@article{MAKHILLJEAS2017122315259,
    title = {Using Regression Models to Predict Electrical Conductivity of
Soil Through ALOS PALSAR Satellite},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {23},
    pages = {7276-7279},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.7276.7279},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.7276.7279},
    author = {Walaiporn},
    keywords = {Electrical conductivity,soil salinity,ALOS,regression model,sufficiency,relationship},
    abstract = {The electrical conductivity is dielectric properties and able to identify normal soil and soil salinity.
EC values is the method able to classify soil salinity levels quickly. To determine soil salinity from the field
experience is very complicated and difficult. ALOS PALSAR is known as penetrated satellite data. They have
been proved as a powerful tool to indicate the accuracy of salinity value in saline conditions. This reserce to
study the sufficiency of EC as derived from ALOS PALSAR satellite data to predict EC values associated with
soil salinity. A regression model was used to create an EC estimation model. This research developed an
estimation model that could explain the EC of saline soil. This research illustrated that a relationship between
two different data sources, ALOS PALSAR and ground data, the statistical model could be developed to
accurately estimate the value of EC soil using ALOS satellite.}
    }