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Photovoltaic (PV) panels convert sunlight into electricity, and play a crucial role in energy decarbonization, and in promoting urban resources and environmental sustainability. The area of PV panels in China’s coastal regions is rapidly increasing, due to the huge demand for renewable energy. However, a rapid, accurate, and robust PV panel mapping approach, and a practical PV panel classification strategy for large-scale applications have not been established. Here, we developed a new approach that uses spectral and textural features to identify and map the PV panels there were in coastal China in 2021 using multispectral instrument (MSI) and synthetic aperture radar (SAR) images, and the Google Earth Engine (GEE), to differentiate PV panels according to their underlying surface properties. Our 10-m-spatial-resolution PV panel map had an overall accuracy of 94.31% in 2021. There was 510.78 km2 of PV panels in coastal China in 2021, which included 254.47 km2 of planar photovoltaic (PPV) panels, 170.70 km2 of slope photovoltaic (SPV) panels, and 85.61 km2 of water photovoltaic (WPV) panels. Our resultant PV panel map provides a detailed dataset for renewable layouts, ecological assessments, and the energy-related Sustainable Development Goals (SDGs).

Since the main attenuation of solar irradiance reaching the earth’s surface is due to clouds, it has been hypothesized that global horizontal irradiance attenuation and its temporal variability at a given location could be characterized simply by cloud properties at that location. This hypothesis is tested using global horizontal irradiance measurements at two stations in San Antonio, Texas, and satellite estimates of cloud types and cloud layers from the Geostationary Operational Environmental Satellite (GOES) Surface and Insolation Product. A modified version of an existing solar attenuation variability index, albeit having a better physical foundation, is used. The analysis is conducted for different cloud conditions and solar elevations. It is found that under cloudy-sky conditions, there is less attenuation under water clouds than those under opaque ice clouds (optically thick ice clouds) and multilayered clouds. For cloud layers, less attenuation was found for the low/mid layers than for the high layer. Cloud enhancement occurs more frequently for water clouds and less frequently for mixed phase and cirrus clouds and it occurs with similar frequency at all three levels. The temporal variability of solar attenuation is found to decrease with an increasing temporal sampling interval and to be largest for water clouds and smallest for multilayered and partly cloudy conditions. This work presents a first step towards estimating solar energy potential in the San Antonio area indirectly using available estimates of cloudiness from GOES satellites.

Forest managers rely on accurate burn severity estimates to evaluate post-fire damage and to establish revegetation policies. Burn severity estimates based on reflective data acquired from sensors onboard satellites are increasingly complementing field-based ones. However, fire not only induces changes in reflected and emitted radiation measured by the sensor, but also on energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo (LSA) are greatly affected by wildfires. In this study, we examine the usefulness of these elements of energy balance as indicators of burn severity and compare the accuracy of burn severity estimates based on them to the accuracy of widely used approaches based on spectral indexes. We studied a mega-fire (more than 450 km2 burned) in Central Portugal, which occurred from 17 to 24 June 2017. The official burn severity map acted as a ground reference. Variations induced by fire during the first year following the fire event were evaluated through changes in ET, LST and LSA derived from Landsat data and related to burn severity. Fisher’s least significant difference test (ANOVA) revealed that ET and LST images could discriminate three burn severity levels with statistical significance (uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate (κ = 0.63 and 0.57, respectively), similar to the accuracy of the estimate based on dNBR (κ = 0.66). We conclude that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting as useful indicators of burn severity for mega-fires in Mediterranean ecosystems, may provide critical information about how energy balance changes due to fire.

The response of vegetation to regional climate change was quantified between 1982 and 2010 in the Mongolian plateau by integrating the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) (1982–2006) and the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000–2010). Average NDVI values for the growing season (April–October) were extracted from the AVHRR and MODIS NDVI datasets after cross-calibrating and consistency checking the dataset, based on the overlapping period of 2000–2006. Correlations between NDVI and climatic variables (temperature and precipitation) were analyzed to understand the impact of climate change on vegetation dynamics in the plateau. The results indicate that the growing-season NDVI generally exhibited an upward trend with both temperature and precipitation before the mid- or late 1990s. However, a downward trend in the NDVI with significantly decreased precipitation has been observed since the mid- or late 1990s. This is an apparent reversal in the NDVI trend from 1982 to 2010. Pixel-based analysis further indicated that the timing of the NDVI trend reversal varied across different regions and for different vegetation types. We found that approximately 66% of the plateau showed an increasing trend before the reversal year, whereas 60% showed a decreasing trend afterwards. The vegetation decline in the last decade is mostly attributable to the recent tendency towards a hotter and drier climate and the associated widespread drought stress. Monitoring precipitation stress and associated vegetation dynamics will be important for raising the alarm and performing risk assessments for drought disasters and other related natural disasters like sandstorms.

As the “Water Tower of Asia” and “The Third Pole” of the world, the Qinghai–Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948–2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = −0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions.

Very short-term forecasts of wind power provide electricity market participants with extremely valuable information, especially in power systems with high penetration of wind energy. In very short-term horizons, statistical methods based on historical data are frequently used. This paper explores the use of dual-Doppler radar observations of wind speed and direction to derive five-minute ahead deterministic and probabilistic forecasts of wind power. An advection-based technique is introduced, which estimates the predictive densities of wind speed at the target wind turbine. In a case study, the proposed methodology is used to forecast the power generated by seven turbines in the North Sea with a temporal resolution of one minute. The radar-based forecast outperforms the persistence and climatology benchmarks in terms of overall forecasting skill. Results indicate that when a large spatial coverage of the inflow of the wind turbine is available, the proposed methodology is also able to generate reliable density forecasts. Future perspectives on the application of Doppler radar observations for very short-term wind power forecasting are discussed in this paper.

Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.