Wang Jiaqiang, Hu Bifeng, Liu Weiyang, Luo Defang, Peng Jie
College of Agriculture, Tarim University, Alar 843300, China.
Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Tarim University, Alar 843300, China.
Sensors (Basel). 2023 Aug 7;23(15):7003. doi: 10.3390/s23157003.
Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 00.375 m, 00.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale.
土壤盐渍化是干旱地区土地生产力、作物产量和作物品质的主要障碍，直接影响粮食安全。土壤剖面盐分数据是准确确定灌溉量的关键。为了探索利用Landsat 8时间序列数据监测土壤盐渍化的潜力，从中国西北部新疆阿拉尔垦区获取了2013年至2019年的172幅Landsat 8图像。通过16种植被指数的年度最大值或最小值合成多年极值数据集，并将其与30个不同盐渍化程度棉田00.375米、00.75米和0~1.00米深度土壤剖面的540个样本的土壤电导率相结合，这些样本由EM38-MK2进行调查。利用Cubist方法构建了不同深度土壤电导率的三种遥感监测模型，并进行了数字制图。结果表明，0至0.375米、0至0.75米和0至1.00米土壤剖面电导率的Cubist模型具有较高的预测精度，预测集的决定系数分别为0.80、0.74和0.72。因此，利用植被指数的多年极值结合Cubist建模方法在区域尺度上监测土壤剖面盐渍化是可行的。
