Land surface bidirectional reflectance distribution functions (BRDF) are critical for quantitative remote sensing but are significantly constrained by scale effects, limiting the interoperability of multi-resolution data and the accuracy of quantitative inversion, thereby rendering the investigation of BRDF multi-scale effects increasingly urgent. This study utilized UAV (Unmanned Aerial Vehicle)-based multi-angular observations and the RPV model to retrieve the BRDF of typical land covers, employing the Window Averaging Method to simulate multi-scale responses and systematically investigate the relationship between BRDF characteristics and spatial scale. The results indicate the following key findings: (1) The RPV (Rahman–Pinty–Verstraete) model demonstrated high robustness and inversion accuracy, yielding RMSE (Root Mean Square Error) below 0.06 and RRMSE (Relative RMSE) below 25% across all land covers, with the 840 nm band exhibiting superior performance. (2) Significant spatial scale effects were observed, where BRDF characteristics varied distinctively with scale but eventually stabilized at specific thresholds; specifically, the stabilization scales were identified as 1.3 m for bare soil, 1.5 m for tea plantations, 1 m for rice, and 2 m for forests. (3) The scale evolution of BRDF features exhibited a parallel trend with spatial heterogeneity, a correlation that enables the quantitative identification of optimal observation scales for different land cover types.

Accurate retrieval of leaf area index (LAI), chlorophyll content (Cab), and canopy water content (Cw) is critical for growth monitoring and irrigation scheduling in spring wheat. Yet the quantitative impact of unmanned aerial vehicle (UAV) multispectral spatial resolution on the retrieval accuracy of these key traits remains elusive, and whether a single optimal resolution can support coordinated multi-parameter monitoring is unresolved. To bridge this gap, this study generated 14 spatial resolutions (0.07–3.03 m) by pixel aggregation resampling from the original four-band UAV multispectral imagery with a native spatial resolution of 0.07 m, acquired at the jointing and filling stages. By coupling the PROSAIL radiative transfer model with random forest, we systematically assessed scale-dependent retrieval performance and employed texture entropy to elucidate how spatial structure governs accuracy. Our results revealed that retrieval accuracy of all three parameters exhibited non-monotonic responses to changing spatial resolution. At the jointing stage, optimal resolutions diverged markedly—0.49 m for LAI, 2.03 m for Cab, and 2.80 m for Cw. Following canopy closure at the filling stage, the optimal resolution converged uniformly to 2.03 m across all parameters. Mechanistically, this non-monotonic variation was closely associated with the combined behavior of spectral coefficient of variation and texture entropy, rather than with any single indicator alone. Building on these findings, we propose a growth-stage-adaptive resolution strategy and develop a Heterogeneity-Scale Game Model (HSGM) as an exploratory framework to characterize the formation mechanism of optimal aggregation scales. This study moves beyond empirical scale selection and establishes an exploratory framework for multi-parameter crop monitoring, delivering actionable guidance for selecting appropriate spatial scales in UAV data processing and multi-scale parameter retrieval in precision agriculture.