Jianghan Plain is located in the middle reaches of the Yangtze River, the middle and lower reaches of the Han River, and the south-central part of Hubei Province and is an important part of middle Yangtze River Plain, one of the three major plains in China. It starts from the Yichang Zhijiang River in the west; ends in Wuhan, the largest city in central China; reaches Zhongxiang in the north; and is connected to Dongting Lake Plain in the south. It lies between latitudes 29°26′ and 31°37′ north and longitudes 111°14′ and 114°36′ east, with an area of more than 46,000 square kilometers. It has a subtropical monsoon climate with an annual average of approximately 2,000 h of sunshine and a total annual solar radiation value of approximately 460–480 kJ per square centimeter. The frost-free period is approximately 240–260 days, the duration above 10°C is approximately 230–240 days, and the active cumulative temperature is 5,100–5,300°C. The average annual precipitation is 1,100–1,300 mm, and the higher temperature of April–September precipitation accounts for approximately 70% of the total annual precipitation. Jianghan Plain mainly has eight counties and urban areas, including Jingzhou city, Jingzhou district, Shacheng district, Jiangling County, public security county, supervision city, Shishou city, Honghu city, and Songzi city; three provincial-level cities such as Xiantao, Qianjiang, and Tianmen, and radiates into some areas in the surrounding five prefectures of Wuhan, Xiaogan, Jingmen, Yichang, and Xiangyang and includes Caidian district, Hanchuan city, Yingcheng city, Shayang County, Jingshan city, Zhongxiang city, Zhijiang city, and Yicheng city. Figure 2 shows the location map of Jianghan Plain with data extracted from ASTER GDEM 30 m elevation (geospatial data cloud) and 2020 watershed information (land use).

Worldwide Reference System (WRS-2) paths in Rows 130 and 043 using satellite remote sensing images; three Landsat 5/TM layer images corresponding to 1990, 1998, and 2006; and two Landsat 8/OLI layer images for 2014 and 2021 were selected for the study area to synthesize the spatial and temporal distribution of the RSEI on Jianghan Plain from 1990 to 2021. The layer image months were concentrated from January to March, the image cloudiness was less than 5%, and the quality was good (Table 1).

The Google Earth Engine (GEE) platform was applied to collect and process Landsat 5/TM and Landsat 8/OLI imagery provided by the U.S. Geological Survey (USGS) and perform preprocessing, such as radiometric calibration, geometric correction, and atmospheric correction. Among them, the radiometric calibration was performed using the Landsat Surface Reflectance Code (LaSRC) proposed by Vermote for Landsat 5/TM and Landsat 8/OLI (Vermote et al., 2016), geometric correction using the quadratic polynomial and nearest image element method to control the root mean square error within 0.5 image elements (Chander et al., 2009), and atmospheric correction based on the LOWTRAN model for image correction and calculation of base pixel data such as clouds, water, and snow by CFMASK (Foga et al., 2017). In addition, the thermal infrared (TIR) band (Band 6) in Layer 1 in Landsat 5/TM Series 1 was initially acquired at a resolution of 120 m/pixel and resampled at 30 m using cubic convolution interpolation; two thermal infrared (TIR) bands (Band 10 and Band 11) in Layer 1 in Landsat 8/OLI Series 1 were initially acquired at a resolution of 100 m/pixel and resampled at 30 m using cubic convolution interpolation, pixel resolution, and 30 m resampling using cubic convolution interpolation (Xiong et al., 2021). In this study, the LST was calculated using the single-channel calculation rule (SC) (Gomis-Cebolla et al., 2018), and LANDSAT/LT05/C01/T1_SR and LANDSAT/LC08/C01/T1_SR were selected to calculate the NDVI, WET, NDSI, and LST. The surface temperature was calculated using the radiance of Landsat 5/TM Band 6, the radiance of Landsat 8/OLI Band 10, and the NCEP_RE/surface_wv data (Table 2).

The ecological evaluation index was constructed using four factors reflecting the natural ecological environment: normalized difference vegetation index, wetness, normalized difference soil index, and land surface temperature (Boori et al., 2021). The four remote sensing bands corresponding to the normalized difference vegetation index, wetness, normalized difference soil index, and land surface temperature were combined into a new index image after normalization and then subjected to principal component analysis (PCA), with a PCA range of [0, 1]; after normalization of the four indices, PC1 was calculated using feature analysis (https://developers.google.com/earth). The initial ecological index ( R S E I 0 ) can be obtained by subtracting the calculated first principal component (PC1) from 1 and then the remote sensing ecological index ( R S E I ) can be obtained by normalizing it. The remote sensing ecological index ( R S E I ) was divided into 5 levels to represent 5 ecological conditions by the equal spacing grading method, corresponding to bad [0,0.2], fair [0.2,0.4], medium [0.4,0.6], good [0.6,0.8], and excellent [0.8,1] (Wu et al., 2020), to evaluate the ecological environmental status of Jianghan Plain. R S E I 0 = 1 − { P C 1 [ f ( N D V I , W E T , N D S I , L S T ) ] } (1) R S E I = ( R S E I 0 − R S E I 0 _ m i n ) / ( R S E I 0 m a x − R S E I 0 _ m i n ) (2) where NDVI, WET, NDSI, and LST refer to the normalized difference vegetation index, wetness, normalized difference soil index, and land surface temperature, respectively. RSEI _{0_max} and RSEI _{0_min} refer to the maximum and minimum values of the original ecological index, respectively.

The NDVI, as the most widely used vegetation index, is closely related to plant biomass, leaf area index, and vegetation cover (Liaqat et al., 2017). Therefore, the NDVI was chosen to represent the normalized difference vegetation index: N D V I = ( ρ n i r − ρ r e d ) / ( ρ n i r + ρ r e d ) (3) where ρ n i r and ρ r e d represent the reflectance in the near-infrared and red wavelength bands, respectively.

Moisture, greenness, and brightness components of remote sensing tassel cap transformation have been applied in a large number of ecological environmental quality evaluations, among which WET better reflects the moisture of soil and vegetation (Baig et al., 2014). The calculation model is as follows (Polevshchikova, 2019): W E T = c 1 ρ 1 + c 2 ρ 2 + c 3 ρ 3 + c 4 ρ 4 + c 5 ρ 5 + c 6 ρ 6 (4) where ρ 1 − ρ 6 represent the reflectance of blue, green, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 wavelengths, respectively, and c1–c6 are the sensor parameters.

The NDSI consists of the building index (BI) and the bare soil index (SI), which is expressed as the average of the two (Xu, 2008): N D S I = ( B I + S I ) / 2 (5) B I = 2 ρ 5 ρ 5 + ρ 4 − ρ 4 ρ 4 + ρ 3 − ρ 2 ρ 2 + ρ 5 2 ρ 5 ρ 5 + ρ 4 + ρ 4 ρ 4 + ρ 3 + ρ 2 ρ 2 + ρ 5 (6) S I = [ ( ρ 5 + ρ 3 ) − ( ρ 4 + ρ 1 ) ] / [ ( ρ 5 + ρ 3 ) + ( ρ 4 + ρ 1 ) ] (7) where ρ 1 − ρ 5 represents the reflectance of blue light, green light, red light, near-infrared, and shortwave infrared 1 bands.

Surface temperature is closely related to the growth and distribution of vegetation and the evaporation cycle of surface water resources and is one of the important parameters reflecting the surface environment; therefore, LST is represented by surface temperature, and the index model is given as follows (Neteler, 2010): L S T = T / [ 1 + ( λ T / ρ ) l n ε ] (8) T = K 2 / l n ( K 1 / L t i r + 1 ) (9) where LST is the surface temperature, T is the temperature at the sensor, λ is the central wavelength of the thermal infrared band, ρ = 1.438 × 10^–2 m·K, ɛ is the surface specific emissivity, K ₁ and K ₂ are the calibration parameters, and L _tir is the radiation value of the thermal infrared band at the sensor.

Spatial correlation analysis of ecological environmental quality can be used to describe the spatially homogeneous distribution of ecological environmental quality in the study area (Degefu et al., 2021). In this study, we used global spatial autocorrelation (global Moran’s I) and local indicator spatial association (local Moran’s I) to analyze the spatial correlation of the RSEI.

The global Moran’s I (Moran’s I) index reflects the correlation of attribute values of adjacent spatial units. The closer the absolute value of Moran’s I is to 1, the stronger the spatial autocorrelation. The formula is as follows: G l o b a l m o r a n ′ s   I = m × ∑ i = 1 m ∑ j = 1 m W i j ( D i − D ¯ ) ( D j − D ¯ ) ∑ i = 1 m ∑ j = 1 m W i j ( D i − D ¯ ) 2 , (10) where m is the total number of indicators, D _i represents the ecological environmental quality value of location i, D ¯ is the average value of ecological environmental quality of all indicators in the study area, W i j is the spatial weight, and Moran’s I ranges from [-1,1]. When Moran’s I is greater than 0, data present a positive spatial correlation, and the larger the value is, the more obvious the spatial correlation will be; when Moran’s I is less than 0, the data show a negative spatial correlation, and the smaller the value is, the greater the spatial difference will be; and when Moran’s I is 0, there is no spatial autocorrelation (Shi et al., 2019).

Local Moran’s I (LISA) index can effectively reflect the correlation between the ecological environmental quality of each grid cell in the territory (Bi et al., 2021). There are five types of local spatial aggregation in the LISA clustering diagram, namely, high–high (H-H), low–low (L-L), low–high (L-H), and high–low (H-L), without significant differences. In this study, H-H indicates a high-ecological quality value of the selected region and spatially adjacent regions, L-L indicates a low-ecological quality value of the selected region and spatially adjacent regions, L-H indicates a low-ecological quality value of the selected region but a high-ecological quality value of the adjacent regions, and H-L indicates a high-ecological quality value of the selected region but a low-ecological quality value of the adjacent regions. If global spatial autocorrelation does not exist, the location of local spatial autocorrelation that may be masked can be found. When global spatial autocorrelation exists, the presence of spatial heterogeneity can be analyzed (Zhou et al., 2019b). The calculation formula is as follows: l o c a l m o r a n ′ s   I = ( D i − D ¯ ) × ∑ j = 1 m W i j ( D j − D ¯ ) ∑ i = 1 m ( D i − D ) 2 (11)

As shown in Table 3, the first principal component (PC1) eigenvalues were above 60% in all five images from 1990 to 2021, with PC1 at 62.28% in 1990, 67.09% in 1998, 67.15% in 2006, 63.42% in 2014, and the highest at 71.65% in 2021, indicating that PC1 concentrated the four ecological indices of the main percentage characteristics. From PC2 to PC4, the magnitudes of the characteristic values were reflected in different positive and negative ways. In PC1, the eigenvalues of NDVI and WET were positive, and those of NDSI and LST were negative, which was consistent with the actual situation (xiong et al., 2021).

Figure 3 represents the distribution characteristics of the five RSEI values on Jianghan Plain from 1990 to 2021. The mean values of the RSEI corresponding to the five equally spaced years during 1990–2021 were 0.667, 0.636, 0.599, 0.621, and 0.648, respectively, with some RSEI levels between good levels (0.6 and 0.8), except for the lower quartile in 2006, when the RSEI values in the lower quartile were less than 0.4, while the RSEI values in the upper quartile were all greater than 0.8. The RSEI values in the lower quartile were less than 0.4 in 2006, while the RSEI values in the upper quartile were all greater than 0.8. Overall, the ecological and environmental quality of Jianghan Plain showed a decreasing trend from 1990 to 2006 and an increasing trend from 2006 to 2021, when the ecological and environmental quality improved.

Figure 4 shows the spatial and temporal variations in the ecological quality of Jianghan Plain (the watershed part was excluded). Red, dark green, brown, light green, and yellow represent “bad,” “fair,” “moderate,” “good,” and “excellent” ecological grades, respectively. The overall ecological quality was good from 1990 to 2021. The areas with poor and moderate ecological levels were mainly distributed in Jingzhou district, Xiantao city, and Hanchuan city, which are subject to the economic radiation effect of the Wuhan city circle, in which urbanization, construction area growth, and human activities are frequent, which in turn leads to the decline of ecological environmental quality in the region. The good and excellent ecological levels were mainly distributed in the southwestern part of Songzi city, the southern part of Jili County, and the southeastern part of Honghu city, mainly due to the high regional forest coverage and low urbanization.

Combined with the temporal and spatial variation characteristics of eco-environmental quality in Jianghan Plain, further evidence research is carried out. Overall, 3,364 sample points are collected from the grid image, which is displayed in arcgis10.0; the eco-environmental quality index is divided into five categories by using the natural clustering zoning method. After taking the average value, they are “bad eco-environmental quality,” “fair eco-environmental quality,” “moderate eco-environmental quality,” “good eco-environmental quality,” and “excellent eco-environmental quality.” The spatial distribution of eco-environmental quality shows that from 1990 to 2021, the grid of good and good eco-environmental quality in Jianghan Plain is mainly distributed in the north and west of the town, and the grid of poor and poor eco-environmental quality is mainly distributed in the surrounding areas of Honghu city in the east. Among them, the overall eco-environmental quality deviation in the east in 2006 is consistent with Figure 4. Therefore, the temporal change of eco-environmental quality reflects the ecological model of Jianghan plain to a certain extent in Figure 5. Jianghan Plain is located in the economic core of the Yangtze River. In recent 30 years, the economic scale, urbanization, and industrialization level of the region have been continuously improved. By 2021, the GDP output value of Jianghan Plain will be 1771.676 billion yuan. The rapid development of the economy and society will inevitably bring corresponding changes to the ecological environment. Especially in recent years, the rapid economic growth the large-scale city construction, and the intensity of development of human activities will inevitably destroy its ecological environment quality.

The area and proportion of each ecological class (bad, fair, moderate, good, and excellent) were calculated based on the five RSEI maps corresponding to 1990, 1998, 2006, 2014, and 2021. Figure 6 shows the proportional changes in each ecological level, with the total proportion of “bad,” “fair,” and “moderate” RSEI gradually increasing from 1990 to 2006 and the total proportion of “good” and “excellent” RSEI gradually decreasing from 2006 to 2021. The overall ecological quality of Jianghan Plain showed a decreasing trend from 1990 to 2006 and an increasing trend from 2006 to 2021. The area and proportion of each RSEI level are shown in Table 4, and we calculated the total proportion of “bad,” “fair,” and “moderate” (PFM%) and “good” and “moderate,” respectively. The PFM% and GE% for 1990, 1998, 2006, 2014, and 2021 were 3.04%/96.96%, 7.1%/92.9%, 16.37%/83.63%, 6.54%/93.46%, and 10.94%/89.06%, respectively, with the highest PFM% and lowest GE% in 2006 compared with the other periods. Related literature shows that the strongest surface rainstorm occurred on the central Jianghan Plain during the night of 24 May 2006, causing soil, water disasters, and ecological imbalances, which in turn affected the quality of the ecosystem in that year (Zhang and Zhang, 2008). Except for 2006, PFM% increased and then decreased, and GE% decreased and then increased in the other periods.

Based on the results of the RSEI level grading in 1990, 1998, 2006, 2014, and 2021, the differences in RSEI performance and spatial and temporal area changes in ecological environmental quality on Jianghan Plain are shown in Table 5. The differences in changes were calculated for four periods (1990–1998, 1998–2006, 2006–2014, and 2014–2021). The results were classified into five classes: improvement obvious (IO), improvement slight (IS), invariability (IN), deterioration slight (DS), and deterioration obvious (DO). The results show that the ecological environmental quality was stable in most areas of Jianghan Plain, with the largest proportion of IN levels in each period being 51.38, 71.59, 67.14, and 64.38%, respectively. The proportion of IO and DO change levels was less than 1%, indicating that there was no significant change in ecological environmental quality from 1990 to 2021. The proportions of IS in the four periods were 3.59, 6.60, 26.64, and 16.70%, with the largest percentage in 2006–2014, and 44.86, 21.48, 6.21, and 18.75% in the four periods for DS, respectively, with a decreasing trend in 1990–2014, followed by an increasing trend in 2014–2021. The main reason for this was that the increase in urbanization and the expansion of urban areas led to the deterioration, followed by an improvement in ecological environmental quality on Jianghan Plain.

The size of global spatial autocorrelation indicates the spatial convergence of eco-environmental quality in Jianghan Plain. Figure 7 is Moran’s I scatter diagram of eco-environmental quality in Jianghan Plain. The horizontal axis represents the eco-environmental quality index, and the vertical axis represents the spatial matrix weight of eco-environmental quality. Both of them have been standardized, and the slash represents the linear correlation between them. The slope of the slash (Moran’s I) indicates the global autocorrelation of the eco-environmental quality. As can be seen from Figure 7, Moran’s I indexes from 1990 to 2021 are 0.531, 0.529, 0.525, 0.540, and 0.545, respectively, indicating that the spatial distribution of the eco-environmental quality in Jianghan Plain is positively correlated, that is, the eco-environmental quality in Jianghan Plain does not show complete randomness in space. However, it shows strong spatial aggregation, and its spatial connection characteristics are the sampling grid with high ecological environment quality tends to be adjacent to the sampling grid with high ecological environment quality, and the sampling grid with low ecological environment quality tends to be adjacent to the sampling grid with low ecological environment quality. The calculation shows that Moran’s I showed a gradual downward trend from 1990 to 2006. Then it showed an upward trend from 2006 to 2021, of which Jianghan Plain had the strongest positive correlation in 2021. The changing trend of local clustering is consistent with the changing trend of the eco-environmental quality level in Figure 6.

Local autocorrelation statistical variables can identify different spatial correlation patterns (or aggregation patterns) that may exist in different spatial locations, so as to observe the local instability of space and find the spatial heterogeneity between data. The local spatial autocorrelation is represented by Moran's I scatter diagram. In Figure 7, the first and third quadrants of Moran's I scatter diagram from 1990 to 2021 represent positive spatial correlation, and the second and fourth quadrants represent negative spatial correlation. Among them, the first quadrant represents the coverage of high-high units of ecological environment quality (H-H), the second quadrant represents that low-high units of ecological environment quality are covered by low-high units of ecological environment quality (L-H), the third quadrant represents that low-low units are covered by low-low units (L-L), and the fourth quadrant represents that high-low units are covered by high-low units (H-L). This paper uses geoda9 5 analyze the local spatial autocorrelation Lisa value of ecological environment quality of 3364 sampling grids in Jianghan Plain, as shown in Figure 8.

The LISA map of eco-environmental quality index of Jianghan Plain from 1999 to 2021 shows that it is not significant, and it is mainly distributed in some counties and districts of Jianghan Plain, such as Gongan County. H-H cluster areas are mainly distributed in the north and northwest. From 1990 to 2021, the grid cluster of the H-H cluster area with Jiangling County as the central area increased significantly, the cluster centers increased, and the ecological environment was better. The agglomeration area of L-L is mainly distributed around Honghu city in the east of Jianghan Plain. The reason is that this area is close to the metropolis Wuhan, with a dense population and frequent human activities, but the overall agglomeration characteristics have little change. After 1998, L-L cluster areas continued to increase, mainly due to the deterioration of ecological environmental quality caused by the accelerated development of urbanization. The analysis shows that the H-H of the urban edge is high, and the L-L of the urban edge is low. The spatial agglomeration characteristics of the eco-environmental quality of Jianghan Plain are affected by the landform and socioeconomic development to a certain extent. These reflect the characteristics of natural and socioeconomic development of Jianghan Plain to a certain extent. The agglomeration characteristics of LISA of eco-environmental quality in this study are consistent with the actual situation of Jianghan Plain.