The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases.

Spatial heterogeneity of vegetation canopies significantly influences the ecosystem energy exchange and gross primary production (GPP) by affecting processes, such as, the photosynthetic light absorption, energy distribution, and carbon fixation. Conventional one-dimensional (1D) models cannot account for horizontal structural diversity of complex canopies as, for instance, sparse row crops or crops in early growth stages. Therefore, the primary aim of this study was to enhance the accuracy of GPP estimations by incorporating three-dimensional (3D) plant structural complexity. To achieve this goal, a first version of the 3D energy balance DART-EB model was developed by combining the Discrete Anisotropic Radiative Transfer (DART), effectively capturing full 3D canopy structure, with modified energy balance (EB) equations originating from the 1D model SCOPE. Unlike the 1D approach, DART-EB iteratively resolves EB for individual elements of a 3D soil–vegetation scene (i.e., soil surface, plant leaves, and stems) separately. High computational efficiency of DART’s LuxCorRender Monte Carlo bi-directional photon-path tracing algorithm allows to compute emitted and absorbed radiation per side of a leaf-representing triangular facet. To verify DART-EB performance, we first cross-compared its results with outputs of the widely used SCOPE model for a virtual homogeneous canopy. A strong consistency between the two models was confirmed by their high agreement in simulated net photosynthesis (d-index = 1; RRMSE = 3 %). Once verified, DART-EB was tasked to perform a comparison between geometrically-explicit vertically-homogeneous and spatially-heterogeneous vineyard canopies. Omitting the spatial heterogeneity of a vine canopy structure led to 44 % RRMSE in simulated net photosynthesis. Further comparison of DART-EB outputs against eddy-covariance measurements of an alfalfa crop in Spain revealed a significantly lower error (RRMSE = 8 %) for its genuine than for a homogeneous canopy representation (RRMSE = 20 %). The spatially-explicit digital 3D representations of alfalfa field were reconstructed from their in-situ RGB photographs. The error reduction was caused by more accurate modelling of a total absorbed photosynthetically active radiation by chlorophylls, enabled by a realistic 3D spatial distribution of the alfalfa foliage. The presented first version of DART-EB continues to be further developed, aiming to increase the computational accuracy, efficiency, and applicability to structurally highly complex canopies (e.g., forests) and large landscapes.