Soil moisture is classified as an essential climate variable (ECV) and is relevant to understanding hydrological, agricultural and ecological processes. Yet, in spite of its importance, direct observations of soil moisture remain limited globally-those that exist are typically limited in duration and spatial extent. Consequently, alternative approaches for estimating soil moisture have been developed, including water balance ('bucket') models, the use of remotely sensed information and the application of land surface modelling techniques. Spaceborne and land surface modelling based methods offer significant potential for monitoring and modelling soil moisture at a variety of spatial scales; however, their resolution remains relatively coarse for global and continental scale applications. At country scale, land surface models have demonstrated their potential but they require access to computational resources to deliver high resolution products. With the advent of machine- and deep- learning and data fusion techniques, high resolution global and regional soil moisture datasets are increasingly becoming available. Here, we evaluated a statistical machine learning approach to downscale the European Space Agency's (ESA) Climate Change Initiative (CCI) combined passive and active soil moisture product for Ireland using covariates that included both static (e.g., topography) and dynamic (e.g., gridded rainfall and temperature) variables. The model was developed using in situ cosmic ray neutron sensor (CRNS) measurements obtained from a network of sites in the United Kingdom, justified on the basis that the United Kingdom is geographically similar to Ireland in terms of its climate, soil types and land cover management practices. The model was found to perform reasonably well when validated against limited in situ data obtained from available time domain reflectometry (TDR) measurements available from Ireland. The developed model was subsequently used to derive spatial estimates of soil moisture on a 1 km grid across the Republic of Ireland.

Since the launch of satellites into space in the 1970s, the usage of their data has expanded across various applications, from communication (tele-communication and broadcasting) and navigation (e.g., the Global Positioning SystemGPS) to precision agriculture and environmental monitoring. Advances in sensors, instruments, and platforms-including drones-have equipped researchers with new measurements of the physical properties of elements on Earth (e.g., vegetation, soil, and water) leading to improved environmental assessment and monitoring across scales. Earth Observation includes remote sensing technologies, which play a vital role in monitoring vegetation-providing information on vegetation extent, deforestation, phenological stages, crop growth, above-ground biomass, and changes in land use/land cover over time (temporal series) and across different spatial scales (larger or smaller areas). Satellite remote sensing technologies can be further classified into optical/non-optical as well as active and passive systems. Optical, passive remote sensing of vegetation relies on the interaction between plant pigments/cell structure and incident radiation in the electromagnetic spectrum, particularly in the red and near-infrared spectrum. In green vegetation, remote sensing techniques leverage the process whereby chlorophyll absorbs incident energy while cell structure strongly scatters near-infrared radiation to prevent cell damage. Non-optical, active, microwave remote sensing systems transmit radiation from the satellite onto the ground target, in this case biomass such as crops, which will then reflect the radiation as backscatter to the satellite. This reflected backscatter signal is affected by the physical properties, water content and electric potential energy stored in plants (including grass) and tress. Considering that biomass can serve as a proxy for biodiversity, this chapter presents examples of estimating above-ground biomass using remote sensing data and techniques in two contrasting landscapes: forested and managed landscapes.

Geostatistical and non-geostatistical methods have been applied in soil science to different types of data, from field collections to proximal- and remotely sensed. However, studies addressing the use of microwave sensors for soil texture estimates are typically constrained to bare soil conditions. Here, we test radar remote sensing data and techniques (backscattering coefficient, radar vegetation index, and pol-decomposition) using four models as non-spatial (linear regression and tree-based regression) and spatial approaches (Empirical Bayesian Kriging Regression-EBKR and Cokriging-CK) to estimate soil particle size fractions and soil texture classes on topsoil layer (10 cm depth). The study area is Ireland spanning from western-eastern coast. The models’ performance was assessed with the leave-one-out cross-validation method. Results showed that CK yielded good predictions for sand, silt, and clay with Nash Sutcliffe Efficiency values of 0.9, 0.6, and 0.5, respectively. However, EBKR was a better approach to capture trends and spatial patterns in the interpolated surface. Importantly, this work provides a methodological framework to inform the design of intensive in situ soil surveys (areas of large uncertainty estimates in the models are those areas that may require detailed soil surveys). It also provides a means to estimate soil properties over large spatial areas and generate new data products for use in model-based approaches that currently employ coarse global scale soil texture products (soil security, climate, hydrological, land surface). The methods outlined here are also relevant to locations where optical remotely sensed data is limited and/or where vegetation cover is present throughout the year.

Soil properties and their associated hydrophysical parameters represent a significant source of uncertainty in land surface models (LSMs), with consequent effects on simulated sub-surface thermal and moisture characteristics, surface energy exchanges, and turbulent fluxes. These effects can result in large model differences, particularly during extreme events. As is typical of many model-based approaches, spatial soil information such as the location, extent, and depth of soil textural classes is derived from coarse-scale soil information and employed largely due to its being readily availability rather than its suitability. However, the use of a particular spatial soil dataset can have important consequences for many of the processes simulated within an LSM. This study investigates model uncertainty in the Noah-MP model in simulating soil moisture (expressed as a ratio of water to soil volume, m3 m−3) and soil temperature changes, associated with two widely used global soil databases (STATSGO and SoilGrids). Both soil datasets produced significant dry biases in loam soils of 0.15 and 0.10 m3 m−3 during a wet and dry period, respectively. The spatial disparities between STATSGO and SoilGrids also influenced the simulated regional soil hydrothermal changes and extremes. SoilGrids was found to intensify drought characteristics – shifting low and moderate drought areas into the extreme and exceptional classifications – relative to STATSGO. Our results demonstrate that the coarse STATSGO performs as well as the fine-scale SoilGrids soil database, though the latter represents the soil moisture dynamics better. However, the results underscore the need for greater collaborative efforts to develop more detailed regionally derived soil texture characteristics and to improve pedotransfer function (PTF) parameterizations for better representations of soil properties in LSMs. Enhancing these soil property representations in LSMs is essential for improving operational modeling and forecasting of hydrological processes and extremes.

Interconnected societal challenges such as climate change, biodiversity loss and food security demand immediate and coordinated action across local to global scales, guided by coherent policies and management mechanisms. Reflecting on the critical need to address societal challenges, Nature-based Solutions in marine and coastal environments, known as blue NBS, have emerged as an important part of the response strategy. Blue NBS integrate actions to protect and restore marine and coastal ecosystems while managing human impacts, embedding nature and people into decision-making through multifaceted approaches. However, blue NBS implementation trails terrestrial NBS. To effectively inform blue NBS implementation, research must produce actionable science that is relevant, timely and usable, requiring collaboration and active knowledge exchange across the science-policy-practitioner interface. Working with stakeholders, we developed the MaCoBioS Blue NBS Toolbox to begin addressing some of the barriers facing blue NBS implementation. Containing a collection of multi-disciplinary, scientifically-grounded and stakeholder-informed tools and products, the toolbox guides practitioners through different stages of blue NBS implementation. This toolbox provides an important initial set of resources to support the design and implementation of effective blue NBS and pave the way for further collaborative work to operationalise these tools in different social-ecological contexts.

Despite their ecological, economic, and social importance, mangrove ecosystems suffer from high levels of degradation caused by a combination of anthropogenic stressors and the effects of climate change. Their degradation inevitably reduces the provision of ecosystem services and ultimately impacts human well-being, especially in coastal communities of Small Islands Developing States (SIDS). To timely identify and manage stressors causing local mangrove degradation, in situ monitoring is required. However, the financial means and human capacity to do so are often limited in SIDS, hampering adequate management of their mangrove forests. In search of a cost-effective alternative, we evaluated the use of Sentinel-2 satellites to monitor mangrove extent and species distribution in Lac Bay, a bay located on the small tropical island of Bonaire (Caribbean Netherlands). We also evaluated the mangrove’s ecological condition through two biophysical variables 1) Effective Leaf Area Index (LAIe) and 2) Net Primary Productivity (NPP). Our results showed that Sentinel-2 data are a valuable tool for mapping the extent of mangrove forests in Bonaire and species composition (mean overall accuracy > 95 %). Using five Sentinel-2 images from 2021 and 2022, the extent of mangrove forests in Lac Bay was estimated to be on average 222.3 ha, of which 136.0 ha were classified as Rhizophora mangle (red mangrove) and 77.1 ha as Avicennia germinans (black mangrove). Mean values for predicted LAIe ranged from 3.37 to 3.85 for Lac Bay, with significantly higher values in the wet season (3.82 ± 0.57) compared to the dry season (3.40 ± 0.56). The generic Simplified Level-2 Prototype Processor (SL2P) underestimated the LAIe values in Lac Bay, with moderate differences between SL2P values and in situ data (BDE = 0.41, RMSE = 1.09). Mean NPP values were estimated to be 8.82 ± 1.46 (g Cm−2 d -1). LAIe and NPP maps showed a zonal distribution, with highest values in the mid-West and East on the seaward side, and lowest values in the northern landward part of Lac Bay. The method developed in this study provides a cost-effective way to monitor the extent, composition, and ecological condition of mangrove forests, which can be used by small island states to make informed decisions about the management and protection of mangrove ecosystems.

Data extracted from Synthetic Aperture Radar (SAR) have been widely employed to estimate soil properties. However, these studies are typically constrained to bare soil conditions, as soil information retrieval in vegetated areas remains challenging. Polarimetric decomposition has emerged as a potentially useful method to separate the scattering contributions of different targets (e.g. canopy/leaves and the underlying soil), which is of significance for areas that are near-permanently covered in low-lying vegetation (e.g. grass) like Ireland – the study area for this investigation. Here, we test the surface scattering mechanism, derived from H-alpha dual-pol decomposition, together with other covariates, to estimate percentages of sand, silt, and clay, over vegetated terrain, using Sentinel 1 data (dual-pol C-band SAR). The statistical modelling approaches evaluated – linear regression (LRM) and tree-based regression models (machine learning) – explicitly consider the compositional nature of soil texture. When compared to the models fitted without surface scattering data, results showed that the inclusion of the surface scattering data improved estimates of silt and clay, with the compositional linear regression model, and estimates of sand and silt fractions with different tree-based models. While not without limitations, our study demonstrated that the polarimetric decomposition method, which is typically used for classification and segmentation purposes, could also be used for soil property estimation, broadening the application of this technique in microwave remote sensing studies.

The monitoring of coastal waters using satellite data, from sensors including Sentinel-3 OLCI, has become a vital tool in the management of these water environments; especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI case 2 (Neural Network) products from Sentinel-3’s processing baseline: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. Results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3h validation window. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data for the region.

The monitoring of coastal waters using satellite data, from sensors such as Sentinel-3 OLCI, has become a vital tool in the management of these water environments, especially when it comes to improving our understanding of the effects of climate change on these regions. In this study, the latest Level-2 water products derived from different OLCI Sentinel-3 processors were validated against a comprehensive in situ dataset from the NW Baltic Sea proper region through a matchup analysis. The products validated were those of the regionally adapted Case-2 Regional Coast Colour (C2RCC) OLCI processor (v1.0 and v2.1), as well as the latest standard Level-2 OLCI Case-2 (neural network) products from Sentinel-3’s processing baseline, listed as follows: Baseline Collection 003 (BC003), including “CHL_NN”, “TSM_NN”, and “ADG443_NN”. These products have not yet been validated to such an extent in the region. Furthermore, the effect of the current EUMETSAT system vicarious calibration (SVC) on the Level-2 water products was also validated. The results showed that the system vicarious calibration (SVC) reduces the reliability of the Level-2 OLCI products. For example, the application of these SVC gains to the OLCI data for the regionally adapted v2.1 C2RCC products resulted in RMSD increases of 36% for “conc_tsm”; 118% for “conc_chl”; 33% for “iop_agelb”; 50% for “iop_adg”; and 10% for “kd_z90max” using a ±3 h validation window. This is the first time the effects of these SVC gains on the Level-2 OLCI water products has been isolated and quantified in the study region. The findings indicate that the current EUMETSAT SVC gains should be applied and interpreted with caution in the region of study at present. A key outcome of the paper recommends the development of a regionally specific SVC against AERONET-OC data in order to improve the Level-2 water product retrieval in the region. The results of this study are important for end users and the water authorities making use of the satellite water products in the Baltic Sea region.

Electric mobility is critical to reducing emissions from transport and dependency on Internal Combustion Engine vehicles. This study attempts to model the suitability of the built environment for electric vehicle (EV) adoption in urban areas based on sociodemographics and access to driveways for installing charging infrastructure. A novel approach using geospatial techniques is adopted to detect driveways from multispectral remote sensing information. A region in Dublin, Ireland, has been chosen as the study area. The region is further categorised based on the feasibility of EV adoption using hierarchical cluster analysis. Initial results highlight the disparity in access to low-emission modes to those not dependent on cars. Results from zero-inflated count models at the neighbourhood level reiterate the impact of driveways and sociodemographic factors on EV adoption. The proposed methodology can help evaluate infrastructure availability for widespread EV transition and inform strategic planning. The driveway detection framework may be adapted to other regions while accounting for geographic characteristics.

Biodiversity loss and climate change have severely impacted ecosystems and livelihoods worldwide, compromising access to food and water, increasing disaster risk, and affecting human health globally. Nature-based Solutions (NbS) have gained interest in addressing these global societal challenges. Although much effort has been directed to NbS in urban and terrestrial environments, the implementation of NbS in marine and coastal environments (blue NbS) lags. The lack of a framework to guide decision-makers and practitioners through the initial planning stages appears to be one of the main obstacles to the slow implementation of blue NbS. To address this, we propose an integrated conceptual framework, built from expert knowledge, to inform the selection of the most appropriate blue NbS based on desired intervention objectives and social-ecological context. Our conceptual framework follows a four incremental steps structure: Step 1 aims to identify the societal challenge(s) to address; Step 2 highlights ecosystem services and the underlying biodiversity and ecological functions that could contribute to confronting the societal challenge(s); Step 3 identify the specific environmental context the intervention needs to be set within (e.g. the spatial scale the intervention will operate within, the ecosystem's vulnerability to stressors, and its ecological condition); and Step 4 provides a selection of potential blue NbS interventions that would help address the targeted societal challenge(s) considering the context defined through Step 3. Designed to maintain, enhance, recover, rehabilitate, or create ecosystem services by supporting biodiversity, the blue NbS intervention portfolio includes marine protection (i.e., fully, highly, lightly, and minimally protected areas), restorative activities (i.e., active, passive, and partial restoration; rehabilitation of ecological function and ecosystem creation), and other management measures (i.e., implementation and enforcement of regulation). Ultimately, our conceptual framework guides decision-makers toward a versatile portfolio of interventions that cater to the specific needs of each ecosystem rather than imposing a rigid, one-size-fits-all model. In the future, this framework needs to integrate socio-economic considerations more comprehensively and be kept up-to-date by including the latest scientific information.

Biodiversity is a key indicator of ecosystem health and plays an important role in providing ecosystem services that are essential for economic development and social well-being. In Ireland, climate change and anthropogenic pressures have led to biodiversity loss and habitat degradation, which in turn affects the provision of ecosystem services. The aim of this study was to determine how marine coastal biodiversity and ecosystem services in the Republic of Ireland are portrayed in the scientific literature, news media, and current legislation in order to identify knowledge gaps and priority areas of intervention for policymakers and other decision-making stakeholders. A review of the scientific literature based on the Scopus database suggests a lack of stakeholder involvement in the scientific-based approach. Up to 2022 research on marine coastal biodiversity focused primarily on species richness and diversity, especially in the context of climate change, while research on ecosystem services focused primarily on ecosystem function. Analysis of the news media found that coverage of biodiversity and ecosystem services has increased over the past decade, but opportunities are being missed to raise public awareness of the multiple benefits, not just economic, of marine coastal ecosystems, their biodiversity, and the services they provide. The terms “biodiversity” and “ecosystem services” have only been introduced into Irish legislation in recent years. The legislative context at the national level is rather fragmented at diverse levels and mainly corresponds to the transposition of European Directives while national specific legislation is less developed. The interconnection between these three domains: scientific, news media and legislative was not evident in this study, especially in the case of the scientific field. Efforts to promote science-based knowledge, communication, collaboration, and transparency between these domains are crucial to support informed decision-making and promote public engagement while conserving biodiversity. Addressing the information gaps identified in this work could help advance the implementation of Nature-based Solutions (NbS), key cost-effective measures to mitigate and adapt to the impacts of climate change while protecting biodiversity and ecosystem services.

Coastal wetlands are highly efficient ‘blue carbon’ sinks which contribute to mitigating climate change through the long-term removal of atmospheric CO2 and capture of carbon (C). Microorganisms are integral to C sequestration in blue carbon sediments and face a myriad of natural and anthropogenic pressures yet their adaptive responses are poorly understood. One such response in bacteria is the alteration of biomass lipids, specifically through the accumulation of polyhydroxyalkanoates (PHAs) and alteration of membrane phospholipid fatty acids (PLFA). PHAs are highly reduced bacterial storage polymers that increase bacterial fitness in changing environments. In this study, we investigated the distribution of microbial PHA, PLFA profiles, community structure and response to changes in sediment geochemistry along an elevation gradient from intertidal to vegetated supratidal sediments. We found highest PHA accumulation, monomer diversity and expression of lipid stress indices in elevated and vegetated sediments where C, nitrogen (N), PAH and heavy metals increased, and pH was significantly lower. This was accompanied by a reduction in bacterial diversity and a shift to higher abundances of microbial community members favouring complex C degradation. Results presented here describe a connection between bacterial PHA accumulation, membrane lipid adaptation, microbial community composition and polluted C rich sediments. Geochemical, microbiological and polyhydroxyalkanoate (PHA) gradient in a blue carbon zone.

Data derived from synthetic aperture radar (SAR) are widely employed to predict soil properties, particularly soil moisture and soil carbon content. However, few studies address the use of microwave sensors for soil texture retrieval and those that do are typically constrained to bare soil conditions. Here, we test two statistical modelling approaches—linear (with and without interaction terms) and tree‐based models, namely compositional linear regression model (LRM) and random forest (RF)—and both nongeophysical (e.g., surface soil moisture, topographic, etc) and geophysical‐based (electromagnetic, magnetic and radiometric) covariates to estimate soil texture (sand %, silt % and clay %), using microwave remote sensing data (ESA Sentinel‐1). The statistical models evaluated explicitly consider the compositional nature of soil texture and were evaluated with leave‐one‐out cross‐validation (LOOCV). Our findings indicate that both modelling approaches yielded better estimates when fitted without the geophysical covariates. Based on the Nash–Sutcliffe efficiency coefficient (NSE), LRM slightly outperformed RF, with NSE values for sand, silt and clay of 0.94, 0.62 and 0.46, respectively; for RF, the NSE values were 0.93, 0.59 and 0.44. When interaction terms were included, RF was found to outperform LRM. The inclusion of interactions in the LRM resulted in a decrease in NSE value and an increase in the size of the residuals. Findings also indicate that the use of radar‐derived variables (e.g., VV, VH, RVI) alone was not able to predict soil particle size without the aid of other covariates. Our findings highlight the importance of explicitly considering the compositional nature of soil texture information in statistical analysis and regression modelling. As part of the continued assessment of microwave remote sensing data (e.g., ESA Sentinel‐1) for predicting topsoil particle size, we intend to test surface scattering information derived from the dual‐polarimetric decomposition technique and integrate that predictor into the models in order to deal with the effects of vegetation cover on topsoil backscattering.

The long-term provision of ocean ecosystem services depends on healthy ecosystems and effective sustainable management. Understanding public opinion about marine and coastal ecosystems is important to guide decision-making and inform specific actions. However, available data on public perceptions on the interlinked effects of climate change, human impacts and the value and management of marine and coastal ecosystems are rare. This dataset presents raw data from an online, self-administered, public awareness survey conducted between November 2021 and February 2022 which yielded 709 responses from 42 countries. The survey was released in four languages (English, French, Spanish and Italian) and consisted of four main parts: (1) perceptions about climate change; (2) perceptions about the value of, and threats to, coasts, oceans and their wildlife, (3) perceptions about climate change response; and (4) socio-demographic information. Participation in the survey was voluntary and all respondents provided informed consent after reading a participant information form at the beginning of the survey. Responses were anonymous unless respondents chose to provide contact information. All identifying information has been removed from the dataset. The dataset can be used to conduct quantitative analyses, especially in the area of public perceptions of the interlinkages between climate change, human impacts and options for sustainable management in the context of marine and coastal ecosystems. The dataset is provided with this article, including a copy of the survey and participant information forms in all four languages, data and the corresponding codebook.

Global research is showing that coastal blue carbon ecosystems are vulnerable to climate change driven threats including accelerated sea-level rise and prolonged periods of drought. Furthermore, direct anthropogenic impacts present immediate threats through deterioration of coastal water quality, land reclamation, long-term impact to sediment biogeochemical cycling. These threats will invariably alter the future efficacy of carbon (C) sequestration processes and it is imperative that currently existing blue carbon habitats be protected. Knowledge of underlying biogeochemical, physical and hydrological interactions occurring in functioning blue carbon habitats is essential for developing strategies to mitigate threats, and promote conditions to optimise C sequestration/storage. In this current work, we investigated how sediment geochemistry (0–10 cm depth) responds to elevation, an edaphic factor driven by long-term hydrological regimes consequently exerting control over particle sedimentation rates and vegetation succession. This study was performed in an anthropogenically impacted blue carbon habitat along a coastal ecotone encompassing an elevation gradient transect from intertidal sediments (un-vegetated and covered daily by tidal water), through vegetated salt marsh sediments (periodically covered by spring tides and flooding events), on Bull Island, Dublin Bay. We determined the quantity and distributions of bulk geochemical characteristics in sediments through the elevation gradient, including total organic carbon (TOC), total nitrogen (TN), total metals, silt, clay, and also, 16 individual polyaromatic hydrocarbon’s (PAH’s) as an indication of anthropogenic input. Elevation measurements for sample sites were determined on this gradient using a LiDAR scanner accompanied by an IGI inertial measurement unit (IMU) on board a light aircraft. Considering the gradient from the Tidal mud zone (T), through the low-mid marsh (M) to the most elevated upper marsh (H), there were significant differences between all zones for many measured environmental variables. The results of significance testing using Kruskal–Wallis analysis revealed, that

Globally, croplands represent a significant contributor to climate change, through both greenhouse gas emissions and land use changes associated with cropland expansion. They also represent locations with significant potential to contribute to mitigating climate change through alternative land use management practices that lead to increased soil carbon sequestration. In spite of their global importance, there is a relative paucity of tools available to support field- or farm-level crop land decision making that could inform more effective climate mitigation practices. In recognition of this shortcoming, the Simple Algorithm for Yield Estimate (SAFY) model was developed to estimate crop growth, biomass, and yield at a range of scales from field to region. While the original SAFY model was developed and evaluated for winter wheat in Morocco, a key advantage to utilizing SAFY is that it presents a modular architecture which can be readily adapted. This has led to numerous modifications and alterations of specific modules which enable the model to be refined for new crops and locations. Here, we adapted the SAFY model for use with spring barley, winter wheat and winter oilseed rape at selected sites in Ireland. These crops were chosen as they represent the dominant crop types grown in Ireland. We modified the soil–water balance and carbon modules in SAFY to simulate components of water and carbon budgets in addition to crop growth and production. Results from the modified model were evaluated against available in situ data collected from previous studies. Spring barley biomass was estimated with high accuracy (R2 = 0.97, RMSE = 95.8 g·m−2, RRMSE = 11.7%) in comparison to GAI (R2 = 0.73, RMSE = 0.44 m2·m−2, RRMSE = 10.6%), across the three years for which the in situ data was available (2011–2013). The winter wheat module was evaluated against measured biomass and yield data obtained for the period 2013–2015 and from three sites located across Ireland. While the model was found to be capable of simulating winter wheat biomass (R2 = 0.71, RMSE = 1.81 t·ha−1, RRMSE = 8.0%), the model was found to be less capable of reproducing the associated yields (R2 = 0.09, RMSE = 2.3 t·ha−1, RRMSE = 18.6%). In spite of the low R2 obtained for yield, the simulated crop growth stage 61 (GS61) closely matched those observed in field data. Finally, winter oilseed rape (WOSR) was evaluated against a single growing season for which in situ data was available. WOSR biomass was also simulated with high accuracy (R2 = 0.99 and RMSE = 0.52 t·ha−1) in comparison to GAI (R2 = 0.3 and RMSE = 0.98 m2·m−2). In terms of the carbon fluxes, the model was found to be capable of estimating heterotrophic respiration (R2 = 0.52 and RMSE = 0.28 g·C·m−2·day−1), but less so the ecosystem respiration (R2 = 0.18 and RMSE = 1.01 g·C·m−2·day−1). Overall, the results indicate that the modified model can simulate GAI and biomass, for the chosen crops for which data were available, and yield, for winter wheat. However, the simulations of the carbon budgets and water budgets need to be further evaluated—a key limitation here was the lack of available in situ data. Another challenge is how to address the issue of parameter specification; in spite of the fact that the model has only six variable crop-related parameters, these need to be calibrated prior to application (e.g., date of emergence, effective light use efficiency etc.). While existing published values can be readily employed in the model, the availability of regionally derived values would likely lead to model improvements. This limitation could be overcome through the integration of available remote sensing data using a data assimilation procedure within the model to update the initial parameter values and adjust model estimates during the simulation.

As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the upper ocean and, consequently, the effect of their changes on species populations. In this study, nineteen years (1998–2016) of monthly data of essential climate variables (ECVs), chlorophyll (Chl-a), and the diffuse attenuation coefficient (K490) were used, together with previous analyses of sea surface temperature (SST), to investigate the temporal and spatial variability of surface waters around Ireland. The study area was restricted to specific geographically delineated divisions, as defined by the International Council of the Exploration of the Seas (ICES). The results showed that SST and Chl-a were positively and significantly correlated in ICES divisions corresponding to oceanic waters, while in coastal divisions, SST and Chl-a showed a significant negative correlation. Chl-a and K490 were positively correlated in all cases, suggesting an important role of phytoplankton in light attenuation. Chl-a and K490 had significant trends in most of the divisions, reaching maximum values of 1.45% and 0.08% per year, respectively. The strongest seasonal Chl-a trends were observed in divisions VIId and VIIe (the English Channel), primarily in the summer months, followed by northern divisions VIa (west of Scotland) and VIb (Rockall) in the winter months.

Real-time soil moisture measurements are essential to manage for adaptive dynamic management of climate change adaptation and reduction of nutrient losses and greenhouse gas emissions from agriculture and forestry. Soil moisture status influences crop growth, run-off, groundwater recharge, land surface-atmospheric exchange dynamics and greenhouse gas emissions as well as the risk of forest fire danger. Here we present the new Irish Soil Moisture Observation Network (ISMON) as an umbrella to bring together several recently established long-term environmental observational networks. These are: 1) initiative of the AGMET group (agmet.ie), 2) COSMOS UK – Northern Ireland, 3) Teagasc NASCO (National Agricultural Soil Carbon Observatory) and 4) Terrain-AI, all of which include several different methodologies for measuring soil moisture at field scale. For instance, AGMET is installing novel cosmic ray neutron sensors which can provide field averaged soil moisture estimates (400m diameter) and Teagasc NASCO and Terrain AI are using Time Domain Reflectometry probes. Such networks are seen as necessary to resolving the problem of scale between point, field-based measurements and satellite-derived soil moisture products and are important in monitoring key biogeochemical processes that vary rapidly in time and space. In the initial phase of the implementation of the ISMON network, the current distribution of the stations in relation to the other networks are presented. The ISMON aims to represent the most relevant soil types, land cover, and regional climate regimes to corroborate direct measurements of soil moisture; it will adapt its design to improve the monitoring network as required. The ISMON will make a valuable contribution to, and expand the international soil moisture monitoring network.