1,721,122 research outputs found
Irrigation monitoring from satellite at hyper-high resolution: Paving the way for remote-sensing-based agricultural water management support services
No full textRecent advances in satellite retrievals of key hydrological variables have fostered the development of approaches for tracking the irrigation footprint on water resources. Nevertheless, constraints due to the native spatial and temporal resolutions of remotely sensed data still limit the building of supporting systems for agricultural water management relying on Earth Observation. This work aims at filling this gap by applying well-established irrigation mapping and quantification techniques with multiresolution satellite data as input to reproduce irrigation dynamics at the unprecedented spatial sampling of 10 m. Results are validated across different scales of interest for water allocation managers, i.e., from the consortium to the single farm level. The irrigation quantification experiment, carried out through the SM-based (Soil-Moisture-based) inversion approach, provides satisfactory results especially in light of the scarcity of ancillary information for refining the estimates. Percentage errors aggregated at the consortium and the farm scales equal to −24 % and to −14 %, respectively, are found. Such results are achieved without considering losses due to irrigation efficiency, as this information is not explicitly available. The irrigation mapping experiment, carried out by leveraging the TSIMAP (Temporal Stability derived Irrigation MAPping) method, is validated at the farm scale only. An overall accuracy of 93 % is reached, corresponding to two agricultural fields misreproduced as non-irrigated out of the total number equal to twenty-eight. The outcomes of this study show the potential of hyper-high resolution implementations of the considered irrigation mapping and quantification techniques for supporting agricultural water managers, highlighting improvements needed to further meet potential users’ requirements
Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products
No full textERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepo Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28-100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28-100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days
Spatial-temporal variability of soil moisture and its estimation across scales
No full textThe soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil‐atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall‐runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km2. Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m2. The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field‐mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall‐runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most “representative” field, previously determined through the temporal stability analysis, can reproduce the catchment‐mean soil moisture with a determination coefficient, R2, higher than 0.96 and a root‐mean‐square error, RMSE, equal to 2.38%. For the “nonrepresentative”
fields the accuracy in terms of RMSE decreased, but similar R2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know
an index of soil moisture temporal pattern to be incorporated in conceptual rainfall‐runoff models. The obtained results can address the soil moisture monitoring network design from
which a reliable soil moisture temporal pattern at the catchment scale can be derived
Tree species identity and diversity drive fungal richness and community composition along an elevational gradient in a Mediterranean ecosystem
No full textEcological and taxonomic knowledge is important for conservation and utilization of biodiversity. Biodiversity and ecology of fungi in Mediterranean ecosystems is poorly understood. Here, we examined the diversity and spatial dis- tribution of fungi along an elevational gradient in a Mediterranean ecosystem, using DNA metabarcoding. This study provides novel information about diversity of all eco- logical and taxonomic groups of fungi along an elevational gradient in a Mediterranean ecosystem. Our analyses revealed that among all biotic and abiotic variables tested, host species identity is the main driver of the fungal richness and fungal community composition. Fungal richness was strongly asso- ciated with tree richness and peaked in Quercus-dominated habitats and Cistus-dominated habitats. The highest taxonom- ic richness of ectomycorrhizal fungi was observed under Quercus ilex, whereas the highest taxonomic richness of saprotrophs was found under Pinus. Our results suggest that the effect of plant diversity on fungal richness and community composition may override that of abiotic variables across environmental gradients
Data-Mining of a Large Virtual Community: Relationships Between the Users DB and the Web-Log File
No full textCatchment scale soil moisture spatial-temporal variability
No full textThe characterization of the spatial–temporal variability of soil moisture is of paramount importance in many scientific fields and operational applications. However, due to the high variability of soil moisture, its monitoring over large areas and for extended periods through in situ point measurements is not straightforward. Usually, in the scientific literature, soil moisture variability has been investigated over short periods and in large areas or over long periods but in small areas. In this study, an effort to understanding soil moisture variability at catchment scale (>100 km2), which is the size needed for some hydrological applications and for remote sensing validation analysis, is done. Specifically, measurements
were carried out in two adjacent areas located in central Italy with extension of 178 and 242 km2 and over a period of 1 year (35 sampling days) with almost weekly frequency except for the summer period because of soil hardness. For each area, 46 sites were monitored and, for each site, 3 measurements were performed to obtain reliable soil moisture estimates. Soil moisture was measured with a portable Time
Domain Reflectometer for a layer depth of 0–15 cm. A statistical and temporal stability analysis is employed to assess the space–time variability of soil moisture at local and catchment scale. Moreover, by comparing the results with those obtained in previous studies conducted in the same study area, a
synthesis of soil moisture variability for a range of spatial scales, from few square meters to several square kilometers, is attempted. For the investigated area, the two main findings inferred are: (1) the spatial
variability of soil moisture increases with the area up to ~10 km2 and then remains quite constant with an average coefficient of variation equal to ~0.20; (2) regardless of the areal extension, the soil moisture exhibits temporal stability features and, hence, few measurements can be used to infer areal mean values with a good accuracy (determination coefficient higher than 0.88). These insights based on in situ soil moisture observations corroborate the opportunity to use point information for the validation
of coarse resolution satellite images. Moreover, the feasibility to use coarse resolution data for hydrological applications in small to medium sized catchments is confirmed
Utilizzo di una modellistica idrologica accoppiata ad un modello USLE modificato per la previsione della perdita di suolo parcellare in Umbria
No full textModeling the Relationships Between the Users DB and the Web-Log File of a Large Virtual Community
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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