Estimating soil salinity in mulched cotton fields using UAV-based hyperspectral remote sensing and a Seagull Optimization Algorithm-Enhanced Random Forest Model

Xinjiang is a crucial production base for mulched cotton fields but faces increasingly severe challenges of soil salinization. Under the current drive towards precision agriculture, high precision monitoring of agricultural conditions at the field scale has become critical. Traditional remote sensing technologies are limited by insufficient spatial and spectral resolution, making field monitoring under mulched conditions exceptionally challenging. This study introduces a novel approach combining drone-based hyperspectral remote sensing with the Seagull Optimization Algorithm-Enhanced Random Forest Model (SOA-RF). After employing the preprocessing techniques of standard normal variable (SNV) and fractional order derivative (FOD), we employed the model to assess the level of field-scale salinization under optimization of feature selection using separate dimensionality reduction algorithms (sequential projection algorithm (SPA), optimal band combination analysis (OBCA)) and a combined approach (SPA-OBCA). The following main results were found: (1) SNV and FOD enhanced the spectral characteristics of cotton canopies, with the FOD technique performing better in detecting subtle signal changes in the positive and negative peaks of the spectrum. (2) SPA and OBCA were effective in screening the soil salt-sensitive bands in cotton canopies but the combined SPA-OBCA method provided a better selective preference for hyperspectral bands, with an optimal correlation of 0.926. (3) The model combined the advantages of SOA with the power of Random Forest to optimize the parameters and improve the estimation accuracy (R2 = 0.925, RPD = 4.128). We used unmanned aerial vehicle hyperspectral imagery to create a 5-cm resolution map of soil salinity distribution in cotton fields that allowed detection of differences in soil salinity between mulches, thus providing a scientific basis for soil salinity management and the development of precision agriculture.