Author(s)

Tongrui Zhang, Gengxing Zhao, Chunyan Chang, Zhuoran Wang, Ping Li, Deyu An, Jichao Jia

College of Resources and Environment, National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Shandong Agricultural University, Tai’an, China.

In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.

The Yellow River Delta, Landsat Imagery, SSIE Model, NDVI, Soil Salinity

Zhang, T. , Zhao, G. , Chang, C. , Wang, Z. , Li, P. , An, D. and Jia, J. (2015) Information Extraction Method of Soil Salinity in Typical Areas of the Yellow River Delta Based on Landsat Imagery. Agricultural Sciences, 6, 71-77. doi: 10.4236/as.2015.61006.