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Imaging spectrometry of non-oceanic aquatic ecosystems has been in development since the late 1980s when the first airborne hyperspectral sensors were deployed over lakes. Most water quality management applications were, however, developed using multispectral mid-spatial resolution satellites or coarse spatial resolution ocean colour satellites till now. This situation is about to change with a suite of upcoming imaging spectrometers being deployed from experimental satellites or from the International Space Station. We review the science of developing applications for inland and coastal aquatic ecosystems that often are a mixture of optically shallow and optically deep waters, with gradients of clear to turbid and oligotrophic to hypertrophic productive waters and with varying bottom visibility with and without macrophytes, macro-algae, benthic micro-algae or corals. As the spaceborne, airborne and in situ optical sensors become increasingly available and appropriate for aquatic ecosystem detection, monitoring and assessment, the science-based applications will need to be further developed to an operational level. The Earth Observation-derived information products will range from more accurate estimates of turbidity and transparency measures, chlorophyll, suspended matter and coloured dissolved organic matter concentration, to more sophisticated products such as particle size distributions, phytoplankton functional types or distinguishing sources of suspended and coloured dissolved matter, estimating water depth and mapping types of heterogeneous substrates. We provide an overview of past science, current state of the art and future directions so that early career scientists as well as aquatic ecosystem managers and associated industry groups may be prepared for the imminent deluge of imaging spectrometry data.

Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial latent heat flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management.

Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated.

In this second part of a two-part article, we present extensive studies on spatial and temporal resolution in wind turbine wake computation with the computational framework presented in the first part. The framework, which is made of the Space–Time Variational Multiscale (ST-VMS) method, ST isogeometric discretization, and the Multidomain Method (MDM), enables accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. Because of the ST context, the framework has higher-order accuracy to begin with; because of the VMS feature of the ST-VMS, it addresses the computational challenges associated with the multiscale nature of the flow; with the isogeometric discretization, it provides increased accuracy in the flow solution; and with the MDM, a long wake can be computed over a sequence of subdomains, instead of a single, long domain, thus reducing the computational cost. Also with the MDM, the computation over a downstream subdomain can start several turbine rotations after the computation over the upstream subdomain starts, thus reducing the computational cost even more. In the computations presented here, the velocity data on the inflow plane comes from a previous wind turbine computation, extracted by projection from a plane located 10 m downstream of the turbine, which has a diameter of 126 m. The resolution studies involve three different spatial resolutions and two different temporal resolutions. The studies show that the computational framework provides, with a practical level of efficiency, high-fidelity solutions in wind turbine long-wake computations.

Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. The model learns the intrinsic correlations of the data generated by the numerical model, making it possible to combine the correlations between wind and wind waves to improve the predictions. In addition, this study also optimizes the long-term prediction ability of the model through the proposed Mask method and Replace mechanism. The experimental results show that the introduction of the wind field can significantly improve the significant wave height prediction results. The research on the prediction effect of the entire study area and two separate stations shows that the prediction performance of the proposed model is better than the existing methods. The model makes full use of the physical correlation between wind and wind waves, and the validity is up to 24 hours. The 24-hour forecast R² reached 0.69.

Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling.

The detection of irrigated areas by means of remote sensing is essential to improve agricultural water resource management. Currently, data from the Sentinel constellation offer new possibilities for mapping irrigated areas at the plot scale. Until now, few studies have used Sentinel-1 (S1) and Sentinel-2 (S2) data to provide approaches for mapping irrigated plots in temperate areas. This study proposes a method for detecting irrigated and rainfed plots in a temperate area (southwestern France) jointly using optical (Sentinel-2), radar (Sentinel-1) and meteorological (SAFRAN) time series, through a classification algorithm. Monthly cumulative indices calculated from these satellite data were used in a Random Forest classifier. Two data years have been used, with different meteorological characteristics, allowing the performance of the method to be analysed under different climatic conditions. The combined use of the whole cumulative data (radar, optical and weather) improves the irrigated crop classifications (Overall Accuary (OA) ≈ 0.7) compared to the classifications obtained using each data separately (OA < 0.5). The use of monthly cumulative rainfall allows a significant improvement of the Fscore of irrigated and rainfed classes. Our study also reveals that the use of cumulative monthly indices leads to performances similar to those of the use of 10-day images while considerably reducing computational resources.

A light-addressable potentiometric sensor (LAPS) is a semiconductor electrochemical sensor based on the field-effect which detects the variation of the Nernst potential on the sensor surface, and the measurement area is defined by illumination. Thanks to its light-addressability feature, an LAPS-based chemical imaging sensor system can be developed, which can visualize the two-dimensional distribution of chemical species on the sensor surface. This sensor system has been used for the analysis of reactions and diffusions in various biochemical samples. In this review, the LAPS system set-up, including the sensor construction, sensing and substrate materials, modulated light and various measurement modes of the sensor systems are described. The recently developed technologies and the affecting factors, especially regarding the spatial resolution and temporal resolution are discussed and summarized, and the advantages and limitations of these technologies are illustrated. Finally, the further applications of LAPS-based chemical imaging sensors are discussed, where the combination with microfluidic devices is promising.

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

In this sequel to a two-part article on wind turbine wake computation with the Space–Time Variational Multiscale (ST-VMS) method and ST isogeometric discretization, we study directional preference in spatial refinement. We evaluate the wake computation accuracy of different combinations of mesh resolutions in the free-stream and cross-flow directions. We also evaluate the accuracy of different combinations of B-spline polynomial orders in those directions. The computational framework is the same as in the two-part article. It is made of, in addition to the ST-VMS and ST isogeometric discretization, the Multidomain Method (MDM). It enables accurate representation of the turbine long-wake vortex patterns in a computationally efficient way. Because of the ST context, the computational framework has higher-order accuracy to begin with; because of the VMS feature, it addresses the computational challenges associated with the multiscale nature of the flow; with the isogeometric discretization, it provides increased accuracy in the flow solution; and with the MDM, a long wake can be computed over a sequence of subdomains, instead of a single, long domain, thus reducing the computational cost. Also with the MDM, the computation over a downstream subdomain can start several turbine rotations after the computation over the upstream subdomain starts, thus reducing the computational cost even more. In the computations presented here, as in the two-part article, the velocity data on the inflow plane comes from a previous wind turbine computation, extracted by projection from a plane located 10 m downstream of the turbine, which has a diameter of 126 m. The directional-refinement studies involve four different spatial resolutions, two different B-spline polynomial orders, and two different temporal resolutions. The studies show that there is some preference for refinement in the cross-flow directions.