A novel remote sensing monitoring index of salinization based on three-dimensional feature space model and its application in the Yellow River Delta of China

Abstract

Previous studies were mostly conducted based on two-dimensional feature space to monitor salinization, while studies on dense long-term salinization monitoring based on three-dimensional feature space have not been reported. Based on Landsat TM/ETM+/OLI images and three-dimensional feature space method, this study introduced six typical salinization surface parameters, including NDVI, salinity index, MSAVI, surface albedo, iron oxide index, wetness index to construct eight different feature space monitoring index. The optimal soil salinization monitoring index model was proposed base on field observed data and then the evolution process of salinization in Yellow River Delta (YRD) were analyzed and revealed during 1984–2022. The salinization monitoring index model of MSAVI-Albedo-I_Fe2O3 feature space had the highest accuracy with R² = 0.93 and RMSE = 0.678g/kg. The spatial distribution of salinization in YRD showed an increasing trend from inland southwest to coastal northeast and the salinization intensity showed an increasing trend during 1984–2022 due to the implements of agricultural measures such as planting salt-tolerant crops, microbial remediation and fertility improvement. The rate of salinization deterioration in the northeast part was greater than others. Zones of salinization improvement were mainly located in cultivated land of the southwest parts.

Keywords:

• Soil salinization
• 3-D feature space
• Landsat images
• spatial distribution
• vegetation index

Acknowledgements

This work was supported by Natural Science Foundation of Shandong Province (grant no. ZR2021MD047); National Natural Science Foundation of China (grant no.42101306); Open fund of Key Laboratory of National Geographic Census and Monitoring, MNR (grant no.2020NGCM02); the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (grant no. KF-2020-05-001), and Agricultural Science and Technology Innovation Program (grant no. CAAS-ZDRW202201).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Guo B, upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the authors.