Mapping soil salinity in irrigated areas using hyperspectral UAV imagery

• Published in: Guanʻgai paishui xuebao Vol. 44; no. 2; pp. 72 - 82
• Main Authors: ZHOU Shixun, YIN Juan, WANG Juntao, CHANG Buhui, YANG Zhen
• Format: Journal Article
• Language: Chinese
• Published: Science Press 01.02.2025
• Subjects: soil salinization; hyperspectral; spectral transformation; inversion model; spatial distribution
• ISSN: 1672-3317
• DOI: 10.13522/j.cnki.ggps.2024066

【Objective】Soil salinization induced by poor irrigation management poses a significant challenge to irrigated agriculture, reducing soil productivity and crop yields. Estimating soil salinity and its spatial distribution in irrigated areas can help improve soil and irrigation management. The objective of this paper is to use hyperspectral inversion techniques and a develop model to accurately estimate soil salinity and its distribution in the Hetao Irrigation District. 【Method】The experiment was conducted in the Shenwu Irrigation Area, where spectral reflectance and salinity data were measured and collected from 253 soil samples. Fifteen spectral transformations were applied to improve the correlation between hyperspectral data and soil salinity. Four models, including multiple linear stepwise regression (MLSR), partial least squares regression (PLSR), support vector machine regression (SVR), and backpropagation neural network (BPNN), were evaluated for their accuracy to estimate soil salinity. The most accur