Enhanced cotton chlorophyll content estimation with UAV multispectral and LiDAR constrained SCOPE model

Abstract

Accurate and non-destructive estimation of leaf chlorophyll content (LCC) is crucial for optimizing cotton production. This study enhances the SCOPE model by integrating unmanned aerial vehicle (UAV)-derived multispectral data with leaf area index (LAI) from LiDAR data, significantly improving precision of LCC estimation, particularly during crucial growth stages of cotton. We construct and analyze three cost functions: COST1, which relies solely on spectral data; COST2, which incorporates direct LAI inputs; and COST3, which adjusts for LAI measurement uncertainties by combining spectral term with an error term representing the squared relative error between measured and model-estimated LAI. Our findings indicate that while COST1 establishes a baseline, COST2 and COST3 provide more accurate LCC estimations. COST3, validated against theoretical data, field-measured cotton datasets, and an additional maize dataset, proves most robust, maintaining consistent accuracy across all growth stages especially when considering input data uncertainties. This highlights the importance of integrating appropriate forms of LAI in cost functions to refine LCC estimation. Future research should focus on improving data acquisition quality and developing more advanced cost functions to advance LCC estimation further.

Keywords

• Cotton leaf chlorophyll content
• SCOPE model
• UAV remote sensing
• Multispectral-LiDAR fusion
• Multi-temporal analysis