农业机械学报2026，Vol.57Issue(20)：108-117,10.DOI:10.6041/j.issn.1000-1298.2026.20.010
基于Transformer光谱匹配的星机协同滨海盐渍化农田土壤含盐量监测方法
Satellite-UAV Collaborative Monitoring Method for Soil Salinity in Coastal Saline Farmland Based on Transformer Spectral Matching
摘要
Abstract
Satellite-UAV collaborative remote sensing provides an important approach for spatial monitoring of soil salinity content(SSC)in coastal saline farmland.However,in such regions,soil salinity shows strong spatial heterogeneity and surface cover is complex.Sentinel-2 multispectral instrument(MSI)imagery was limited by band configuration,spectral resolution and mixed-pixel effects,resulting in limited expression of salt-sensitive information.To alleviate the influence of cross-sensor spectral differences on SSC estimation from Sentinel-2 MSI imagery,a satellite-UAV collaborative soil salinity monitoring method was proposed based on Transformer spectral matching.Coastal saline farmland in Huanghua City,Hebei Province,China,was selected as the study area,and measured SSC,UAV hyperspectral imagery and Sentinel-2 MSI imagery were acquired during the same period.Firstly,the UAV hyperspectral imagery was resampled to 10 m and spatially aligned with Sentinel-2 MSI imagery.Then,using UAV hyperspectral matched-band reflectance as a reference,multilayer perceptron(MLP)and Transformer models were used to construct nonlinear spectral matching models from Sentinel-2 MSI to the UAV hyperspectral reference space.Finally,original bands and salinity spectral indices were constructed,salt-sensitive features were selected,SSC was estimated by using a random forest(RF)model,and model accuracy was evaluated by leave-one-out cross-validation.Results showed that the coefficient of determination(R2)of the original Sentinel-2-RF model was 0.37,and the root mean square error(RMSE)and mean absolute error(MAE)were 0.82 g/kg and 0.62 g/kg,respectively.After MLP spectral matching,R2 was increased to 0.45,and RMSE and MAE were 0.77 g/kg and 0.61 g/kg,respectively.After Transformer spectral matching,R2 was further increased to 0.49,and RMSE and MAE were 0.74 g/kg and 0.58 g/kg,respectively.Compared with the original Sentinel-2-RF model,the Sentinel-2_Transformer-RF model increased R2 by 0.12 and reduced RMSE by 0.08 g/kg.The results indicated that Transformer spectral matching can enhance the expression of salt-sensitive information in Sentinel-2 MSI imagery and provide a methodological reference for SSC spatial distribution estimation in coastal saline farmland.关键词
土壤含盐量/星机协同/光谱匹配/Transformer/Sentinel-2Key words
soil salt content/satellite-UAV collaboration/spectral matching/Transformer/Sentinel-2分类
农业科技引用本文复制引用
顾晓鹤,翟胜楠,刘星宇,张宝元,屈旭洲,吴文彪..基于Transformer光谱匹配的星机协同滨海盐渍化农田土壤含盐量监测方法[J].农业机械学报,2026,57(20):108-117,10.基金项目
本文为退化耕地监测研究成果 （）
