Estimating soil salinity under sunflower cover in the Hetao Irrigation District based on unmanned aerial vehicle remote sensing. (6th October 2022)

• Record Type: Journal Article
• Authors: Cui, Xin; Han, Wenting; Zhang, Huihui; Cui, Jiawei; Ma, Weitong; Zhang, Liyuan; Li, Guang
• Is Part Of: Land degradation & development. Volume 34:Number 1(2023)
• Page Start: 84
• Page End: 97
• Publication Date: 2022-10-06
• Subjects: artificial neural network (ANN) -- leaf area index -- remote sensing -- soil salinization -- unmanned aerial vehicle; Land degradation -- Periodicals; Soil conservation -- Periodicals; Reclamation of land -- Periodicals; Land use -- Periodicals; Economic development -- Environmental aspects -- Periodicals; 333.7315
• DOI: 10.1002/ldr.4445
• Languages: English
• ISSNs: 1085-3278

Abstract

Abstract: Soil salinization is one of the important factors limiting the sustainable development of agriculture and causing land degradation. In order to explore the effect of growth stage division on the estimation accuracy of soil salt content (SSC) in fields cropped with sunflower, multiple time‐series unmanned aerial vehicle (UAV) were used to monitor SSC in the Hetao Irrigation District, Inner Mongolia, China. Aerial and field campaigns for four growth stages of sunflowers in six study areas were conducted from July to September in 2021. The ground samplings of electrical conductivity (EC), leaf area index (LAI), plant height (H), and leaf chlorophyll content (CHL) were taken simultaneously with (UAV) multispectral images. The correlation between six vegetation indices (VIs), four salinity indices (SIs), three crop parameters (LAI, CHL, H) and SSC was investigated. The optimal parameters were determined and used as input variables to establish SSC estimation model using artificial neural network (ANN), random Forest (RF), multiple linear regression (MLR) algorithm, respectively. The results show that the division of growth stages could improve the correlation between spectral index, growth parameters and SSC, and the estimation model by each growth stage was more accurate than that of the whole growth period. Among the spectral indices, the VIs showed a higher correlation with SSC than the SIs. Among the crop parameters, the LAI was the most sensitive to the degree of soil salinization. The nonlinear regression algorithm (ANN, RF) performed better than the linear regression model (MLR) in the application of SSC estimation, and the best estimation models for the four growth stages of sunflower were the ANN_SSC models. This study proposed a fast and low‐cost method to monitor the soil salinization of sunflower‐cropped fields in time‐series and provided a reference for the quick perception and prevention of soil salinization.