Soil health is critical for sustaining ecosystem functions and addressing environmental challenges. Effective soil health management requires reliable methods for assessing soil properties. Soil spectroscopy may allow resource-effective assessment of soil properties, but more knowledge is needed to transfer knowledge from laboratory-grade spectrometers to in-field data acquisition. This study explores the predictive potential of selected spectral subsets from the full visible and near-infrared (VIS–NIR) range, using various machine learning algorithms (MLAs), as a theoretical exercise to support the design of practical soil sensing tools. Specifically, we evaluated whether narrower spectral ranges can provide predictions comparable to those achieved with the full VIS–NIR spectrum. The ranges are chosen to emulate the spectral coverage and resolution of commercially available sensors which are candidates for widespread and resource-effective data collection. We used the VIS–NIR spectral data (400–2500 nm) alongside laboratory analyses of several soil properties to be predicted from the pan-European LUCAS dataset. We employed four different MLAs for estimating soil properties: support vector regression (SVR), cubist, random forest (RF), and multi-layer perceptron (MLP), which were benchmarked against ordinary least squares regression. Our results showed that spectral subset ranges of 1000–2500 nm and 1350–2500 nm (emulating Trinamix and NeoSpectra sensors, respectively) yielded prediction accuracies similar to the full spectrum. Spectral subsets limited to the visible and early NIR range (350–1000 nm) were less effective. The most informative spectral features were found in wavelengths above approximately 1750 nm. Among MLAs, MLP consistently delivered the best performance, particularly when estimating organic carbon, nitrogen, pH and clay, which were predicted with greater accuracy compared to potassium (K), phosphorus (P) and coarse fragments (CF) which cannot yet be robustly predicted from spectral data alone. This study provides preliminary insight into the spectral regions most relevant for soil property prediction. These findings may inform future development and optimisation of real-world soil sensors. Validation with actual sensor data, both on dried and in situ samples, remains an important next step.

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.