Identification and Spatial Analysis of Land Salinity in China's Yellow River Delta Using a Land Salinity Monitoring Index from Harmonized UAV-Landsat Imagery

Precise identification and spatial analysis of land salinity in China's Yellow River Delta are essential for the rational utilization and sustainable development of land resources. However, the accurate retrieval model construction for monitoring land salinity remains challenging. This study constructed a land salinity retrieval framework using a harmonized UAV and Landsat-9 multi-spectral dataset. The Kenli district of the Yellow River Delta was selected as the case study area, and a land salinity monitoring index (LSMI) was proposed based on field survey data and UAV multi-spectral image and applied to the reflectance-corrected Landsat-9 OLI image. The land salinity distribution patterns were then mapped and spatially analyzed using Moran's I and Getis-Ord GI* analysis. The results demonstrated the following: (1) The LSMI-based method can accurately retrieve land salinity content with a validation determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of 0.75, 1.89, and 2.11, respectively. (2) Land salinization affected 93.12% of the cultivated land in the study area, and the severely saline soil grade (with a salinity content of 6-8 g/kg) covered 38.41% of the total cultivated land area and was widely distributed throughout the study area. (3) Saline land exhibited a positive spatial autocorrelation with a value of 0.311 at the p = 0.000 level; high-high cluster types occurred mainly in the Kendong and Huanghekou towns (80%), while low-low cluster types were mainly located in the Dongji, Haojia, Kenli, and Shengtuo towns (88.46%). The spatial characteristics of various salinity grades exhibit significant variations, and conducting separate spatial analyses is recommended for future studies.

Automated summary

Precise identification and spatial analysis of land salinity in China's Yellow River Delta are crucial for the rational use and sustainable development of land resources. However, constructing an accurate retrieval model for monitoring land salinity remains a challenge. This study developed a retrieval framework using a combination of UAV and Landsat-9 multi-spectral data and successfully mapped the distribution patterns of land salinity in the Kenli district of the Yellow River Delta. The study demonstrated the effectiveness of the method and highlighted the need for separate spatial analyses for different salinity grades.