Document Type

Dissertation - Open Access

Award Date


Degree Name

Doctor of Philosophy (PhD)


Plant Science

First Advisor

David E. Clay


agriculture, agronomy, Google Earth Engine, machine learning, Random Forest, soil salinity


Climate and land-use changes when combined with the marine sediments that underlay portions of the Northern Great Plains have increased the salinization and sodification risks. The objectives of this dissertation were to compare three chemical amendments (calcium chloride, sulfuric acid and gypsum) remediation strategies on water permeability and sodium (Na) transport in undisturbed soil columns and to develop a remote sensing technique to characterize salinization in South Dakota soils. Fortyeight undisturbed soil columns (30 cm x 15 cm) collected from White Lake, Redfield, and Pierpont were used to assess the chemical remediation strategies. In this study the experimental design was a completely randomized design and each treatment was replicated four times. Following the application of chemical remediation strategies, 45.2 cm of water was leached through these columns. The leachate was separated into 120- ml increments and analyzed for Na and electrical conductivity (EC). Sulfuric acid increased Na leaching, whereas gypsum and CaCl2 increased water permeability. Our results further indicate that to maintain effective water permeability, ratio between soil EC and sodium absorption ratio (SAR) should be considered. In the second study, soil samples from 0-15 cm depth in 62 x 62 m grid spacing were taken from the South Dakota Pierpont (65 ha) and Redfield (17 ha) sites. Saturated paste EC was measured on each soil sample. At each sampling points reflectance and derived indices (Landsat 5, 7, 8 images), elevation, slope and aspect (LiDAR) were extracted. Regression models based on multiple linear regression, classification and regression tree, cubist, and random forest techniques were developed and their ability to predict soil EC were compared. Results showed that: 1) Random forest method was found to be the most effective method because of its ability to capture spatially correlated variation, 2) the short wave infrared (1.5 -2.29 μm) and near infrared (0.75-0.90 μm) were very sensitive to soil salinity; 3) EC prediction model using all 3 season (spring, summer and fall) images was better on state wide validation dataset compared to individual season model. Finally, in eastern South Dakota, the model predicted that from 2008 to 2012, EC increased in 569,165 ha or 13.4% of the land seeded to corn (Zea mays L.) or soybeans (Glycine max L).

Library of Congress Subject Headings

Soils, Salts in -- Great Plains

Soils, Salts in -- South Dakota


Climatic changes -- Great Plains

Climatic changes -- South Dakota

Landscape changes -- Great Plains

Landscape changes -- South Dakota


Includes bibliographical references (page 63-66)



Number of Pages



South Dakota State University


Copyright © 2016 Tulsi P. Kharel