# Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing
## 3 Brief Discussion of the Published Articles in the Special Issue
The increasing share of such intermittent energy sources requires developing and implementing strategies to manage the electrical system [16], and this has mobilized the international scientific community [17; 18]. [PERSON] et al. [2] presented an article focusing on forecasting the beam solar irradiance (DNI) to support the concentrated solar power environment. They developed three models for estimating the DNI attenuation percentage under all-sky conditions based on a sky camera with a rotational shadow band (Model TSI 880). [PERSON] [4] also worked with total sky imagers to develop a low-cost and autonomous cloud detection system in real time. The author pointed out that the system represents a new development in predicting cloud cover and solar radiation over the short term. [PERSON] et al. [6] proposed a novel forecasting model for multistep-ahead (MSA) global solar radiation predictions based on the light gradient boosting machine (LightGBM), which is a tree-based ensemble learning technique. The authors fed the forecasting model with meteorological data and long time series data for global surface solar irradiation for Jeju Island (Korea) provided by the Korean Meteorological Administration. Their experiment results demonstrated that the proposed approach can achieve better performance than other deep learning methods. [PERSON] et al. [10] also focused on a short-term photovoltaic forecasting system based on machine learning methods using remote sensing data. The authors scrutinized the application of different machine learning algorithms, including the random forest regressor, gradient boosting regressor, linear regression, and decision tree regression for a real industry-scale photovoltaic power plant, providing robust forecasting results for either mostly-sunny or mostly-cloudy days. The long time series of surface solar radiation and cloud cover indexes are crucial indicators of the solar power resource. Two articles [8; 11] in this Special Issue focus on analyzing remote sensing data in the land-water interface in a large hydropower reservoir or coastal areas, respectively. [PERSON] et al. [8] used GOES-16 visible imagery, ISCCPdatabase products, and ground measurement sites operating close to a large hydropower reservoir, providing data for the statistical analysis. Their results suggest that floating solar PV power plants in hydropower reservoirs can be an excellent option to integrate renewable energy resources into a hybrid power generation due to the high solar irradiance in the Brazilian territory combined with the prevailing breeze mechanism in large tropical water reservoirs. [PERSON] et al. [11] used two satellite climate data records together with ground-based pyranometer measurements and a Baltic region coastline database to conclude that the annual SSR is 8% higher 20 km off the coastline than 20 km inland. For summer, i.e., June-August, this difference is higher than the annual mean value. Some contributions focused on different topics in solar energy applications, such as the research articles from [PERSON] at al. [7] and [PERSON] et al. [1]. The first paper describes an investigation on the potential application of attribute information stored in point cloud data to support a new computational method for a voxel-based design approach based on shading performance criteria. [PERSON] et al. [1] improved a variant of the whale optimization algorithm to extract PV model parameters. This Special Issue encompasses two research articles focused on remote sensing applied to wind energy resource assessment. [PERSON] et al. [3] compared the wind data recorded by a scanning LiDAR to the observations obtained by a vertical profiling LiDAR operating 400 m apart from each other. The authors executed nine experiments to identify the best practice for offshore wind measurements using the scanning LiDAR and discussed the variations in data availability and accuracy based on the carrier-to-noise ratio (CNR) distribution. [PERSON] et al.