This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i. e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R 2 , i.e.,~0.6 to~0.7 between SNAPderived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m 2 m-2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R 2 of~0.55 and~0.8 respectively, and RMSE of~0.5 m 2 m-2 and~0.6 m 2 m-2 , respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAPderived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions.

The Leaf Area Index (LAI) is used as input in hydrological and biochemical models for the estimation of water-cycle characteristics, agricultural primary production and other processes. Vegetation Indices (VIs) are used to monitor vegetation state and health. Considering that easily computed VIs can be used for the estimation of LAI, this study applied a regression analysis between MODIS Enhanced Vegetation Index (EVI) and LAI data in five sites around the world. A linear model was found to provide a good description of the LAI-EVI relationship across all examined vegetation types and times. Medium accuracy models were improved when variability of time and vegetation type were considered, indicating that these parameters highly affect the LAI-EVI relationship. Sensitivity of EVI to LAI was higher in periods of high biomass production. Regression analysis between LAI-EVI showed a stronger relationship for the study sites characterized by dry and warm tropical climatic conditions.

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

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