A better comprehension of soil properties and processes permits a progress in agricultural management effectiveness, together with a diminution of environmental damage and more beneficial use of resources. This research investigated the usage of multispectral (Sentinel-2 MSI) satellite data at the farm/regional level, for the identification of agronomic bare soil presence, utilizing bands of the spectral range from visible to shortwave infrared. The research purpose was to assess the frequency of cloud-free bare soil time-series images available during the year in typical agricultural areas, needed for the development of digital soil mapping (DSM) approaches for agricultural applications, using hyperspectral satellite missions such as current PRISMA and the planned EnMAP or CHIME. The research exploited the Google Earth Engine platform, by processing all available cloud-free Sentinel-2 images throughout a time span of four years. Two main results were obtained: (i) bare soil frequency, indicating where and when a pixel (or an agricultural field) was detected as bare surface in three representative agricultural areas of Italy, and (ii) a temporal sensitivity analysis, providing the acquisition frequency of useful bare soil images applicable for the retrieval of soil variables of interest. It was shown that, in order to provide for an effective agricultural soil monitoring capability, a revisit frequency in the range of five to seven days is required, which is less than the planned specifications e.g., of PRISMA or CHIME hyperspectral missions, but could be addressed by combining data from the two sensors.

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ ± SE values of 2.32 ± 0.07 for clay, 3.85 ± 0.19 for silt, and 3.51 ± 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.

The olive tree (Olea europaea L.) is the main fruit tree in most of the arid and semi-arid regions of Tunisia, which is where the problem of salinity is more pronounced. Salinity is one of the main factors that affects the productivity of olive trees, so the objective of this experiment was to study the effects of salinity on the photosynthesis, water relations, mineral status, and enzymatic activity of two cultivars of Olea europaea L., ‘Chemlali’ and ‘Koroneiki’. The trial was conducted under controlled conditions in a greenhouse for a period of 49 days and included two treatments: T0 control and T100 (irrigation with 100 mM of NaCl solution). Under salinity stress, the photosynthesis, stomatal conductance, and leaves of both cultivars were negatively affected. ‘Chemlali’ showed greater tolerance to NaCl salinity, based on a progressive decrease in osmotic potential (Ψπ) followed by a progressive and synchronous decrease in gs, without a comparable decrease in photosynthesis. The water use efficiency (WUE) improved as a result. In addition, the K⁺/Na⁺ ratio in ‘Chemlali’ rose. This appears to be crucial for managing stress. Conversely, enzymatic activity showed an accumulation of glutathione peroxidase (GPX) in stressed plants. The catalase (CAT) and ascorbate peroxidase (APX) content decreased in both stressed varieties. It can be concluded that the cultivar ‘Koroneiki’ is more susceptible to salt stress than the cultivar ‘Chemlali’, because the accumulation of GPX and the decreases in CAT and APX were more pronounced in this cultivar.

NIR spectrometers based on micro-electromechanical systems (MEMS) have become available in the market, with lower prices and smaller dimensions than traditional spectrometers. MEMS technology allows for miniaturizing and reduces the cost of the spectrometers, allowing a wider use for agricultural consultants, technicians, and scientific researchers. The aim of this work was to evaluate an innovative FT-NIR MEMS spectrometer, namely the Neospectra Scanner (NS), covering the range from 1350 to 2500 nm. The assessment was performed by comparing the accuracy of prediction of soil organic carbon, texture fractions, and total calcium carbonate, obtained with NS, with that of a standard full VIS-NIR spectrometer, namely the ASD-Fieldspec Fr Pro (AF). A dataset of 182 soil samples, dried and sieved at 2 mm, collected from 4 different agricultural areas of Italy were scanned with both devices. AF showed slightly higher R² and lower prediction error (RMSEP) than NS for all soil features, but the accuracy of the two instruments can be considered comparable. Removing the 350–1350 nm range from VIS-NIR spectra of AF, i.e., as to have the same spectral range of NS, made the prediction accuracy of AF reduced spectra (1350–2500 nm) slightly lower than that of NS. This demonstrates that the lower accuracy of the NS in soil features prediction is not due to the lower resolution of the spectra, but probably due to the lack of visible and beginning of the NIR range (350–1300 nm).

Tunisia is known to be a country with poor water resources, and water scarcity is evident in certain regions. In the long term, this situation could worsen, given the increased risk of drought. The olive tree (Olea europaea L.) is one of the plants best adapted to this climate, and numerous studies have been carried out to assess the effects of water stress. The aim of this work was to study and compare the ecophysiological behavior of a main Tunisian olive cultivar (Chetoui) and four rare Tunisian olive cultivars (Chemchali, Besbessi, Sayali and Jarboui) under drought stress and to identify the main parameters while comparing the tolerance of the cultivars studied to this abiotic stress. One-year-old olive trees grown in pots in a greenhouse were subjected to four drought treatments (i.e., 15, 30, 45 and 60 days of drought stress) in comparison with control trees. The evaluation of the response of the olives to this induced stress was based on five parameters: relative water content (RWC), stomatal resistance (SR), photosystem PSII, maximal photochemical efficiency (F_V/F_M), performance index on absorption basis (PI), measured by the handy PEA, and chlorophyll index, measured by SPAD. The relative water content (RWC) of the five cultivars decreased with increasing drought stress. Jarboui showed lower RWC values than Chemchali, especially under severe drought stress. This result was confirmed by changes in fluorescence characteristics. F_V/F_M, PI and SPAD index decreased during the development of drought stress. These observations are discussed in relation to the strategies developed by the cultivars to grow under drought stress. The Principal Component Analysis allowed the parameter with the strongest loading factor, which is F_V/F_M, to be highlighted and the cultivars most tolerant to drought stress to be distinguished. These cultivars, Besbessi and Sayali in the north of Tunisia and Chemchali in the south, can present a possible alternative to replace the local or foreign cultivars most cultivated in the country, which are characterized by high water needs.