Estimation of Aboveground Biomass of Chinese Milk Vetch Based on UAV Multi-Source Map Fusion

Chinese milk vetch (CMV), as a typical green manure in southern China, plays an important role in improving soil quality and partially substituting nitrogen chemical fertilizers for rice production. Accurately estimating the aboveground biomass (AGB) of CMV is crucial for quantifying the biological nitrogen fixation amount (BNFA) and assessing its viability as a nitrogen fertilizer alternative. However, the traditional estimation methods have low efficiency in field-scale evaluations. Recently, unmanned aerial vehicle (UAV) remote sensing technology has been widely adopted for AGB estimation. This study utilized UAV-based multispectral and RGB imagery to extract spectral (Sp), textural (Tex), and structural features (Str), comparing various feature combinations in AGB estimation for CMV. The results indicated that the fusion of spectral, textural, and structural features indicated optimal estimation performance across all feature combinations, resulting in R2 values of 0.89 and 0.83 for model cross-validation and spatial transferability validation, respectively. The inclusion of textural and spectral features notably improved AGB estimation, indicated an increase of 0.15 and 0.14 in R2 values for model cross-validation and spatial transferability validation, respectively, compared with relying on spectral features only. Estimation based exclusively on structural features resulted in R2 values of 0.65 and 0.52 for model cross-validation and spatial transferability validation, respectively. The present study establishes a rapid and extensive approach to evaluate the BNFA of CMV at the full blooming stage utilizing the optimal AGB estimation model, which will provide an effective calculation method for chemical fertilizer reduction.