Soil Salinity Mapping Using Machine Learning in Google Earth Engine: Sentinel-2 Data

This tutorial demonstrates how to map soil salinity distribution using machine learning and sentinel-2 imagery in Google Earth Engine. The process is divided into three main steps: 1. Estimating Soil Salinity Distribution: Soil salinity was initially estimated using a spectral index derived from sentinel-2 data. 2. Training Data Collection: Training datasets were created based on the calculated soil salinity index to support the modeling process. 3. Mapping with Machine Learning: A machine learning algorithm was employed to produce a comprehensive map of soil salinity distribution. ------------------------------------------------------------------------------------------------------------- Want to learn more about Google Earth Engine? See here:    / @satelliteremotesensingandgis   ------------------------------------------------------------------------------------------------------------- Join this channel to get access to Satellite Remote Sensing and GIS: URLL:    / @satelliteremotesensingandgis   ------------------------------------------------------------------------------------------------------------- Linkedin:   / satellite-remote-sensing-and-gis-2bba07355   ------------------------------------------------------------------------------------------------------------- Here, you can find a bunch of videos exploring the efficiency of Google Earth Engine:    / @satelliteremotesensingandgis   ------------------------------------------------------------------------------------------------------------- #googleearthengine #soil #SatelliteRemoteSensingandGIS #salinity