Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach
Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m<sup>−1</sup>) by combining envi...
Main Authors: | Salman Naimi, Shamsollah Ayoubi, Mojtaba Zeraatpisheh, Jose Alexandre Melo Dematte |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-11-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/23/4825 |
Similar Items
-
Soil salinity inversion based on differentiated fusion of satellite image and ground spectra
by: Hongyan Chen, et al.
Published: (2021-09-01) -
Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China
by: Jiaqiang Wang, et al.
Published: (2021-01-01) -
A PLSR model to predict soil salinity using Sentinel-2 MSI data
by: Sahbeni Ghada
Published: (2021-08-01) -
Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta
by: Guanghui Qi, et al.
Published: (2020-11-01) -
Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach
by: Guanghui Qi, et al.
Published: (2021-08-01)