Two-year remote sensing and ground verification: Estimating chlorophyll content in winter wheat using UAV multi-spectral imagery

Leaf chlorophyll content serves as a critical biophysical indicator for characterizing wheat growth status. Traditional measurement using a SPAD meter, while convenient, is hampered by its localized sampling, low efficiency, and destructive nature, making it unsuitable for high-throughput field applications. To overcome these constraints, this research developed a novel approach for assessing canopy SPAD values in winter wheat by leveraging multispectral imagery obtained from an unmanned aerial vehicle (UAV). The generalizability of this methodology was rigorously evaluated through a replication experiment conducted in a subsequent growing season. Throughout the study, canopy reflectance data were acquired across key phenological stages and paired with synchronized ground-based SPAD measurements to construct stage-specific estimation models. The acquired multispectral images were processed to remove soil background interference, from which 17 distinct vegetation indices and 8 texture features were subsequently extracted. An in-depth examination followed, aiming to clarify the evolving interplay of these features with SPAD values throughout growth phases. Among the vegetation indices, the Modified Climate Change Canopy Vegetation Index (MCCCI) displayed a “rise-and-decline” pattern across the season, aligning with the crop's intrinsic growth dynamics and establishing it as a robust and phonologically interpretable proxy. Texture features, particularly contrast and entropy, demonstrated notable associations with SPAD values, reaching their peak strength during the booting stage. Comparative evaluation of various predictive modeling techniques revealed that a Support Vector Regression (SVR) model integrating both vegetation indices and texture features yielded the highest estimation accuracy. This integrated model outperformed models based solely on spectral or textural data, improving estimation accuracy by 23.81 % and 22.48 %, respectively. The model's strong generalization capability was further confirmed on the independent validation dataset from the second year (RMSE = 2.54, R 2 = 0.748). In summary, this study establishes an effective and transferable framework for non-destructively monitoring chlorophyll content in winter wheat canopies using UAV data. • The precision of UAV-based SPAD estimation varies considerably with wheat developmental phases. • Identifies key features (MCCCI, Contrast, Entropy) strongly linked to winter wheat SPAD values. • Validates robust generalization of SPAD model across growth stages with R 2 = 0.749.