Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

作者：Yuxuan Wang, Zhongyi Qu, Wei Yang, Xi Chen, Tian Qiao · 发表于：Agronomy · 年份：2024 · DOI：10.3390/agronomy14030523 · 被引用次数：10 · 研究领域：Remote Sensing and Land Use、Remote Sensing in Agriculture、Rangeland and Wildlife Management

Soil salinization is a global issue confronting humanity, imposing significant constraints on agricultural production in the irrigated regions along the southern bank of the Yellow River. This, in turn, leads to the degradation of the ecological environment and inadequate grain yields. Hence, it is essential to explore the magnitude and spatial patterns of soil salinization to promote efficient and sustainable agricultural development. This study carried out a two-year surface soil sampling experiment encompassing the periods before spring irrigation and the budding, flowering, and maturity stages of sunflower fields in the irrigated area along the southern bank of the Yellow River. It employed deep learning in conjunction with multispectral remote sensing conducted by UAV to estimate soil salinity levels in the sunflower fields. Following the identification of sensitive spectral variables through correlation analysis, we proceeded to model and compare the accuracy and stability of various models, including the deep learning Transformer model, traditional machine learning BP neural network (BPNN), random forest model (RF), and partial least squares regression model (PLSR). The findings indicate that the precision of soil salinity content (SSC) retrieval in saline–alkali land can be significantly enhanced by incorporating the RE band of UAV data. Four SSC inversion models were developed using the most suitable spectral variables, resulting in precise soil salinity inversion. T...