DOI: 10.5281/zenodo.15671180
title: A 100 m annual soil salinization dataset from 1985 to 2024 in the Western Songnen Plain, China

description: In our study, we identified and classified soil salinization degrees in the Western Songnen Plain at 100 m spatial resolution using ground surveys data and remote sensing imagery, combined with machine learning algorithms over the period 1985 to 2024.

 

The publicly available data used in this paper, as well as the modeling code, are shown below: 

1)        The remote sensing satellite data used in this study are freely available on GEE platform (available at https://code.earthengine.google.com/). 

2)        Land cover data can be accessed at https://doi.org/10.5281/zenodo.4417810. 

3)        SSSG dataset is available at  https://files.isric.org/public/global_soil_salinity. 

4)        LDSS data are available at https://doi.org/10.6084/m9.figshare.13295918.v1. 

5)        Yearly identification results of saline soils are available at https://code.earthengine.google.com/3dd47875d7455825297b9a6a8766a8ee. 

6)        The data required for model inputs (TIR、SIT、PDI) are available at https://code.earthengine.google.com/7fc0e7ee2cf4cb6b744aaf4706a140c9.

 

The presented data file contains:

1)  Soil sampling metadata

Filename: Soil_EC_sampling_points.csv

Format: CSV

Description: Contains georeferenced soil electrical conductivity observations used for model training and validation.






Column



Description







Municipal



Administrative region (city) where the point is located





LnEC



Natural logarithm of observed soil EC1:5 (in dS m⁻¹), measured in lab





TIR



Thermal infrared reflectance value from Landsat imagery





SIT



Salinity Index based on Red, NIR, and SWIR bands





PDI



Perpendicular Drought Index, used as a proxy for surface soil moisture





 

2) Model files



TIRSITPDI_predicted.mat


Format: Matlab .mat file

Description: Contains the trained Neural Network Fitting (NNF) model for soil EC prediction. This model was optimized using 14,000 iterations and parameter tuning (e.g., number of hidden layers, learning rate, activation function).



Soil_EC_prediction_model.m


Format: MATLAB script

Description: Implements the prediction process. It reads spectral input parameters (TIR, SIT, PDI), applies the trained model, and outputs predicted soil EC values.

3) Annual Salinity Mapping Outputs (1985–2024)

These folders contain annual gridded maps and summary statistics derived from the soil EC prediction model.

📁 Statistical_results_by_year/

Contents: CSV tables and a .png summarizing the area (in km²) of saline soils in each salinity class per year.

Classes: Slightly saline (2–4 dS m⁻¹), Moderately saline (4–8), Highly saline (8–16), Extremely saline (>16)

📁 Salinization_degree_maps/

Contents: Raster maps (GeoTIFF, EPSG:4326, 100 m resolution) of classified salinity zones for each year (1985–2024) based on U.S. Salinity Laboratory classification. A .png contains year-by-year classified salinity zones.

📁 Saline_soil_identification/

Contents: Binary maps (GeoTIFF) showing annual identification of saline vs. non-saline soils from 1985 to 2024. A .png contains year-by-year identification results.

📁 Soil_EC_prediction_results/

Contents: Continuous raster maps (GeoTIFF, EPSG:4326, 100 m resolution) representing the predicted soil EC values (in dS m⁻¹) for each year (1985–2024).