A 40-year dataset of soil salinity dynamics (1985–2024) at 100 m resolution in the Western Songnen Plain, China

In our study, we identified saline soil area and predicted soil salinity 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 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) The NLS are available at https://gtdc.mnr.gov.cn/Share#/secondSurvey. All source scripts used for saline soil identification model, soil EC prediction model and prediction model input parameters are publicly available on GitHub at: https://github.com/mercyxinian/Code.git. The presented data file contains: 1) Soil sampling metadata Filename: Soil_EC_sampling_points.csv Format: CSV (UTF-8 encoded) Description: Contains georeferenced field observations of soil EC, used for prediction model training and validation. Column Description ID Unique identifier for each sample point Lon Longitude coordinate of each sample point Lat Latitude coordinate of each sample point Municipal Administrative region (city) where the point is located EC Observed soil EC (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: 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 Degree Mapping (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 degree per year. Classes: Slightly saline (2–4 dS m⁻¹), Moderately saline (4–8), Highly saline (8–16), Extremely saline (>16) 📁 Salinity_degree_maps/ Contents: Raster maps (GeoTIFF, EPSG:4326, 100 m resolution) of classified salinity degree for each year (1985–2024) based on U.S. Salinity Laboratory classification. The .png contains year-by-year salinity degree. 📁 Saline_soil_identification/ Contents: Binary maps (GeoTIFF) showing annual identification of saline vs. non-saline soils from 1985 to 2024. The .png contains year-by-year identification results.本研究以1985-2024年为研究时段，基于野外实地调查数据与遥感影像，结合机器学习算法，以100米空间分辨率对松嫩平原西部盐碱土面积进行识别，并对土壤盐分等级进行预测。 本文所使用的公开数据集如下： 1) 本研究使用的遥感卫星数据可在GEE（Google Earth Engine）平台获取，网址为https://code.earthengine.google.com/。 2) 土地覆被数据可通过https://doi.org/10.5281/zenodo.4417810获取。 3) SSSG数据集可通过https://files.isric.org/public/global_soil_salinity获取。 4) LDSS数据可通过https://doi.org/10.6084/m9.figshare.13295918.v1获取。 5) NLS数据集可通过https://gtdc.mnr.gov.cn/Share#/secondSurvey获取。 本研究用于盐碱土识别模型、土壤电导率（Electrical Conductivity, EC）预测模型及预测模型输入参数的所有源代码，均已在GitHub平台公开，仓库地址为https://github.com/mercyxinian/Code.git。 本次公开的数据文件包含以下内容： 1) 土壤采样元数据 文件名：Soil_EC_sampling_points.csv 格式：CSV（UTF-8编码） 描述：包含带地理坐标的野外土壤电导率观测值，用于预测模型的训练与验证。 列名 | 描述 --- | --- ID | 每个采样点的唯一标识符 Lon | 采样点的经度坐标 Lat | 采样点的纬度坐标 Municipal | 采样点所在的行政区（市级） EC | 实验室测定的土壤电导率（Electrical Conductivity, EC），单位为dS m⁻¹ TIR | 来自Landsat影像的热红外反射率 SIT | 基于红光、近红外（Near Infrared, NIR）和短波红外（Short Wave Infrared, SWIR）波段构建的盐分指数 PDI | 垂直干旱指数，用作表层土壤湿度的替代指标 2) 模型文件 TIRSITPDI_predicted.mat 格式：Matlab .mat文件 描述：包含训练完成的土壤电导率预测神经网络拟合（Neural Network Fitting, NNF）模型，该模型经14000次迭代与参数调优（如隐藏层数量、学习率、激活函数）完成优化。 Soil_EC_prediction_model.m 格式：MATLAB脚本 描述：实现土壤电导率预测流程：读取光谱输入参数（TIR、SIT、PDI），加载训练好的模型并输出预测的土壤电导率值。 3) 1985-2024年年度盐分等级制图 该部分包含基于土壤电导率预测模型得到的年度栅格制图与汇总统计结果： 📁 Statistical_results_by_year/ 内容：年度CSV统计表与一张.png图片，汇总了各年份各盐分等级盐碱土的面积（单位：km²）。 盐分等级分类标准：轻度盐碱（2–4 dS m⁻¹）、中度盐碱（4–8 dS m⁻¹）、重度盐碱（8–16 dS m⁻¹）、极重度盐碱（>16 dS m⁻¹）。 📁 Salinity_degree_maps/ 内容：各年份（1985-2024年）的分级盐分等级栅格地图（GeoTIFF格式，EPSG:4326坐标系，100米分辨率），分类体系采用美国盐度实验室标准。附带的.png图片展示了逐年盐分等级分布情况。 📁 Saline_soil_identification/ 内容：1985-2024年逐年盐碱土与非盐碱土的二值化栅格地图（GeoTIFF格式），附带的.png图片展示了逐年识别结果。