Department of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Faculty of Natural Resource, University of Tehran, Karaj, Iran.
Environ Monit Assess. 2020 Nov 12;192(12):759. doi: 10.1007/s10661-020-08718-z.
In recent years, indirect methods have been used to estimate soil salinity in agricultural lands. In this research, the electrical conductivity of 93 soil samples from 0 to 30 cm and 0 to 100 cm was measured using the hypercube technique at Sharifabad-Saveh Plain, Iran. Land area parameters such as TWI, TCI, STP, DEM, and LS were used as topographic variables and spatial indices of salinity and vegetation were derived from Landsat 8 images. Soil salinity off crops and gardens was determined at 0-30 cm and 0-100 cm. The data were divided into two series: the training set (70%) and the test set (30%). In order to model and predict salinity, models such as an artificial neural network (ANN), integration of neural network and genetic algorithm (ANN-GA), PLSR, and decision tree (DT) were used. The results of the models' evaluation based on MSE and R indices showed that the ANN-GA model has the highest accuracy in predicting soil properties. This model improved the accuracy of soil salinity prediction by 28%, 42%, and 23% in 0-30 cm and by 20%, 28%, and 25% at 100 cm than ANN, PLSR, and DT. The result showed the 2 dS/m EC at alfalfa and cucurbits farmlands while pistachio orchards have low salinity and bare lands have moderate and high salinity.
近年来，人们已经采用间接方法来估算农业用地的土壤盐度。在这项研究中，使用超立方体技术在伊朗沙里法巴德-萨维平原测量了 93 个土壤样本在 0 到 30cm 和 0 到 100cm 处的电导率。土地面积参数，如 TWI、TCI、STP、DEM 和 LS，被用作地形变量，从 Landsat 8 图像中得出盐分和植被的空间指数。在 0-30cm 和 0-100cm 处测定了作物和花园周围的土壤盐分。将数据分为两个系列：训练集（70%）和测试集（30%）。为了对盐分进行建模和预测，使用了人工神经网络（ANN）、神经网络和遗传算法集成（ANN-GA）、偏最小二乘法（PLSR）和决策树（DT）等模型。根据 MSE 和 R 指数评估模型的结果表明，ANN-GA 模型在预测土壤特性方面具有最高的准确性。与 ANN、PLSR 和 DT 相比，该模型在 0-30cm 处提高了土壤盐分预测的准确性 28%、42%和 23%，在 100cm 处提高了 20%、28%和 25%。结果显示，在紫花苜蓿和瓜类农田中，EC 值为 2dS/m，而油橄榄果园盐分较低，裸地盐分中等偏高。
