# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
Hybrid methods combine physical models with the computational efficiency of non-parametric regression methods [6,7]. Machine learning techniques including Neural Networks (NNs) and Gaussian Process Regression (GPR) are widely used. The NNs require less formal statistical training and can implicitly detect complex nonlinear relationships between dependent and independent variables. The GPR allows handling the model selection issue within a Bayesian framework automatically, which offers the potential advantage of avoiding the traditional empirical and tricky tuning of the free parameters of the model [7]. There are already a wide range of existing remote sensing LAI products with different spatial and temporal resolutions based on above algorithms, including MODerate resolution Imaging Spectroradiometer (MODIS, 500 m, 4-day) [8], Copernicus Global Land Service (CGLS) VEGETATION and PROBA-V bioGEOphysical product Version 2 (GEOV2, 1 km, 10-day) [9], CGLS PROBA-V bioGEOphysical product Version 3 (GEOV3, 300 m, 10-day) [10], Satellite Application Facility EUMETSAT Polar System (SAF EPS, 1.1 km, 10-day) [11], Global LAnd Surface Satellite (GLASS, 500 m, 8-day) [12] and Copernicus Climate Change Service (C3S) PROBA-V product Version 2 (C3S V2, 1 km, 10-day) [13] products. Scientific validation is critical to understand their accuracy in order to quantitatively characterize uncertainties embedded in LAI products and acquire key feedback for algorithm improvement [14]. The Land Product Validation (LPV) subgroup of the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) proposed a direct approach for validating coarse resolution LAI products based on the comparison with ground-based reference maps [15]. The field-measured LAI cannot be directly used as a reference for the validation of satellite products due to the scale differences and surface heterogeneity issues [16]. To alleviate the footprint differences, the field-measured LAI acquired within decametric elementary sampling units (ESUs) are scaled up via high-resolution data [2,17] and empirical calibration functions. Then, the high-resolution ground-based maps are aggregated to match the spatial resolution of the LAI products and used as a reference for their validation [15]. A series of studies on the validation of global LAI products have been carried out based on this upscaling validation strategy [18,19]. Up to now, the majority of validation activities have been conducted for hectometric and kilometric (300-1000 m) coarse resolution products [20,21]. Only few studies have focused on the validation of decametric LAI products [19,22,23]. In addition, the existing validation activities only have focused on a single scale although the LAI products across different scales may be inconsistent [24]. Therefore, a joint validation exercise of multi-resolution LAI products is imperative to elucidate scale effects on different LAI products. The validation of temporal performance is also extremely lacking, especially for multi-resolution LAI products. Multi-temporal validation activities are key since the temporal consistency of satellite time series and their accuracy to monitor actual vegetation phenology is mandatory for a successful application of LAI products in land surface and climate models, and in environmental and global change research [25]. Accurate and representative field measurements are key for the validation of LAI satellite products. The direct LAI measurements using litter fall traps and destructive harvest techniques are accurate but time and labor consuming [26]. Currently, the most commonly used LAI field measurements rely on indirect methods based on the light transmittance through the canopy as measured with optical instruments, such as Digital Hemispherical Photographs [27], Tracing Radiation and Architecture of Canopies [28] and LAI-2000 Plant Canopy Analyzer [29], among others [30].