Work

A UAV-Based Salt Stress Response Index for Comparative Evaluation of Salinity-Management Interventions in Maize and Soybean

Year: 2026

Type: article

Abstract: UAV multispectral remote sensing provides an efficient approach for monitoring crop salt stress in saline farmland. This study investigated maize and soybean grown under different salinity-management practices in a coastal saline region. The experiment was conducted at a single site during one growing season, including 14 maize plots and 13 soybean plots with three sampling sites per plot. A Salt Stress Response Index (SSRI) was developed using partial least squares path modeling (PLS-PM) based on baseline soil salinity and four crop growth indicators: leaf area index, plant height, SPAD, and fractional vegetation cover. Boruta was used for vegetation-index selection, and four regression algorithms (PLSR, EN, RF, and GPR) were evaluated for UAV-based SSRI retrieval using leave-one-plot-out cross-validation. SSRI was significantly and negatively correlated with crop yield across growth stages (r = −0.670 to −0.764), supporting its relevance as an integrated indicator of crop stress response. Linear models generally outperformed nonlinear models. EN performed best for maize at the jointing stage (R2 = 0.774, RMSE = 0.124, RPD = 2.103) and soybean at the branching stage (R2 = 0.737, RMSE = 0.131, RPD = 1.952), whereas PLSR performed best for maize and soybean at flowering (R2 = 0.819 and 0.803, respectively). UAV-derived SSRI maps captured within-field spatial heterogeneity, and baseline-salinity-adjusted SSRI enabled exploratory comparison among management practices within the same crop and growth stage. The proposed framework provides a proof-of-concept approach for UAV-based crop salt stress monitoring and exploratory assessment of management responses in saline agricultural systems.

Source: Agriculture

DOI: 10.3390/agriculture16161769

Authors: Chuang Lu, Shiwei Dong, Xueyang Yu, Yinkun Li

Institutions: Beijing Academy of Agriculture and Forestry Sciences, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137