Using Unmanned Aerial Vehicle Data to Improve Satellite Inversion: A Study on Soil Salinity

Ruiliang Liu, Keli Jia (), Haoyu Li and Junhua Zhang
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Ruiliang Liu: School of Geography and Planning, Ningxia University, Yinchuan 750021, China
Keli Jia: School of Geography and Planning, Ningxia University, Yinchuan 750021, China
Haoyu Li: School of Geography and Planning, Ningxia University, Yinchuan 750021, China
Junhua Zhang: School of Ecology and Environment, Ningxia University, Yinchuan 750021, China

Land, 2024, vol. 13, issue 9, 1-19

Abstract: The accurate and extensive monitoring of soil salinization is essential for sustainable agricultural development. It is difficult for single remote sensing data (satellite, unmanned aerial vehicle) to simultaneously meet the requirements of wide-scale and high-precision soil salinity monitoring. Therefore, this paper adopts the upscaling method to upscale the unmanned aerial vehicle (UAV) data to the same pixel size as the satellite data. Based on the optimally upscaled UAV data, the satellite model was corrected using the numerical regression fitting method to improve the inversion accuracy of the satellite model. The results showed that the accuracy of the original UAV soil salinity inversion model ( R 2 = 0.893, RMSE = 1.448) was higher than that of the original satellite model ( R 2 = 0.630, RMSE = 2.255). The satellite inversion model corrected with UAV data had an accuracy of R 2 = 0.787, RMSE = 2.043, and R 2 improved by 0.157. The effect of satellite inversion correction was verified using a UAV inversion salt distribution map, and it was found that the same rate of salt distribution was improved from 75.771% before correction to 90.774% after correction. Therefore, the use of UAV fusion correction of satellite data can realize the requirements from a small range of UAV to a large range of satellite data and from low precision before correction to high precision after correction. It provides an effective technical reference for the precise monitoring of soil salinity and the sustainable development of large-scale agriculture.

Keywords: UAV; Sentinel-2; scale-up; numerical regression fitting; random forest; soil salinity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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