Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring land areas facilitates management ecological restoration. In this research, we integrated salinization index (SI), albedo, normalized difference vegetation (NDVI) surface soil moisture (LSM) through principal component analysis (PCA) method establish (SDI). Based on SDI, typical area Central Asia Amu Darya delta (ADD) was analysed for period 1990–2019. The results showed that proposed SDI had high positive correlation (R2 = 0.89, p < 0.001) with salt content based field sampling, indicating can reveal characteristics ADD. indicated extreme strong increased from 1990 2019, mainly downstream peripheral regions From 2000, improvement over larger than developed, conversely, 2000 especially, 2010, proportion developed 32%, which concentrated region spatial autocorrelation values Moran’s I 1990, 2010 2019 were 0.82, 0.78, 0.82 0.77, respectively, suggesting notably clustered space rather randomly distributed. expansion unused due use change, water withdrawal River discharge all contributed This study provides several valuable insights into monitoring similar settings worldwide.