Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partial least squares inversion model was constructed by the fused UAV image. Then, inversion model was scaled up to the satellite by the TsHARP method, and finally, the accuracy of the satellite-UAV-ground inversion model and results was verified. The results show that the band fusion of UAV and ground images effectively enrich the spectral information of the UAV image. The accuracy of the inversion model constructed based on the fused UAV images was improved. The inversion results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with the measured soil salinity (R-2 = 0.716 and RMSE = 0.727), and the inversion model had excellent universal applicability. This research integrated the advantages of multi-source data to establish a unified satellite-UAV-ground model, which improved the ability of large-scale remote sensing data to finely indicate soil salinity.

Automated summary

This research utilized multi-source remote sensing data to successfully conduct soil salinity inversion in Kenli District of the Yellow River Delta, establishing a universal satellite-UAV-ground integration model.