A 100 m annual soil salinization dataset from 1985 to 2024 in the Western Songnen Plain, China

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 EC sampling points 2) TIRSITPDI_predicted.mat 3) Soil_EC_prediction_model.m 4) Statistical results of soil salinization degree by area (1985-2024) 5) Yearly degree of soil salinization (1985-2024) 6) Yearly identification results of saline soils (1985-2024) 7) Yearly soil EC results (1985-2024) Samples: The file, Soil_EC_sampling_points.csv, provides essential information on 942 sample points. Below are the descriptions for each column in the metadata: Municipal: Location where the point is located LnEC: The observed LnEC using a conductivity meter (Light Magnetic, model: DDS-307A). TIR、SIT、PDI: Characteristic parameters in the models. Models: We trained on this data using eight models (NNF, GPR, LSBoost, KPLS, Tree, SVM, LR, and SVM Kernel) from the Matlab (R2024a) Toolbox. To optimize the NNF for accurate soil salinity estimation (R² = 0.467; RMSE = 0.729 dS m⁻¹), we trained the model using nested loops and fine-tuned parameters such as the training algorithm, the number of hidden layer nodes, and the learning rate. After 14,000 rounds of cycling, the optimal model TIRSITPDI_predicted.mat is finally obtained. Finally, the characteristic parameters with the model are put into Soil_EC_prediction_model.m to obtain the soil salinity values. Results: Statistical results of soil salinization degree by area (1985-2024) shows statistical results for each class (Slightly, Moderately, Highly, Extremely) as well as total saline acreage in the Western Sonnen Plain for the years 1985-2024. Yearly degree of soil salinization (1985-2024) presents the results of saline soil classification based on the U.S. Salinity Laboratory’s classification system for the Western Sonnen Plain from 1985-2024. Yearly identification results of saline soils (1985-2024) displays the results of saline soil identification in the Western Sonnen Plain from 1985-2024. Yearly soil EC results (1985-2024) exhibits the results of model-predicted soil EC content in the Western Sonnen Plain for the years 1985-2024.本研究以1985—2024年为研究时段，以松嫩平原西部为研究区，结合地面调查数据与遥感影像，采用机器学习算法，以100米空间分辨率（spatial resolution）开展土壤盐渍化（soil salinization）程度识别与分级工作。 本文所使用的公开数据集与建模代码如下所示： 1) 本研究使用的遥感卫星数据可在谷歌地球引擎（Google Earth Engine，GEE）平台免费获取（链接：https://code.earthengine.google.com/）。 2) 土地覆盖（land cover）数据可通过以下链接获取：https://doi.org/10.5281/zenodo.4417810。 3) SSSG数据集可通过以下链接获取：https://files.isric.org/public/global_soil_salinity。 4) LDSS数据可通过以下链接获取：https://doi.org/10.6084/m9.figshare.13295918.v1。 5) 逐年盐渍土壤识别结果可通过以下链接获取：https://code.earthengine.google.com/3dd47875d7455825297b9a6a8766a8ee。 6) 模型输入所需的TIR、SIT、PDI参数数据可通过以下链接获取：https://code.earthengine.google.com/7fc0e7ee2cf4cb6b744aaf4706a140c9。 本次提交的数据文件包含以下内容： 1) 土壤电导率（electrical conductivity，EC）采样点数据 2) TIRSITPDI_predicted.mat 3) Soil_EC_prediction_model.m 4) 1985—2024年各区域土壤盐渍化程度统计结果 5) 1985—2024年逐年土壤盐渍化程度 6) 1985—2024年逐年盐渍土壤识别结果 7) 1985—2024年逐年土壤电导率结果 样本数据： 文件Soil_EC_sampling_points.csv包含942个采样点的核心信息。元数据（metadata）中各字段的说明如下： Municipal：采样点所在行政区划 LnEC：使用雷磁DDS-307A型电导率仪测得的LnEC观测值。 TIR、SIT、PDI：模型所用特征参数。 模型构建： 本研究基于Matlab（R2024a）工具箱中的8种模型（NNF、GPR、LSBoost、KPLS、Tree、SVM、LR及SVM Kernel）对上述数据开展训练。为优化NNF模型以实现精准的土壤盐渍化估算（决定系数R²=0.467，均方根误差（root mean square error，RMSE）=0.729 dS·m⁻¹），我们采用嵌套循环训练模型，并对训练算法、隐含层节点数、学习率等参数进行微调。经过14000轮迭代后，最终得到最优模型TIRSITPDI_predicted.mat。最后将模型特征参数导入Soil_EC_prediction_model.m，即可得到土壤盐渍化数值。 结果分析： 1985—2024年各区域土壤盐渍化程度统计结果包含了松嫩平原西部各盐渍化等级（轻度、中度、重度、极重度）的统计结果以及总盐渍化面积。 1985—2024年逐年土壤盐渍化程度基于美国盐度实验室分类系统，展示了1985—2024年松嫩平原西部的盐渍土壤分级结果。 1985—2024年逐年盐渍土壤识别结果展示了1985—2024年松嫩平原西部的盐渍土壤识别结果。 1985—2024年逐年土壤电导率结果展示了1985—2024年松嫩平原西部经模型预测得到的土壤电导率含量结果。