Evaluation of optimum season selection for construction RSEI: A case study of ecological environment quality assess-ment in the Beijing-Tianjin-Hebei region from 2001 to 2020

Timely and objective assessment of the optimal season for the construction of remote sensing ecological index (RSEI) is of great significance for accurate and effective assessment of ecological environment quality. We manipulated RSEI in different seasons to monitor and evaluate seasonal ecological environment quality (EEQ) variations in the Beijing-Tianjin-Hebei （JJJ） region from 2001 to 2020. First, we evaluated the image quality, and the bad observations were interpolated. Second, the seasonal RSEI was constructed by MODIS, and we compared the eigenvalues contribution rate of PC1 in seasons. Third, we assessed the temporal and spatial variations in EEQ across the same season within distinct years. Third, Moran’s I was employed to evaluate the spatial autocorrelation of EEQ and the stability of mean and standard deviation of correlation between RSEI and four indicators of seasons was compared. The results showed that: 1)the PC1 component concentrates most of the characteristics of the four indicators, especially in summer (over 71%); 2) the Moran’ I in the summer of 2001, 2006, 2011, 2016, and 2020 are 0.909, 0.898, 0.917, 0.921, and 0.892, respectively, which indicated that the EEQ has a strong positive spatial correlation. 3) the correlation between the four indicators and summer RSEI showed high correlation in different years, and the standard deviation of the correlation between the four indicators and RSEI fluctuated most slightly in summer, which the std of NDVI, WET, LST, and, NDBSI were 0.005, 0.052, 0.026, and 0.017, respectively. 4) Only the RSEI in summer and the VCF show spatial distribution consistency in the long time series RSEI spatial distribution of four seasons. This study explored the spatiotemporal variations of EEQ in the JJJ region at a seasonal scale, which can provide a reference for selecting the optimum season for the ecological quality monitoring of urban agglomeration in the future.