Molaeinasab Azita, Bashari Hossein, Esfahani Mostafa Tarkesh, Pourmanafi Saeid, Toomanian Norair, Aghasi Bahareh, Jalalian Ahmad
Department of Natural Resources, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Soil and Water Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, Iran.
Sci Rep. 2025 Jul 2;15(1):22809. doi: 10.1038/s41598-025-04554-8.
Digital Soil Mapping (DSM) techniques have advanced significantly in recent decades, helping to close critical gaps in soil data and knowledge. This study was conducted in the arid Gavkhouni sub-basin of Isfahan Province, central Iran, where environmental stresses such as salinity and water scarcity challenge sustainable land management. We employed 34 environmental covariates derived from Landsat 8 imagery and a digital elevation model, combined with 96 surface soil samples (0 to 20 cm depth), to assess the performance of six machine-learning models: Random Forest (RF), Classification and Regression Tree (CART), Support Vector Regression (SVR), Generalized Additive Model (GAM), Generalized Linear Model (GLM), and an ensemble approach. Unlike many previous studies that have focused on a single soil attribute with a limited set of predictors, our work adopts an integrated approach to map four salinity-related soil properties: Ca, CaCO, CaSO, and SO. Predictor selection involved multicollinearity testing using the Variance Inflation Factor (VIF) and the Boruta algorithm. Model performance was assessed using tenfold cross-validation. The ensemble model performed best, achieving R values of 0.89 for Ca, 0.84 for CaCO, 0.79 for SO, and 0.73 for CaSO. Elevation and the Temperature-Vegetation Dryness Index (TVDI) were the most influential predictors for Ca, while the Tasseled Cap Brightness (TCB) and Tasseled Cap Wetness (TCW) indices were most important for CaCO. For CaSO, Band 5 (B5) and TCB were the most effective, whereas SO predictions were driven by TCB along with Bands 5 and 7. These findings highlight the potential of remote sensing-based DSM to enhance soil monitoring in data-scarce, arid environments. The growing availability of free satellite data, such as Landsat, offers valuable opportunities to improve soil assessment and promote sustainable land management in resource-limited regions like Iran.
近几十年来，数字土壤制图（DSM）技术取得了显著进展，有助于填补土壤数据和知识方面的关键空白。本研究在伊朗中部伊斯法罕省干旱的加夫胡尼子流域进行，该地区盐度和水资源短缺等环境压力对可持续土地管理构成挑战。我们利用从Landsat 8影像和数字高程模型中提取的34个环境协变量，结合96个表层土壤样本（深度0至20厘米），评估六种机器学习模型的性能：随机森林（RF）、分类与回归树（CART）、支持向量回归（SVR）、广义相加模型（GAM）、广义线性模型（GLM）以及一种集成方法。与以往许多专注于单一土壤属性且预测变量集有限的研究不同，我们的工作采用综合方法来绘制四种与盐度相关的土壤属性图：钙（Ca）、碳酸钙（CaCO₃）、硫酸钙（CaSO₄）和硫酸根（SO₄²⁻）。预测变量选择涉及使用方差膨胀因子（VIF）和博鲁塔算法进行多重共线性检验。模型性能通过十折交叉验证进行评估。集成模型表现最佳，钙的R值为0.89，碳酸钙为0.84，硫酸根为0.79，硫酸钙为0.73。海拔和温度植被干旱指数（TVDI）是钙的最具影响力的预测变量，而缨帽亮度（TCB）和缨帽湿度（TCW）指数对碳酸钙最为重要。对于硫酸钙，波段5（B5）和TCB最为有效，而硫酸根的预测则由TCB以及波段5和7驱动。这些发现凸显了基于遥感的DSM在数据稀缺的干旱环境中加强土壤监测的潜力。免费卫星数据（如Landsat）的日益普及，为改善伊朗等资源有限地区的土壤评估和促进可持续土地管理提供了宝贵机会。
