 1.96; otherwise, it was considered nonsignificant. By integrating the results of the Sen estimator and the MK test, the ImWBEI trends were classified into the following five categories: significant degradation, slight degradation, stable, slight improvement, and significant improvement, thereby facilitating a detailed analysis of the EEQ trends across the study area.

2.4.2. Hurst Exponent

To predict future trends in the EEQ, this study employed the Hurst exponent based on rescaled range analysis to quantitatively characterize the characteristics of the long-term dependence and persistence of the time series [40]. This method calculates the scaling relationship between the cumulative deviation and the standard deviation of a time series. The Hurst exponent is derived by fitting a least squares regression in a double-logarithmic coordinate system. For a time series of length n, its mean series is defined as follows:

$${〈\xi 〉}_r={\displaystyle \frac{1}{\tau }}{\displaystyle \sum _{t=1}^{\tau }\xi \left(t\right)},\tau =1,2,\dots$$

(9)

The cumulative deviation is as follows:

$$X\left(t,\tau \right)={\displaystyle \sum _{t=1}^{\tau }\left(\xi \left(t\right)-{〈\xi 〉}_{\tau }\right)},1\le t\le \tau$$

(10)

The range is as follows:

$$R\left(\tau \right)=\underset{1\le t\le \tau }{\max }X\left(t,\tau \right)-\underset{1\le t\le \tau }{\min }X\left(t,\tau \right),\tau =1,2,\dots$$

(11)

The standard deviation is as follows:

$$S\left(\tau \right)={\left[{\displaystyle \frac{1}{\tau }}{\displaystyle \sum _{t=1}^{\tau }{\left(\xi \left(t\right)-{〈\xi 〉}_{\tau }\right)}^2}\right]}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}},\tau =1,2,\dots$$

(12)

If the relationship $R\left(\tau \right)/S\left(\tau \right)\propto {\tau }^H$ holds, this indicates the presence of long-term memory, known as the Hurst phenomenon, where H is the Hurst exponent. H ranges from 0 to 1, and its magnitude reveals the correlation between future trends and the past behavior of a time series. When 0.5 < H < 1, the series is persistent, indicating that future trends align with past trends, with stronger persistence indicated by higher H values. When H = 0.5, no long-term correlation exists. When 0 < H < 0.5, the series shows antipersistence, implying that future trends may oppose past trends, with stronger antipersistence indicated by lower H values.

To more precisely characterize the persistence intensity of EEQ changes and effectively project future trends, H was further classified into discrete levels. These levels were then spatially integrated with the Sen analysis results. Following established methods [41], H was first categorized into five classes: strong persistence (0.65 < H ≤ 1), weak persistence (0.5 < H ≤ 0.65), random (H = 0.5), weak antipersistence (0.35 ≤ H < 0.5), and strong antipersistence (0 ≤ H < 0.35). This classification was subsequently superimposed at the pixel scale onto the three Sen trend categories—improvement, stable and degradation. Through this spatial superposition, the future trajectory of the EEQ was ultimately mapped into nine distinct trend types: strong persistent improvement, weak persistent improvement, strong antipersistent degradation, weak antipersistent degradation, essentially stable, strong persistent degradation, weak persistent degradation, strong antipersistent improvement, and weak antipersistent improvement.

2.5. Analysis of Driving Factors

In this study, the optimal parameter-based geographical detector (OPGD) was used to analyze the driving mechanisms behind the spatial differentiation of the EEQ in Guangdong Province. Building upon the traditional geographical detector framework, the OPGD enhances the accuracy and robustness of detecting spatially stratified heterogeneity by systematically optimizing the discretization schemes for continuous explanatory variables [42]. This method does not assume linear relationships, is applicable to various data types, and can effectively identify the independent explanatory powers of individual factors as well as their interaction effects [43,44].

First, all the continuous explanatory variables were preprocessed. Using the OPGD, five discretization methods—equal interval, natural breakpoint, quantile, geometric spacing, and standard deviation—were sequentially tested. The optimal number of classification intervals, ranging from 3 to 7, was determined by searching for the combination that maximized the q statistic of the factor detector. This optimization process aims to minimize the within-stratum variance and maximize the between-stratum variance for each variable, thereby more accurately revealing its spatial association structure with the EEQ. After the optimal discretization parameters for each factor were determined, several detectors were applied for analysis.

The factor detector quantifies the independent explanatory power of a single factor on the spatial differentiation of EEQ, measured by the q value. Its calculation formula is as follows:

$$q=1-{\displaystyle \frac{{\displaystyle {\sum }_{h=1}^L{N}_h{\sigma }_h^2}}{N{\sigma }^2}}$$

(13)

where L is the number of strata for the variable; N_h and σ_h² are the sample size and variance of stratum h, respectively; and N and σ² are the total sample size and overall variance of the study area, respectively. The q value ranges from 0 to 1, with a higher value indicating a stronger explanatory power of the factor regarding the spatial distribution of EEQ.

The interaction detector assesses the interaction between any two factors, determining whether their combined effect enhances or weakens the explanatory power concerning the EEQ. This is achieved by comparing the individual q values, q(X₁) and q(X₂), with the interactive q value q(X₁∩X₂). The interaction type (e.g., bifactor enhancement or nonlinear enhancement) is then identified on the basis of predefined decision criteria.

The ecological detector tests for statistically significant differences in the mean values of the EEQ between different factor stratifications or different regions, using an F statistic for the judgement are calculated as follows:

$$F={\displaystyle \frac{N_{X_1}(N_{X_2}-1)\cdot SS{W}_{X_1}}{N_{X_2}(N_{X_1}-1)\cdot SS{W}_{X_2}}}$$

(14)

where N_X1 and N_X2 are the sample sizes of the two groups and where SSW_X1 and SSW_X2 are the within-group sums of the squared deviations.

2.6. Coupling Coordination Degree Model

The coupling coordination degree model is a vital method for measuring the intensity of interaction and the level of synergistic development between two or more systems [45,46]. A higher coupling coordination degree indicates a tighter relationship of mutual promotion and coordinated symbiosis between systems, signifying a healthier and more sustainable state of overall system development. To quantify the interactive relationship between the EEQ and urbanization progression in Guangdong Province, this study introduces the model to reveal the spatiotemporal characteristics and underlying mechanisms of their coordinated development. The calculation formulas are as follows [47]:

$$C=2\sqrt{{\displaystyle \frac{U_1\times U_2}{{\left(U_1+U_2\right)}^2}}}$$

(15)

$$\mathrm{D}=\sqrt{\mathrm{C}\times \left(\alpha {\mathrm{U}}_1+\beta {\mathrm{U}}_2\right)}$$

(16)

where C represents the degree of coupling, U₁ denotes the comprehensive urbanization index value, U₂ denotes the EEQ value, and D represents the degree of coupling coordination. The coefficients α and β are weighting factors that characterize the relative contribution of each system to coordinated development. This study posits that the EEQ and urbanization are equally important for regional sustainable development; therefore, both α and β are set to 0.5.

The quantitative results of the coupling coordination degree (D) were evaluated according to the classification criteria presented in

Table 1. Classification of the coupling coordination degree.

Coupling Coordination Degree	Coupling Coordination Degree Type	Coupling Coordination Degree	Coupling Coordination Degree Type
0.0 ≤ D ≤ 0.1	Extreme dysregulation	0.5 < D ≤ 0.6	Barely coordinated
0.1 < D ≤ 0.2	Severe dysregulation	0.6 < D ≤ 0.7	Primary coordination
0.2 < D ≤ 0.3	Moderate dysregulation	0.7 < D ≤ 0.8	Intermediate coordination
0.3 < D ≤ 0.4	Mild dysregulation	0.8 < D ≤ 0.9	Good coordination
0.4 < D ≤ 0.5	Near dysregulation	0.9 < D ≤ 1.0	High-quality coordination

. This analysis systematically reveals the spatiotemporal patterns and evolutionary characteristics of the coordinated relationship between the EEQ and urbanization systems in Guangdong Province.

3. Results and Analysis

3.1. Local Comparative Analysis of the ImWBEI

To visually validate the representational ability of the WBEI within complex surface environments, three representative local regions were selected. A comparative analysis was conducted to examine the consistency and discrepancies among the ImWBEI, WBEI, RSEI, and the original MODIS imagery. In the forestland–bare land ecotone (Figure 1a), a comparison with the MODIS imagery revealed that the ImWBEI provided a clearer distinction between the boundaries of these two land cover types. In contrast, the RSEI and WBEI underestimated bare land and forestland, respectively, to some degree. In the water–bare land mixed area (Figure 1b), the RSEI failed to effectively assess the water body EEQ because of its water-masking procedure, whereas the WBEI yielded a relatively blurred distinction in the land–water transition zone. By comparison, the ImWBEI provided a more complete representation of this region, with spatial details aligning more closely with the original imagery. In the dense forestland area containing developed land (Figure 1c), the WBEI tended to overestimate the EEQ of developed land and its immediate surroundings, whereas the RSEI tended to underestimate it. The ImWBEI clearly distinguished forestland from developed land and provided a more reasonable depiction of their respective EEQ levels, which was consistent with the textural features observed in the imagery. Overall, the results of the comparative analysis demonstrated that the ImWBEI exhibited superior discriminative ability across different land cover types, particularly in land–water transition zones and densely vegetated areas. It more accurately reflected the spatial heterogeneity of the EEQ and land-cover boundary information, effectively enhancing its applicability and representational accuracy under complex underlying surface conditions.

3.2. Spatial Distribution of the EEQ

The spatial distribution of the improved water benefit ecological index (ImWBEI) in Guangdong Province from 2000 to 2021 revealed a distinct north–south gradient, which was characterized by generally higher quality in the north and lower quality in the south, which was concurrent with a positive temporal trend (Figure 2). Spatially, areas classified as good and excellent were consistently concentrated in northern Guangdong. In contrast, regions rated poor and low were predominantly located in the highly urbanized PRD and select coastal zones. Major metropolitan areas, including Guangzhou, Foshan, Dongguan, Shenzhen, and Shantou, persistently exhibited low ImWBEI values, resulting in the formation of clear spatial clusters of degraded EEQ. Temporally, a marked improvement was observed beginning in approximately 2003 in several northern prefectures (e.g., Zhaoqing, Qingyuan, Shaoguan, Heyuan, and Meizhou), where the dominant EEQ grade shifted progressively from moderate to good and excellent. Conversely, areas in western (e.g., Zhanjiang) and eastern Guangdong (e.g., Shantou and Jieyang) maintained lower quality levels for extended periods. Overall, the results indicated pronounced and persistent spatial differentiation in the EEQ across the province, with a sustained disparity between the northern and southern regions.

Temporal analysis of the ImWBEI for Guangdong Province from 2000 to 2021 revealed an overall fluctuating but discernible increasing trend in the EEQ (Figure 3). The annual provincial mean ImWBEI ranged between 0.514 and 0.604, corresponding to an average annual growth rate of 0.92%. The temporal trajectory could be divided into three distinct phases: a period of rapid increase from 2000 to 2003, which culminated in a peak value of 0.588; a subsequent declining phase from 2004 to 2008, with the index reaching a trough of 0.542 in 2006; and a final phase of steady recovery and growth from 2009 to 2021, when the maximum value of this period was 0.604 by the end of 2021. Throughout the entire study period, the annual mean ImWBEI consistently fell within the moderate to good quality range. The areal proportions of land classified as moderate or better remained consistently high, exceeding 65% in all years. These proportions increased from 65.3% in 2000 to 77.3% in 2021. The ranking of areal coverage by quality grade remained stable (good > excellent > moderate > poor > low), indicating that the good category was the dominant grade and that a substantial area was consistently classified as excellent. In summary, the EEQ of Guangdong Province clearly improved, albeit nonlinearly, from 2000 to 2021.

3.3. Trends in EEQ Changes

Pixel-based trend analysis for the period 2000–2021 indicated pronounced spatial heterogeneity in the direction of ecological change across Guangdong Province (Figure 4a). An overall improving trend was dominant, with areas of improvement covering 60.6% of the total area (51.2% classified as slight improvement). Conversely, 25.7% of the area showed degradation (21.1% slight degradation), which was concentrated in the PRD (e.g., Foshan, Zhongshan, and Jiangmen), eastern Guangdong (e.g., Shantou and Jieyang), and parts of northern Guangdong (e.g., Qingyuan and Heyuan), highlighting localized areas of persistent ecological stress. The integration of the Hurst index for persistence analysis revealed that 52.6% of the area exhibited persistent future trends, while 30.3% showed antipersistence (Figure 4b). Weak persistence was the predominant type (49.90%), followed by weak antipersistence (29.31%), suggesting the potential for trend reversal in some regions. The projected future trajectory was predominantly one of improvement: weak persistent improvement (37.47%) and weak antipersistent improvement (20.27%) together accounted for the greatest share. In contrast, weak persistent degradation and weak antipersistent degradation covered 12.43% and 9.05% of the area, respectively. The spatial pattern of areas projected to remain stable aligned well with that of the core PRD region, demonstrating consistency between the long-term past trend and the forecasted future state.

Statistical analysis of the EEQ trends across Guangdong’s 21 prefecture-level cities revealed that in most cities, the combined area exhibiting improvement or stability exceeded the area undergoing degradation (Figure 5a). Meizhou, Shanwei, and Heyuan recorded the highest proportions of improving areas, covering 84.9%, 73.5%, and 73.2% of their respective administrative areas, respectively. In contrast, Dongguan and Shenzhen were characterized by the most stable areas, which accounted for 44.4% and 43.8% of their territory, respectively. Conversely, Zhanjiang, Foshan, and Zhongshan showed the most extensive decreasing trends, with degrading areas representing 58.0%, 55.5%, and 55.2% of their total areas, respectively. Projections of future trends indicated that more than half of the cities were expected to follow a persistently improving trajectory, predominantly classified as weak persistent improvement, followed by weak antipersistent improvement (Figure 5b). Meizhou exhibited the greatest share of weak persistent improvement, at 55.1% of its area. Where degradation was projected, weak persistent degradation was the dominant pattern. Notably, Foshan and Zhongshan experienced substantial proportions of weak persistent degradation (31.2% and 28.3%, respectively), suggesting that these negative trends might be susceptible to reversal in the future.

3.4. Driving Factors of EEQ

The results from the OPGD factor analysis indicated distinct temporal variations in the explanatory power (q value) of each driving factor for the spatial differentiation of the EEQ in Guangdong Province from 2000 to 2021 (Figure 6). Among the eight factors examined, the comprehensive urbanization index (CU) exhibited the strongest overall explanatory power, with a fluctuating upwards trend; its q value increased from 0.25 in 2000 to 0.44 in 2021, making it the most influential factor by the end of the period. The explanatory powers of the average temperature (T) and slope (S) were also relatively high and generally increased over time, underscoring the persistent role of thermal and topographic conditions in shaping ecological patterns. Elevation (E) and soil type (ST) maintained stable, moderate explanatory powers, reflecting the consistent foundational influence of these inherent natural factors. In contrast, the q values for cumulative precipitation (CP), potential evapotranspiration (PE), and aspect (A) remained low (generally <0.10), indicating their relatively weak direct effects on spatial differentiation. Between 2015 and 2021, the explanatory power of the CU followed a declining and then a recovering pattern, whereas that of T and CP markedly increased but then decreased. While natural factors continue to provide a baseline influence on the distribution of the EEQ, human activities—primarily driven by urbanization—have become the key factor shaping the spatial differentiation of the EEQ in Guangdong Province in recent years.

Interaction analysis based on the OPGD revealed that the explanatory power (q value) for any two-factor interaction consistently exceeded that of its individual components, with interaction types dominated by bifactor or nonlinear enhancement (Figure 7). Interactions involving the comprehensive urbanization index (CU) were the most significant and consistently produced higher q values than the average of the other factor combinations did. The strongest interaction in 2000 was between CU and soil type (ST) (q = 0.299), whereas by 2021, the interaction between CU and slope (S) had become the most influential (q = 0.519). In contrast, interactions involving aspect (A) and potential evapotranspiration (PE) remained weak. Interactions among topographic (e.g., elevation E and slope S); climatic (e.g., temperature T and cumulative precipitation CP); and ST factors maintained relatively stable and moderate-to-high explanatory powers. Overall, the CU, representing concentrated human activity, emerged as the core driver of the spatiotemporal changes in the EEQ in Guangdong Province. Its influence on spatial differentiation was further amplified through interactions with various natural factors, such as S, E, and ST. Additionally, the results from the ecological detector confirmed statistically significant differences between most factors, indicating that distinct underlying mechanisms affect the EEQ.

3.5. Coupling Coordination Between the EEQ and Comprehensive Urbanization

Building upon the identification of the comprehensive urbanization index as the dominant driver of EEQ differentiation, the coupling coordination degree model was applied to quantify the dynamic coordination between the EEQ and urban development at the prefectural level from 2000 to 2021. The results revealed significant spatiotemporal heterogeneity in the degree of coupling coordination in Guangdong Province (Figure 8). Temporally, the coordination level steadily improved. The provincial mean degree of coupling coordination increased from 0.197 in 2000 to 0.283 in 2021, indicating a progressive shift towards a more synergistic relationship. Spatially, a clear “high-south, low-north” gradient was observed. High values for the degree of coupling coordination were concentrated in the PRD and selected coastal cities, forming a distinct high-value core. This core exhibited a diffusion pattern, with its influence extending outwards towards eastern, western, and northern Guangdong, thereby establishing a core–periphery spatial structure. Concurrently, the areal extent of regions classified in imbalanced coordination states (e.g., moderate or severe imbalance) contracted noticeably, with most areas transitioning towards mild imbalance or coordinated states. Notably, the core area of the PRD—despite underlying ecological pressures—functioned as a key hub, radiating influence and driving the overall optimization of the coordination pattern across the province. This suggested that high-intensity urban areas could act as pivotal zones for fostering the coevolution of urbanization and EEQ sustainability.

4. Discussion

4.1. Advantages of the ImWBEI

Remote sensing-based ecological indices are essential for the rapid, large-scale assessment of ecological conditions. While the widely used RSEI often masks water pixels, the WBEI offered improved applicability in hydrologically complex regions by explicitly integrating aquatic factors [48]. However, in Guangdong—which is characterized by intense urbanization, dense water networks, and extensive vegetation—the conventional WBEI has remained limited by its reliance on linear spectral indices. Although other indices, such as the RVI, NDVI, and NDMI, are valued for their computational simplicity and physical interpretability, they often fail to capture the nonlinear spectral responses inherent in vegetation growth and moisture dynamics. This led to signal saturation in areas of high biomass and reduced sensitivity to intermediate moisture gradients, ultimately constraining comprehensive ecological assessments [49]. To address these limitations, the ImWBEI proposed in this study incorporated two kernel-based nonlinear indices: the kNDVI and the kNDMI. Unlike conventional ratio-based indices, the kNDVI and kNDMI use kernel functions (e.g., Gaussian radial basis functions) to map spectral reflectance values into a high-dimensional nonlinear feature space. This formulation more accurately represents the complex relationships between the reflectance and vegetation biochemical parameters or soil moisture. Specifically, the kNDVI alleviates signal saturation under high-biomass conditions and increases the sensitivity to eco-physiological variations in dense forests. By leveraging higher-order spectral interactions between the near-infrared and shortwave-infrared bands, the kNDMI provides superior detection of subtle gradients in canopy water contents and soil moisture levels [50]. Compared with traditional indices, the kNDMI improves the performance in monitoring soil moisture and identifying meteorological drought by up to 169%, making it a powerful tool for characterizing moisture heterogeneity in Guangdong. Thus, the kNDVI and kNDMI supply more precise, saturation-resistant, and sensitive inputs to the ImWBEI from the greenness and wetness dimensions. By integrating five core indicators—the kNDVI, kNDMI, SPWI, NDBSI, and LST—and using information entropy for objective weighting, the ImWBEI establishes a multidimensional framework that is capable of concurrently characterizing multiple ecological processes. As shown in Figure 1, the ImWBEI significantly improved the representation of complex surfaces: it revealed richer internal ecological gradients in the densely vegetated north and provided clearer, more continuous responses to ecological fragmentation and aquatic quality in the water–land transition zones of the PRD. In summary, while preserving the capacity to quantify aquatic ecological benefits, the ImWBEI enhances the assessment of vegetation status, water stress, urban heat islands, and land surface drying. This enables a more comprehensive and accurate depiction of the spatial heterogeneity of coupled water–vegetation–heating processes, offering a robust tool for the precise evaluation and dynamic monitoring of the regional EEQ.

4.2. Coupling Analysis Between EEQ Trends and Urbanization Development

This study demonstrated that the EEQ in Guangdong Province exhibits a distinct spatiotemporal pattern that is characterized by “higher quality in the north and lower quality in the south,” with overall improvement occurring alongside localized zones of degradation. This pattern results from the interplay between natural geographical conditions and region-specific development pathways. The comprehensive urbanization index emerged as the dominant factor governing the spatial heterogeneity of the EEQ, highlighting the dominant role of anthropogenic activities in shaping regional ecological patterns. Moreover, the relationship between the EEQ and urbanization exhibited marked spatial variation, closely linked to local differences in physical geography, economic structure, and policy frameworks [51]. The Pearl River Delta (PRD) and coastal regions consistently recorded relatively lower EEQ and were the primary zones of degradation. While leveraging locational and policy advantages to achieve rapid economic growth, the conventional urbanization model in these areas—characterized by high-intensity land development and resource consumption—directly contributes to the compression of ecological space, increased environmental pollution loads, and the erosion of critical ecosystem services, such as those provided by coastal wetlands [52]. Coupling coordination analysis between the EEQ and urbanization subsystems showed that the economically advanced PRD maintained relatively low EEQ alongside high coordination, indicating a close interaction between ecological and economic systems. In recent years, systematic ecological restoration efforts—particularly green infrastructure development, wetland protection, and ecological corridor construction under the Guangdong–Hong Kong–Macao Greater Bay Area initiative—have generated measurable local benefits, such as improved vegetation cover and water quality. Enhanced resource allocation efficiency, refined ecological compensation mechanisms, and the green transformation of infrastructure have collectively promoted synergistic development between the EEQ and urbanization [53,54]. Furthermore, the analysis revealed significant spatial heterogeneity in coordination levels; not all rapidly urbanizing areas exhibited high degrees of coordination. Improvements in coordination stemmed not only from urbanization processes but, more critically, from optimized system interactions. However, persistent urbanization and industrialization pressures continue to pose ecological risks in parts of the PRD, suggesting that while regional coordination has improved, localized pressures on the ecological carrying capacity persist. This highlights the vulnerability of ecosystem resilience under sustained anthropogenic stress. In contrast, northern Guangdong, designated as an ecological conservation zone and serving as the province’s ecological barrier, maintained a superior EEQ. This region has achieved significant outcomes in terms of forest coverage, water retention, and biodiversity conservation. However, it has been confronted with the paradox of high ecological quality alongside low systemic coordination. These findings indirectly demonstrated that the degree of coupling coordination was not a monotonic function of either the EEQ or the urbanization index alone, but rather a comprehensive indicator reflecting the state of interaction between systems. Its inland, mountainous terrain imposes constraints on transportation and logistics, which hinder integrated socioeconomic development and inter-system coupling. Despite policy efforts to promote balanced regional development, the green industrial system and the mechanisms for realizing the ecological product value remain underdeveloped. Consequently, the benefits of these efforts had not yet been fully translated into higher levels of coupling coordination. Eastern and western Guangdong, undergoing industrial transfer and accelerated growth under regional coordinated development strategies, exhibit EEQ levels that depend on the balance between urbanization pace and conservation effectiveness. Localized degradation trends identified in these areas reflect emerging ecological challenges during rapid development.

In summary, Guangdong faces a dual challenge: pronounced ecological pressure in its highly developed economic agglomerations and insufficient systemic coordination within its ecologically superior zones. This pattern fundamentally reflects a spatial mismatch between the intensity of development and the imperatives of conservation. International research provided important references for understanding such issues. For instance, the concept of ‘dispersed areas of human influence’, developed from studies of Spanish wetlands [23], captured the indirect effects of distant land transformations on ecosystems. This aligned with our findings: although PRD urbanization enhanced provincial coordination, it continued to impose localized degradation risks. Both cases underscored the need to move beyond aggregate indicators and monitor fine-scale, dispersed anthropogenic pressures that may trigger ecosystem resilience thresholds. Similar challenges were documented in other rapidly urbanizing deltas, such as Vietnam’s Mekong Delta, where intensive agriculture and urban expansion compromised wetland services and hydrological regimes [55]. These cases affirmed that reconciling EEQ with urbanization is a global sustainability challenge, not unique to China. The comparative insights presented here carry direct implications for spatial planning and policy design in Guangdong and analogous regions. Future strategies must therefore adopt region-specific pathways. In optimized development zones, such as the PRD, the focus should shift towards institutional innovation for green growth, with a transition to a sustainable and intensive development model. In ecological conservation zones, such as northern Guangdong, policies must prioritize ecological integrity through controlled development and leveraging initiatives, such as forest city construction, to transform ecological assets into developmental capital. For the emerging growth zones in eastern and western Guangdong, enhanced ecological safeguards and support for green industries are essential to synchronize development with conservation. Concurrently, the role of the PRD should be strengthened as a core engine of development, utilizing its advantages in innovation, governance, and investment to foster cross-regional coordination and collaborative ecological governance throughout the province. Such a systematic and differentiated approach is critical to advancing Guangdong towards a synergistic stage of high-quality development and high-level environmental protection [56].

4.3. Limitations and Prospects

While the ImWBEI developed in this study performs well in assessing the EEQ across Guangdong Province and offers preliminary insights into its spatiotemporal dynamics and driving mechanisms, several limitations remain. These limitations also point to productive avenues for further research. As a remote sensing-based spectral index, the validity and precision of the ImWBEI are inherently constrained by the availability and characteristics of the underlying data sources. The moderate-resolution imagery (MODIS, 1 km) used in this analysis is suitable for characterizing province-scale trends but lacks the spatial granularity needed to resolve fine-scale ecological gradients or small urban patches [57]. This limitation is particularly critical in heterogeneous urban landscapes, where mixed pixel effects can obscure local-scale variability [56] and lead to imprecise assessments of the EEQ within individual urban functional zones. Temporally, the standard composite periods of such datasets are often too coarse to capture short-term fluctuations in the EEQ that are driven by extreme climatic events or abrupt anthropogenic disturbances. Furthermore, the ImWBEI is fundamentally a proxy measure that is derived from surface physical parameters—greenness, wetness, dryness, and heat. It provides an efficient and objective assessment of the ecological status but offers only an indirect representation of intrinsic ecosystem attributes, such as biodiversity or soil organic carbon pools. Future efforts should focus on integrating multi-source remote sensing data. Combining the high spatial resolution of Sentinel-2, the frequent revisit period of MODIS, and the all-weather capability of Sentinel-1 SAR could produce dynamic datasets with enhanced spatiotemporal continuity and greater robustness against cloud cover and observational gaps. Moreover, coupling the ImWBEI framework with established ecological process models or long-term in situ observations (e.g., biodiversity monitoring and carbon flux measurements) has significant potential. Such an integrated approach would help transition the assessment from a descriptive tool to one more directly informative for understanding ecosystem processes and supporting management decisions.

In the driver-analysis component of this study, the geographical detector method was employed to quantify the global explanatory power of individual factors on the spatial heterogeneity of the EEQ and to characterize their interaction types. However, the geographical detector operates on a spatially static, global-averaging framework. Specifically, its q statistic reflects the average explanatory strength of a driver across the entire study area, thereby masking its potential spatial non-stationarity—that is, it cannot reveal how the magnitude or direction of a driver’s effect may vary locally. Although the coupling coordination degree model helped elucidate the systemic interaction pattern between the EEQ and urbanization, the driver analysis remained limited in its ability to identify the dominant factors operating in specific local contexts. This limitation constrains the practical utility of the results for designing spatially differentiated management policies. To address this gap, future research should combine the global analytical capacity of geographical detectors with localized modeling techniques. A promising strategy would be to first apply a geographical detector to identify globally dominant drivers and significant interactions objectively. Subsequently, locally adaptive methods, such as geographically weighted regression, could be used to map the spatial variations in the influence coefficients for these key drivers [58]. Such an integrated approach would shift the analysis from a global-average understanding to a spatially explicit, context-sensitive explanation, thereby providing a stronger scientific foundation for zoned and targeted environmental governance.

5. Conclusions

On the basis of the GEE cloud computing platform, this study introduced remote sensing indicators that are more sensitive to vegetation and moisture responses to construct an improved water benefit ecological index (ImWBEI). The index was designed to dynamically monitor the EEQ in Guangdong Province from 2000 to 2021, assess its long-term trends, and analyze its responses to key driving factors. The results demonstrated that the ImWBEI substantially improved the fine-scale characterization and discrimination of the EEQ over complex underlying surfaces, such as urban built-up areas and land–water ecotones. Spatially, the EEQ across Guangdong Province exhibited pronounced and persistent “high–north, low–south” differentiation. Most of northern Guangdong consistently maintained ImWBEI values above 0.6, reflecting stable and favorable ecological conditions. In contrast, the values for the Pearl River Delta and southern coastal regions were predominantly between 0.2 and 0.6, indicating long-term moderate to low EEQ values. Temporally, the provincial mean ImWBEI increased at a rate of 0.0092 per year from 2000 to 2021, indicating an overall improving trend, albeit with localized areas of degradation or stability. Predictions derived from the Hurst index further suggested that future changes would be dominated by persistent behavior, with weak persistent improvement emerging as the predominant trend type. With respect to driving mechanisms and systemic coordination, the geographical detector analysis identified the comprehensive urbanization index as the most critical factor explaining the spatial differentiation of the EEQ, with most factor interactions demonstrating either two-factor enhancement or nonlinear enhancement. Analysis based on the coupled coordination degree model further revealed significant north–south disparities in the coordination between the EEQ and comprehensive urbanization, with higher coordination levels observed in the southern region. Nevertheless, provincial-level coordination exhibited a continuous optimizing trend overall, with several cities having already transitioned into a stage of coordinated development. In summary, the ImWBEI and its integrated analytical framework developed in this study constitute a more sensitive and reliable tool for monitoring the EEQ in complex urban regions with dense water networks, lush vegetation, and rapid urbanization. This work also yields methodological insights and decision-support guidance for reconciling ecological conservation with high-quality development in analogous geographical contexts.