| 02 Dec 2025
Spatiotemporal Characterization of Wheat Development Using UAV LiDAR Structure–Intensity Fusion with Multispectral and Thermal Data
Abstract. This study presents the first integration of UAV LiDAR structure (canopy height (CH), multi-layer gap fraction (GF)) and intensity features with multispectral (MS) and thermal infrared (TIR) data for aboveground biomass (AGB) estimation in winter wheat. A shallow artificial neural network (ANN), trained on a limited but high-quality destructive dataset, enabled direct integration of multi-sensor features without complex parameterization, supporting systematic evaluation of individual and combined sensor performance. Among single-sensor inputs, LiDAR features were most effective. LiDAR alone, combining all of its features such as CH, multi-layer GF, and INT, achieved a testing RMSE of 1.73 t/ha (18.27% error) and R² = 0.87, surpassing the common reliance on CH or MS features in UAV-based AGB studies. Multi-layer GF also improved accuracy compared to conventional ground-return GF and was successfully used as a direct ANN input. Fusion with other sensors further enhanced performance, with the best model (LiDAR INT + MS + TIR) reaching a testing RMSE of 1.47 t/ha (16.3% error) and R² = 0.91. Notably, this outperformed fusion models that included LiDAR CH or GF, indicating that INT is a particularly information-rich predictor likely encoding both structural and physiological canopy properties. Furthermore, sensor contributions varied seasonally, with CH and GF most informative during early growth and canopy closure, while MS and TIR became dominant during senescence and stress, with rankings providing practical guidance for sensor selection based on monitoring periods or economic constraints. Results from nitrogen treatments indicated that UAV data captured management effects more effectively than destructive sampling, highlighting the value of spatially comprehensive observations, an advantage that can be further enhanced through the fusion of emerging UAV sensor products. Overall, the findings position LiDAR’s dual structural and spectral information, particularly INT, as a promising breakthrough for improving UAV-based AGB monitoring, with strong potential to advance multi-sensor fusion approaches as algorithms and crop applications broaden.
This manuscript addresses UAV-based multi-sensor fusion for biomass estimation in winter wheat. The dataset is valuable and the full-season evaluation is useful. However, several scientific, methodological, and interpretive issues limit the strength of the conclusions. Many claims are overstated or insufficiently supported by the analysis presented. The paper requires significant refinement before it meets high scientific standards.
Major Issues 1. Novelty is overstated
The manuscript repeatedly claims to be the first to integrate LiDAR CH, multi-layer GF, LiDAR intensity, MS, and TIR for biomass estimation. This is not accurate. Multi-sensor fusion combining LiDAR and MS has been done, including in wheat. LiDAR + TIR exists in related cereal studies. The unique contribution here is the specific configuration and systematic comparison, not the existence of the fusion itself.
The novelty claim must be rewritten. As written, it will not pass expert scrutiny.
2. Interpretation of LiDAR intensity is not adequately justified
The paper makes strong claims about LiDAR intensity encoding physiological and structural canopy traits. These claims are not sufficiently supported.
The conclusions around LiDAR intensity are overstated and require substantial tempering. At present, the manuscript treats INT as if it were a calibrated spectral variable, which it is not.
3. The ANN modelling framework lacks statistical rigor
The modelling approach is a weak point of the paper.
As it stands, the modelling is not statistically robust enough to support the strong performance claims made throughout the manuscript.
4. Multi-layer GF method needs deeper justification
The multi-layer GF analysis is a valuable idea, but the implementation and interpretation need more discipline.
The method shows potential, but the manuscript overstates its generality and does not provide enough evidence for the proposed optimal configuration beyond this specific dataset.
5. Sensor dominance over time is overstated
The temporal analysis suggests shifts in sensor utility across growth stages. While the general trends are plausible, the manuscript repeatedly makes categorical statements (e.g., “MS dominates during senescence,” “CH dominates early”) without rigorous statistical backing.
These conclusions need to be presented as observations from this dataset, not generalizable statements about sensor behaviour.
6. Nitrogen treatment analysis draws conclusions not supported by data
Figure 12 shows expected spatial smoothing from UAV-based predictions compared to subplot-level destructive samples. This does not prove that UAV “captures management effects more effectively.” It only shows that UAV sampling is spatially denser.
The manuscript conflates spatial resolution advantages with biological sensitivity. This needs correction.
Other Critical Points
Overall Recommendation: Major Revision
The study contains useful data and a potentially meaningful contribution, but the manuscript currently over-interprets several findings and lacks the methodological rigor needed to substantiate its strongest claims. The modelling framework must be strengthened, novelty claims must be corrected, and conclusions around LiDAR intensity and temporal sensor dominance must be tempered and grounded.
With substantial revision, this paper could be publishable, but it does not meet the standard required in its current form.