How effective is information on soil-landscape units for determining spatio-temporal variability of near-surface soil moisture?

In the last decades, a growing interest in fostering advanced interdisciplinary studies is leading to the establishment of observatories in pilot catchments for long-term monitoring of hydrological variables and fluxes. Nevertheless prior to sensor network installation, this investment necessitates preliminary surveys on key-variables such as near-surface soil moisture in order to prevent risks of erronously distributing sensors by missing sufficient spatial information for understanding hydrological processes within land-atmosphere interactions. The availability of maps describing areas with similar morphological, topographical, soil and vegetation characteristics enable preliminary surveys to be organized for capturing spatio-temporal variability of soil moisture as best as possible. The soil-landscape classification can be considered as an interesting approach for grouping mapping units with similar hydrological behavior. We therefore assume soil-landscape units as “hydrotopes” or “hydrological similar units”. Six transects were established along two hillsides of the Upper Alento River catchment (southern Italy) which is a proper candidate to become a Critical Zone Observatory. Specifically, in this paper we use a soil-landscape map to infer spatial and temporal dynamics of soil moisture measured along these transects, whereas quantitative analyses were obtained by using multivariate techniques. The effectiveness of available information on soil-landscape mapping units is evaluated with respect to different observed patterns of soil moisture: wetter- and drier-than average observation points belong to agricultural and forested hillslopes, respectively. Soil texture and topographical controlling factors, especially clay content and slope gradient, are found to explain approximately 70% of the spatial variability of soil moisture in the forested hillslopes. The spatial organization explained by the environmental controlling factors decreases to 45% in the cases of the agricultural hillslopes mainly due to perturbations induced by grazing and tillage practices

Temperature-corrected calibration of GS3 dielectric sensors.

Water is a key factor for life and for sustaining food, feed, and biomass for energy production in today’s bio-based economies but, in the coming decades, projected changes in the water cycle will be the main drivers in shaping our environment and its ecosystems. Whereas the water cycle will be strongly affected by climate change, the extent and impact on ecosystems’ functioning and services are only roughly known. Increasing hydroclimatic extreme events, such as floods and droughts, may lead to severe ecological, economic, and societal impacts. There is currently a need to establish a network of hydrological observatories in Europe that allows testing of hydrologic hypotheses. At present, we lack concerted and dedicated action in the field of hydrology neither in Europe nor worldwide for making hydrological data accessible to the research community and in designing cross-catchment experiments (Vereecken et al., 2015; Blöschl, 2017; Bogena et al., 2018; Vereecken et al., 2022). The 8th Galileo Conference is therefore centered on the following scientific sessions and relevant content: 1) Innovative geophysical sensing methods in hydrological and critical zone research 2) Environmental monitoring and modeling with the support of UAS and satellites 3) Data assimilation, artificial intelligence, and hydrological observations 4) Using O-H stable isotopes for studying hydrological process understanding and the history of flowing waters 5) Quantifying regional hydrological change impacts 6) Big data science in hydrological researc

Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks

Evaluating pedotransfer functions for predicting soil bulk density using hierarchical mapping information in Campania, Italy.

In this study, the performance of 63 existing pedotransfer functions (PTFs) is evaluated to estimate oven-dry soil bulk density (BD) by using a dataset of 3,316 soil cores taken mainly in the farmlands of Campania (southern Italy). As expected, the lack of direct calibration yields prediction accuracy from unsatisfactory to rather weak. Therefore, we advance the working hypothesis that the use of hierarchical soil mapping information can make the application of existing PTFs more reliable. We show that grouping data according to land-systems classes or soil groups considerably improves the prediction ability quantified through the root mean squared error (RMSE) and the coefficient of determination (R2). An independent data set of 105 soil cores taken from two hillslopes in the Upper Alento River Catchment in southern Campania was used to verify our assumption. The validation step shows that the knowledge of a soil-landscape map is an efficient tool for improving the prediction of BD. This approach will be employed in a subsequent study to develop site-specific PTFs for the study region

Combined application of photo-selective mulching films and beneficial microbes affects crop yield and irrigation water productivity in intensive farming systems

Cultivation of vegetables under plastic tunnels is a steadily growing farming system, nevertheless there are concerns about its environmental sustainability. This work tests a new cultivation system based on the application of photo-selective mulching films to the soil combined with beneficial microbes to improve crop yield, save irrigation water and enhance crop irrigation water productivity. A two-year project was carried out in three farms of southern Italy that practice cultivation in greenhouses with different soil characteristics. Photo-selective mulching films (PS) were used alone or in combination with microbial consortia (M) containing beneficial microbes (i.e. antagonistic fungi of the genus Trichoderma, mycorrhizal fungi of the genus Glomus and the plant growth promoting bacterium Bacillus subtilis) and compared with black plastic mulching (B). Soil temperature, soil water content, and irrigation water volumes were continuously monitored for eight cropping cycles including tomato, sweet pepper, lettuce, melon, and kohlrabi. Crop yields were assessed at the end of each cycle. PS films in combination with M significantly increased crop yields with respect to control, with the most positive effects on winter crops. Soil temperature under PS was consistently lower than that under B mulch. All mulching films allowed the saving of irrigation water compared with untreated control, but no difference was detected between PS and B. However, PS increases crop irrigation water productivity (CIWP) compared with B film in 25% of the experimental cases. In conclusion, our results indicate that combining PS films with beneficial microbes in cultivation under plastic tunnel greenhouses promotes crop yield and increases CIWP compared with control in 87.5% and 75% of the study cases, respectivel