Comparative research on multi-algorithm of soil salinity monitoring based on Gaofen-5, Sentinel-1, and Sentinel-2

ABSTRACT

Soil ecosystem provides a guarantee for the ecological environment. Soil salinization is a typical land degradation problem. In order to explore the applicability of GF-5, Sentinel-1, and Sentinel-2 remote sensing data in soil salinity research, this study selected Golmud of Qaidam Basin in China as the study area, using particle swarm optimization-artificial neural network (PSO-ANN), partial least squares regression (PLSR), and whale optimization algorithm-extreme learning machine (WOA-ELM) to predict soil salinity. The research results indicated that the correlation between Sentinel-2 and electric conductivity (EC) was higher than GF-5 and Sentinel-1. The simulation accuracy of models based on Sentinel-2 was slightly higher than that based on GF-5 and Sentinel-1. The three models’ mean values of root mean square error (RMSE), R², and mean absolute error (MAE) of Sentinel-2 were slightly better than those of GF-5 and Sentinel-1, and the fitting accuracy of GF-5 was similar to that of Sentinel-1 on the whole. The result proved that Sentinel-1, GF-5, and Sentinel-2 were suitable for salinity monitoring because the strong penetrability and sensitivity of Sentinel-1, the high spectral resolution of GF-5, and the high spatial resolution of Sentinel-2. The performance of PSO-ANN was the best, and that of PLSR was the worst. The salinity mapping result of Sentinel-2 coupled with PSO-ANN proved that machine learning methods and spaceborne data could be used to study large-scale soil salinization.

KEYWORDS:

• Salinization
• PSO-ANN
• spaceborne remote sensing
• multi-algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Due to the nature of this research, participant authors of this study did not agree for their data to be shared publicly.

Additional information

Funding

This work was supported by Strategic Planning for the Alashan SEE Desertification Prevention and Control Vegetation Restoration and Protection (i.e. Haloxylon ammodendron+) Project [Grant No. E0490203] and the Second Tibetan Plateau Scientific Expedition and Research Program [STEP, Grant No. 2019QZKK0305].