Emilia‐Romagna is a region of Northern Italy whose land use is mainly devoted to agriculture, especially over the plain area. Since the region is characterized by summer water shortages and recurrent drought conditions, a summer irrigation water demand forecast system has been developed, operating both at regional and Land Reclamation and Irrigation Board levels. Remote sensing crop detection is used in combination with a soil water balance model as a monitoring tool in support to water management and to optimize the use of available water resources. These instruments are also integrated with operational calibrated probabilistic seasonal forecast products currently based on Copernicus Climate Services, in order to evaluate summer irrigation water needs in advance in support to decision‐making by water managers. Results obtained using this prediction system are characterized by a significant forecast skill. The sources of the predictability of this system are specifically evaluated, showing that the correct representation of crop geographical distribution is crucial for the ability of the system to capture spatial variability in irrigation demand. Furthermore, the system forecast skill is mostly due to the use of an empirical model to assess the hypodermic water table depth initialized by means of the observed meteorological data of the months before the beginning of the summer season. Finally, calibrated probabilistic seasonal predictions also contribute to a minor extent to the good performance of the system as a whole.

Monitoring soil water content (SWC) is vital for various applications, particularly in agriculture. This study compares SWC estimated by means of SCATSAR-SWI remote sensing (RS) at different depths (T-values) with Cosmic Ray Neutron Sensing (CRNS) across four agricultural sites in northern Italy. Additionally, it examines the spatial mismatch and representativeness of SWC products’ footprints based on different factors within the following areas: the Normalized Difference Vegetation Index (NDVI), soil properties (sand, silt, clay, Soil Organic Carbon (SOC)), and irrigation information. The results reveal that RS-derived SWC, particularly at T = 2 depth, exhibits moderate positive linear correlation (mean Pearson correlation coefficient, R = 0.6) and a mean unbiased Root–Mean–Square Difference (ubRMSD) of 14.90%SR. However, lower agreement is observed during summer and autumn, attributed to factors such as high biomass growth. Sites with less variation in vegetation and soil properties within RS pixels rank better in comparing SWC products. Although a weak correlation (mean R = 0.35) exists between median NDVI differences of footprints and disparities in SWC product performance metrics, the influence of vegetation greenness on the results is clearly identified. Additionally, RS pixels with a lower percentage of sand and SOC and silt loam soil type correlate to decreased agreement between SWC products. Finally, localized irrigation practices also partially explain some differences in the SWC products. Overall, the results highlight how RS pixel variability of the different factors can explain differences between SWC products and how this information should be considered when selecting optimal ground-based measurement locations for remote sensing comparison.

Satellite remote sensing is currently an established, effective, and constantly used tool and methodology for monitoring agriculture and fertilisation. At the same time, in recent years, the need for the detection of livestock manure and digestate spreading on the soil is emerging, and the development of spectral indices and classification processes based on satellite multispectral data acquisitions is growing. However, the application of such indicators is still underutilised and, given the polluting impact of livestock manure and digestate on soil, groundwater, and air, an in-depth study is needed to improve the monitoring of this practice. Additionally, this paper aims at exposing a new spectral index capable of detecting the land affected by livestock manure and digestate spreading. This indicator was created by studying the spectral response of bare soil and livestock manure and digestate, using Copernicus Sentinel-2 MSI satellite acquisitions and ancillary datasets (e.g., soil moisture, precipitation, regional thematic maps). In particular, time series of multispectral satellite acquisitions and ancillary data were analysed, covering a survey period of 13 months between February 2022 and February 2023. As no previous indications on fertilisation practices are available, the proposed approach consists of investigating a broad-spectrum area, without investigations of specific test sites. A large area of approximately 236,344 hectares covering three provinces of the Emilia-Romagna Region (Italy) was therefore examined. A series of ground truth points were also collected for assessing accuracy by filling in the confusion matrix. Based on the definition of the spectral index, a value of the latter greater than three provides the most conservative threshold for detecting livestock manure and digestate spreading with an accuracy of 62.53%. Such results are robust to variations in the spectral response of the soil. On the basis of these very encouraging results, it is considered plausible that the proposed index could improve the techniques for detecting the spreading of livestock manure and digestate on bare ground, classifying the areas themselves with a notable saving of energy compared to the current investigation methodologies directly on the ground.

Livestock manure management, especially related to soil-fertilisation practice, is responsible for most of the emissions in agriculture, and in particular the ammonia emissions (NH₃), which play a key role in environmental problems, affecting water, soil and air quality. Within the digital agricultural framework, EO data contribute to agricultural-practices monitoring such as manure spreading, to mitigate pollutant emissions. This study presents a GIS-based tool on an open-source platform, developed for susceptibility estimation of sewage spreading occurrence in agricultural areas of Italy. The tool is based on the analysis of multispectral and hyperspectral satellite time series in synergy with field data and ancillary information collected from regional repositories, to produce a series of classified and prioritised spatially explicit information. Spectral analysis of satellite acquisitions enabled the identification of manure spreading with the precision of about 83%. Field campaigns from October to March were carried out to validate the spreading event. The case study demonstrates the ability of the proposed GIS-based tool in supporting the monitoring of manure spreading.