Seasonal and vertical variation in canopy structure and leaf spectral properties determine the canopy reflectance of a rice field

Physical model simulations have been widely utilized to simulate the reflectance of vegetation canopies. Such simulations can be used to estimate key biochemical and physical vegetation parameters, such as leaf chlorophyll content (LCC), leaf area index (LAI), and leaf inclination angle (LIA) from remotely sensed data via model inversion. In simulations, field crops are typically regarded as one-dimensional (1D) vegetation canopies with constant leaf properties in the vertical direction and across the growing season. We investigated the seasonal effects of these two simplifications, 1D canopy structure, and vertically constant leaf properties, on canopy reflectance simulations in a rice field using in situ measurements and the 3D discrete anisotropic radiative transfer model (DART). We also developed a new methodology for reconstructing 3D crop canopy architecture, which was validated using measurements of gap fraction and canopy reflectance. Our results revealed that the 1D canopy assumption only holds during the early stage of the growing season, then leaf clumping affects canopy reflectance from the jointing stage onwards. Consideration of the 3D canopy structure and its seasonal variation significantly reduced the deviation between simulated and measured canopy reflectance in the green and nearinfrared wavelengths when compared to the typical 1D canopy assumption and produced the closest multiangular distribution pattern to the measurements. The vertical heterogeneity of leaf spectra affected canopy reflectance weakly during the maturation stage when senescence started from the bottom of the canopy. Consideration of seasonal and vertical variation in LIAs significantly improved the results of 1D canopy reflectance simulations, including the multi-angular distribution patterns. In contrast, the directionally-averaged clumping index (CI) only slightly improved the 1D canopy reflectance simulation. To summarize, these findings can be used to reduce the simulation bias of canopy reflectance and improve the retrieval accuracy of key vegetation parameters in crop canopies at the seasonal scale.