Xinjiang Common University Key Lab of Smart City and Environmental Stimulation, College of Resource and Environmental Sciences, Xinjiang University, Urumqi 830046, China.
Xinjiang Common University Key Lab of Smart City and Environmental Stimulation, College of Resource and Environmental Sciences, Xinjiang University, Urumqi 830046, China; College of Water Sciences, Beijing Normal University, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China.
Sci Total Environ. 2021 Feb 1;754:142030. doi: 10.1016/j.scitotenv.2020.142030. Epub 2020 Aug 29.
Tarim River Basin is experiencing heavy soil degeneration in a long term because of the extreme natural conditions, added with improper human activities such as reclamation and rejected field repeatedly, which hindered the soil health. One of the mainly form is soil salinization. Spatial distribution and variation of soil salinity is essential both for agricultural resource management and local economic development. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since 1980s while land use and climate have undergone major changed. Electromagnetic induction (EMI) has been successfully used to directly measurement the spatial distribution of targeting soil property at field- scale, and apparent electrical conductivity (ECa, mS m) has become a surrogate of soil salinity (EC, dS m) studied by many researchers at local scale. However, the effectiveness of this equipment has not been verified in the typical soil salinization areas in southern Xinjiang, especially on a large scale. This study was aimed to test the performance of ECa jointed with Random Forest (RF) for soil salinity regional-scale mapping at a typical arid area, taking Tarim River Basin as an example. The result showed that ECa together with environmental derivative variables and with RF were suited for regional-scale soil salinity mapping. Predicted accuracy of EC was higher at surface (0-20 cm, R = 0.65, RMSE = 5.59) and deeper soil depth (60-80 cm, R = 0.63, RMSE = 2.00, and 80-100 cm, R = 0.61, RMSE = 1.73), lower at transitional zone (20-40 cm, R = 0.55, RMSE = 2.66, and 40-60 cm, R = 0.51, RMSE = 2.49). When ECa is involved in modeling, the prediction accuracy of multiple depths of EC is improved by 13.33%-61.54%, of which the most obvious depths are 60-80 cm and 0-20 cm. The results of variable importance show that SoilGrids were also favored the power EC model. Hence, we strongly recommended to joint EMI reads with remote sensing imagery for soil salinity monitoring at large scale in southern Xinjiang. These EC and ECa map can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor water and salt dynamics, and a guide for the design of future soil surveys.
塔里木河流域由于极端的自然条件以及开垦和弃耕等不当的人类活动，长期以来一直遭受着严重的土壤退化，这阻碍了土壤健康。其中主要形式之一是土壤盐渍化。土壤盐分的空间分布和变化对于农业资源管理和当地经济发展至关重要。然而，自 20 世纪 80 年代以来，该地区的土壤盐渍化空间分布情况一直没有得到更新，而土地利用和气候已经发生了重大变化。电磁感应（EMI）已成功用于直接测量田间尺度的目标土壤特性的空间分布，并且表观电导率（ECa，mS m）已成为许多研究人员在局部尺度上研究土壤盐分的替代指标。然而，这种设备在新疆南部典型的土壤盐渍化地区的有效性尚未得到验证，特别是在大规模情况下。本研究旨在以塔里木河流域为例，测试 EMI 与随机森林（RF）联合用于典型干旱地区土壤盐分区域尺度制图的性能。结果表明，ECa 与环境衍生变量结合使用 RF 非常适合进行区域尺度的土壤盐分制图。在表层（0-20 cm，R=0.65，RMSE=5.59）和更深的土壤深度（60-80 cm，R=0.63，RMSE=2.00，80-100 cm，R=0.61，RMSE=1.73）预测 EC 的精度更高，在过渡区（20-40 cm，R=0.55，RMSE=2.66，40-60 cm，R=0.51，RMSE=2.49）预测精度较低。当 ECa 参与建模时，EC 的多个深度的预测精度提高了 13.33%-61.54%，其中最明显的深度是 60-80 cm 和 0-20 cm。变量重要性的结果表明，SoilGrids 也有利于 EC 模型的建立。因此，我们强烈建议在新疆南部大规模联合 EMI 读数和遥感图像进行土壤盐分监测。这些 EC 和 ECa 图可以为环境建模提供数据源，为评估和监测水盐动态提供基准，并为未来土壤调查提供指导。
