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

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.

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

“…Satellite-UAV-ground integration is a promising significant way to achieve high-precision land surface properties monitoring. The satellite-ground spectral fusion and satellite-UAV collaboration integration methods in this study have significantly improved the accuracy of soil salinity inversion results in cotton fields in coastal areas, compared to the methods of UAV-ground inversion and satellite-UAV collaboration integration and UAV-ground spectral fusion and model upscaling integration in previous studies (Qi et al, 2021;Qi et al, 2020). Compared with the integrated method of pixel true value upscaling, the method based on satellite-ground spectrum fusion and satellite-UAV collaboration integration introduces the fusion of ground imaging hyperspectral and satellite spectrum, which enriches the spectral information of satellites and improves the correlation between spectrum and soil salinity.…”

“…The closer R 2 is to 1, the smaller the RMSE and the larger the RPD, indicating that the higher the precision of model and the better the modeling effect. According to relevant grading standards (Qi et al, 2021), the degree of soil salinisation is divided into five grades: nonsalinization (<1g·kg −1 ), mild salinisation (1–2 gkg −1 ), moderate salinisation (2–4 gkg −1 ), severe salinisation (4–6 gkg −1 ), and saline soil (>6 gkg −1 ), and a distribution map of soil salinity in a cotton field is generated.…”

“…At present, the fusion of remote sensing data from three satellites platforms, UAVs and the ground‐ to monitor surface information has been explored (Kang et al, 2005). Other examples of studies include: soil nutrients of grassland (Sankey et al, 2021), the soil salinity of wheat fields (Qi et al, 2020), and maize fields (Qi et al, 2021), monitored by satellite‐UAV‐ground integration. These research studies prove that the fusion and collaboration of remote sensing data from the three platforms can significantly improve the degree of fine acquisition of land surface information by large‐scale satellite data.…”

Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China

“…The image scale conversion based on numerical regression has a simple principle and is easy to implement, but it lacks sufficient extrapolation ability and portability. Gao et al applied the Bayesian method [49], Pereira et al applied the geostatistical method [50], and Qi et al applied the TsHARP method [51] to achieve image scale conversion, respectively. Different methods have their advantages and disadvantages, and how to use other multi-platform remote sensing data to realize the collaborative inversion of CLQ is still worthy of further study.…”

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

“…The average ratio adjustment method is a common method for combining UAV and satellite images; it uses the reflectance ratio of corresponding bands with an approximate wavelength range between the UAV and satellite multispectral data to correct the satellite images [ 30 , 31 ]. For example, the average ratio adjustment method has been used to correct Sentinel-2A satellite images to invert SSC in the YRD [ 32 ]. However, wavelength range and the central wavelength generally diverge from the corresponding bands of UAV and satellite multispectral data.…”