Abstract
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 (R2 = 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.
Highlights
College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China; The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin’an, Hangzhou 311300, China; Abstract: Soil salinization is a significant factor affecting corn growth in coastal areas
The results indicate that the spatial distribution trends of soil salinity in the two trend surfaces are basically the same; the trend surface transfer function can well reflect the spatial distribution trend of soil salinity in the study area
The fusion of the four bands of the unmanned aerial vehicles (UAV) image with the ground hyperspectral improved the degree of fitting with the hyperspectral data
Summary
College of Information Science and Engineering, Shandong Agricultural University, Tai’an 271018, China; National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China; The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Lin’an, Hangzhou 311300, China; Abstract: Soil salinization is a significant factor affecting corn growth in coastal areas. The inversion results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with the measured soil salinity (R2 = 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. With the change of natural environment and the disturbance of human behavior, regional salinization becomes more and more serious, which affects the sustainable development of agriculture coastal areas to a great extent [7] It is of great significance for agricultural production and sustainable development to accurately extract the soil salinization status and grasp its spatial distribution law in the main crop corn planting area of coastal areas. The traditional method of obtaining soil salinity information in the published maps and institutional affiliations
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