College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China.
College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China; Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China; Key Laboratory of Xinjiang Wisdom City and Environment Modeling, Urumqi 830046, China.
Sci Total Environ. 2018 Feb 15;615:918-930. doi: 10.1016/j.scitotenv.2017.10.025. Epub 2017 Oct 7.
Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid regions. Producers and decision-makers thus require updated and accurate maps of salinity in agronomical and environmentally relevant regions. The goals of this study were to test various regression models for estimating soil salt content based on hyperspectral data, HJ-CCD images, and Landsat OLI data using; develop optimal band Difference Index (DI), Ratio Index (RI), and Normalization Index (NDI) algorithms for monitoring soil salt content using image and spectral data; and to compare the performances of the proposed models using a Bootstrap-BP neural network model (Bootstrap-BPNN) from different data sources. The results showed that previously published optimal remote sensing parameters can be applied to estimate the soil salt content in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). Optimal band combination indices based on DI, RI, and NDI were developed for different data sources. Then, the Bootstrap-BP neural network model was built using 1000 groups of Bootstrap samples of remote sensing indices (DI, RI and NDI) and soil salt content. When verifying the accuracy of hyperspectral data, the model yields an R value of 0.95, a root mean square error (RMSE) of 4.38g/kg, and a residual predictive deviation (RPD) of 3.36. The optimal model for remote sensing images was the first derivative model of Landsat OLI, which yielded R value of 0.91, RMSE of 4.82g/kg, and RPD of 3.32; these data indicated that this model has a high predictive ability. When comparing the salinization monitoring accuracy of satellite images to that of ground hyperspectral data, the accuracy of the first derivative of the Landsat OLI model was close to that of the hyperspectral parameter model. Soil salt content was inverted using the first derivative of the Landsat OLI model in the study area.
土壤盐度被公认为是农业的主要威胁，特别是在干旱地区。生产者和决策者因此需要更新和准确的农业和环境相关地区盐度地图。本研究的目的是测试各种回归模型，根据高光谱数据、HJ-CCD 图像和 Landsat OLI 数据估算土壤盐分含量；开发最佳波段差值指数（DI）、比值指数（RI）和归一化指数（NDI）算法，利用图像和光谱数据监测土壤盐分含量；并利用来自不同数据源的 Bootstrap-BP 神经网络模型（Bootstrap-BPNN）比较所提出模型的性能。结果表明，先前发表的最佳遥感参数可应用于估算艾比湖湿地国家级自然保护区（ELWNNR）的土壤盐分含量。为不同数据源开发了基于 DI、RI 和 NDI 的最佳波段组合指数。然后，使用遥感指数（DI、RI 和 NDI）和土壤盐分含量的 1000 组 Bootstrap 样本构建 Bootstrap-BP 神经网络模型。在验证高光谱数据的精度时，模型的 R 值为 0.95，均方根误差（RMSE）为 4.38g/kg，剩余预测偏差（RPD）为 3.36。遥感图像的最佳模型是 Landsat OLI 的一阶导数模型，其 R 值为 0.91，RMSE 为 4.82g/kg，RPD 为 3.32；这些数据表明该模型具有较高的预测能力。在比较卫星图像和地面高光谱数据的盐渍化监测精度时，Landsat OLI 模型一阶导数的精度接近高光谱参数模型。在研究区域，利用 Landsat OLI 模型的一阶导数反演土壤盐分含量。
