Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination ($R^2$) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.

In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI ($R^2=0.91$, $R^2=0.86$) and lowest for SLA mapping ($R^2=0.53$). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME.

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching $R^2$ = 0.92 (LAI), $R^2$ = 0.72 (CCC) and 0.68 (CWC), $R^2$ = 0.94 (FAPAR), and $R^2$ = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between $4\%$ and $16\%$ ($R^2$ between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.

Computational thinking (CT) is increasingly incorporated into curricular planning across various educational levels in numerous countries. Presently, CT is being integrated into preschool and primary education. To effectively implement CT at the classroom level, the design and study of techniques and tasks are crucial. This research empirically evaluates a didactic sequence using programmable educational robots for problem-solving challenges rooted in mathematical concepts. The study consists of two sets of activities: computational localisation of elements on a regular grid, where students program robots to navigate, and problem-solving tasks involving sum calculations using distinct pre-operational strategies. The study sample is a class of 16 students at the preschool level. The results indicate an increasing complexity in the success of the designed sequence, with the ’counting all’ strategy demonstrating higher efficacy. These promising findings highlight the potential for further research, aiming to establish a strong foundation for early educational levels through the integration of CT via programmable robots and engaging problem-solving challenges.

In recent years, most educational institutions have integrated digital technologies into their teaching–learning processes. Learning Management Systems (LMS) have gained increasing popularity, particularly in higher education, due to their ability to manage teacher–student interactions. These systems store valuable information which describes students’ behaviour throughout a course. These data can be utilised to construct statistical models that represent learner behaviour within an online LMS platform. In this study, we aim to compare different sources of information and, more ambitiously, to provide insights into which source of information is most valuable for inferring student performance. The considered sets of information come from (i) the Moodle LMS; (ii) socio-economic data about students acquired from a survey; and (iii) subject marks achieved throughout the course. To determine the relevance of the incorporated information, we use artificial intelligence (AI) methods, and we report the importance measures of four state-of-the-art methods. Our findings indicate that the selected methodology is suitable for making inferences about student performance while also shedding light on model decisions through explainability.

Advanced retrieval models allow us to make inferences from the signals acquired remotely by satellites to a set of variables, to better understand and describe the states and dynamics of croplands. One essential variable is canopy nitrogen content (CNC), being one of the most relevant traits for agricultural monitoring applications. In the next coming years, there will be an increasing amount of available data acquired by a new generation of hyperspectral satellites (image spectrometer missions), such as PRISMA, and upcoming EnMAP and CHIME missions. When dealing with hyperspectral satellite data, the curse of dimensionality and the effects of noise can be successfully alleviated through feature (band) selection procedures. In our proposed setting, most meaningful spectral bands for the retrieval of CNC were selected, providing a lower spectral subset of the original data but maintaining the physical meaning of each spectral band. Radiative transfer models (RTM) simulate bi-derectional reflectance as a function of diverse biochemical and biophysical input parameters. In this way, RTMs allow to build upon new methods and prepare future missions due to its capability of simulating real scenarios based on their physical consistent definition. In this work, we focus on the leaf optical properties model PROSPECT-PRO coupled with the canopy reflectance 4SAIL model to establish a training database for Gaussian process (GP) regression algorithms. The proposed methodology performs regression from input values, the reflectance, to the output values, the biophysical parameters or traits of interest. In this work, we explored a spectral band selection tool (GPR-BAT) embedded in the ARTMO toolbox (https://artmotoolbox.com/), dedicated to the transformation of optical remote sensing images into biophysical vegetation products and maps. GPR-BAT is based on a sequential backward band removal (SBBR) algorithm that iteratively removes the spectral bands which contribute less to the regression model. This procedure is repeated until only one relevant band is left over. GPR-BAT allows to: i) identify the most informative or relevant bands to estimate one specific biophysical or biochemical variable, and ii) find a smaller set of bands preserving the optimal predictions. The optimal set of 15 bands achieved a coefficient of determination (R²) of 0.6 and a normalised root mean squared error (NRMSE) of 19 % to retrieve canopy nitrogen content sampled over maize and winter wheat during a field campaign in the North of Munich, Germany (MMNI site), during 2017 and 2018 growing seasons. Furthermore, a variance-based global sensitivity analysis of the PROSAIL-PRO model confirmed the optimal position of the identified band setting within the nitrogen (protein) sensitive wavelength domain. The optimal set of bands were to be found in the near infrared and in the short wave infrared, especially in the 1700-1800 nm region. Applying the established models on acquired PRISMA images revealed the adequacy of the proposed method for mapping applications. We conclude that our proposed methodology achieved promising results both in accuracy of estimates and mapping quality over different geographical regions.