Search Results (29)

The earth’s surface has an uneven solar energy density that is sufficient to stimulate solar photovoltaic (PV) production. This causes variations in a solar plant’s output, which are impacted by geometrical elements and atmospheric conditions that prevent it from passing. Motivated by the focus on encouraging increased PV production efficiency, the goal was to use machine learning models (MLM) to map the distribution of solar energy in Mozambique in a regressive literal and geospatial–temporal manner on a short measurement scale. The clear-sky index $\left(K_t^{*}\right)$ theoretical approach was applied in conjunction with MLM that emphasized random forest (RF) and artificial neural networks (ANNs). Solar energy mapping was the result of the methodology, which involved statistically calculating $K_t^{*}$ for the analysis of solar energy in correlational and causal terms of the space-time distribution. Utilizing data from PVGIS, NOAA, NASA, and Meteonorm, a sample of solar energy was gathered at 11 measurement stations in Mozambique over a period of 1 to 10 min between 2012 and 2014 as part of the FUNAE and INAM measurement programs. The statistical findings show a high degree of solar energy incidence, with increments $\left(∆K_t^{*}\right)$ in the average order of −0.05 and $K_t^{*}$ mostly ranging between 0.4 and 0.9. In 2012 and 2014, $K_t^{*}$ was 0.8956 and 0.6986, respectively, because clear days had a higher incident flux and intermediate days have a higher frequency of ${∆K}_t^{*}$ on clear days and a higher occurrence density. There are more cloudy days now 0.5214 as opposed to 0.3569. Clear days are found to be influenced by atmospheric transmittance because of their high incident flux, whereas intermediate days exhibit significant variations in the region’s solar energy. Full article

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R² = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones. Full article

The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions. Full article

The propagation of a beam in atmospheric turbulence causes phase fluctuations due to random variations in the atmospheric refractive index, leading to wavefront distortions. This paper analyzes the mechanisms of wavefront phase changes caused by atmospheric turbulence under different weather conditions and transmission distances. Local wavefront distortions are analyzed using Gaussian curvature, and wavefront distortions are assessed using peak-to-valley values, root mean square values, and the mean square error of the wavefront distortions. Additionally, the effects of different wavelengths and temperatures on wavefront distortions are studied. The experimental results show that the positive and negative Gaussian curvature peak values decrease in the order of snowy day (0.530, −0.850) μm⁻¹, heavy rain (0.345, −0.447) μm⁻¹, dust storm (0.412, −0.057) μm⁻¹, light rain (0.297, −2.75 × 10⁻³) μm⁻¹, sunny (0.154, −0.3 × 10⁻³) μm⁻¹, and cloudy (0.107, −0.1 × 10⁻³) μm⁻¹, with local distortions also decreasing in this order. The peak-to-valley values, root mean square values, and mean square error of the wavefront distortions decrease in the order of heavy rain (129.41 μm, 31.82 μm, 55.18 μm²), dust storm (74.1 μm, 18.84 μm, 51.40 μm²), snowy day (72.09 μm, 17.50 μm, 49.49 μm²), light rain (70.03 μm, 17.11 μm, 37.69 μm²), sunny (57.23 μm, 16.50 μm, 21.84 μm²), and cloudy (52.8 μm, 16.12 μm, 14.40 μm²). Shorter wavelengths exhibit greater phase fluctuations than longer wavelengths, and the degree of distortion increases with temperature. This study lays a theoretical foundation and provides experimental evidence for optical transmission in atmospheric turbulence. Full article

Aircraft contrails exhibit optical properties similar to those of natural high-level clouds (HLCs) and also form persistent cirrus cloudiness. This paper outlines a methodology for detecting and identifying contrails based on the joint analysis of aircraft trajectories (ADS-B monitoring), the vertical profiles of meteorological parameters (radiosonde observation (RAOB) and ERA5 reanalysis), and polarization laser sensing data obtained with the matrix polarization lidar. The potential application of ERA5 reanalysis for determining contrail drift parameters (azimuth, speed, distance, duration, and time of the contrail appearance above the lidar) and interpreting atmospheric polarization laser sensing data in terms of the presence of crystalline ice particles and the assessment of the degree of their horizontal orientation is demonstrated. In the examined case (6 February 2023; Boeing 777-F contrail; flight altitude of 10.3 km; HLC altitude range registered with the lidar of 9.5–10.3 km), the difference in the times of appearance of the contrail over the lidar, calculated from RAOB and ERA5 data, did not exceed 10 min. The difference in the wind direction was 12°, with a wind speed difference of 2 m/s, and the drift distance was approximately the same at about 30 km. The demonstrated technique will allow the experimental dataset of contrail optical and microphysical characteristics to be enhanced and empirical relationships between these characteristics and meteorological quantities to be established. Full article

Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km. Full article

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV_3h and PWV_6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV_3h product performed slightly better than PWV_6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases. Full article

This research investigates the energy efficiency of a novel double-glazing system incorporating solid–solid phase change materials (SSPCMs), which offer significant advantages over traditional liquid–solid phase change materials. The primary objective of this study is to develop a 3D numerical model using the finite volume method, which will be followed by a parametric study under real climatic boundary conditions. A proposed double-glazing setup featuring a 2 mm layer of SSPCM applied on the inner glass pane within the air gap is modeled and analyzed. The simulations consider various transient temperatures and ranges of the SSPCM to evaluate the energy performance of the system under different weather conditions of Miami, FL during the coldest and hottest days of the year, both in sunny and cloudy conditions. The results demonstrate a notable improvement in energy performance compared to standard double-glazing windows (DGWs), with the most efficient SSPCM configuration exhibiting a phase transition temperature and range of 25 °C and 1 °C, respectively. This configuration achieved energy savings of 24%, 26%, and 23% during summer sunny, winter sunny, and winter cloudy days, respectively, relative to DGWs during cooling and heating degree hours. However, a 3% energy loss was observed during summer cloudy days. Overall, the findings of this study have shown the potential for energy savings by incorporating SSPCM with suitable thermophysical properties into double-glazing systems. Full article

Fruit consumption guarantees the supply of most of the necessary nutrients for a complete and balanced diet, as it is a relevant source of vitamins, minerals, and antioxidants. In particular, pomegranate has very interesting medicinal properties, such as an anti-inflammatory effect and the protection of the cardiovascular system, among others. During pomegranate juice production, it appears cloudy and must be clarified to remove suspended solids such as colloids and high-molecular weight tannins. The membrane clarification process is a cost-effective alternative to the conventional methods, resulting in a high-quality product. In this work, the clarification of pomegranate juice using the Triple System Model F1 membrane module was carried out for the Mollar and Wonderful varieties with early and late maturity. Three ultrafiltration membranes with different molecular weight cut-off and different chemical compositions were used. The rejection coefficient and permeate flux (which represent the selectivity of the membranes and the process efficiency, respectively) were measured. GR-40PP showed the best results in terms of membrane selectivity and process efficiency, achieving adequate physicochemical juice parameters. Regarding the comparison of the maturity degree, in general terms, the Mollar variety showed better results. Ripe pomegranates showed greater selectivity, while the process efficiency was higher for the early samples. Full article

Mangroves have extremely high economic and ecological value. Through remote sensing, the spatial distribution of and spatiotemporal changes in mangroves can be accurately obtained, providing data support for the sustainable development of coastal wetlands. However, due to the cloudy and rainy conditions in the growing areas of mangroves, there are relatively few high-quality image data available, resulting in a time difference between regional mosaic images, with a maximum difference of several months, which has a certain impact on accuracy when extracting the spatial distribution of mangroves in some regions. At present, most regional mangrove research has ignored the impact of the time difference between mosaic images, which not only leads to inaccurate monitoring results of mangroves’ spatial distribution and dynamic changes but also limits the frequency of monitoring of regional mangrove dynamic changes to an annual scale, making it difficult to achieve more refined time scales. Based on this, this study takes the coastal mangrove distribution area in China as the research area, uses Landsat 8 and MODIS images as basic data, reconstructs the January 2021 images of the research area based on the FSDAF model, and uses a random forest algorithm to extract the spatial distribution of mangrove forests and analyze the landscape pattern. The results showed that the fused image based on the FSDAF model was highly similar to the validation image, with an R value of 0.85, showing a significant positive correlation, indicating that the fused image could replace the original image for mangrove extraction in the same month. The overall accuracy of the spatial distribution extraction of mangroves based on the fused image was 89.97%. The high sample separation and spectral curve changes highly similar to the validation image indicate that the fused image can more accurately obtain the spatial distribution of mangroves. Compared to the original image, the fused image based on the FSDAF model is closer to the validation image, and the fused image can reflect the changes in mangroves in time series, thus achieving accurate acquisition of dynamic change information in a short time span. It provides data and methodological support for future monitoring of dynamic changes in large-scale mangroves. The total area of mangroves in China in January 2021 based on the fused image was 27,122.4 ha, of which Guangdong had the largest mangrove area, with 12,098.34 ha, while Macao had the smallest mangrove area of only 16.74 ha. At the same time, the mangroves in Guangdong and Guangxi had a high degree of fragmentation and were severely disturbed, requiring strengthened protection efforts, while the mangroves in Hong Kong, Zhejiang, and Macao had regular shapes, benefiting from local active artificial restoration. Full article

In this paper, we investigate the surface temperature of a wall with a facade heat-insulating composite system (FHIC), which has a thin plaster layer, taking into account solar radiation exposure at different degrees of cloudiness during the month. The object of study is a wall with FHIC, on the outer surface of which temperature sensors were mounted and measurements were taken. Air temperatures were also measured for one month of the warm period of the year. The coefficient of absorption of solar radiation by the surface of the facade is calculated based on the measurement of the spectral reflection coefficient. Measurements of direct and scattered solar radiation arriving on a horizontal surface were carried out, and the cloudiness of the sky was also recorded. The calculation of direct and scattered solar radiation was carried out, taking into account the shading of surrounding buildings using the authors’ novel methods. The experimental days were divided into three groups according to the degree of cloudiness; statistically significant differences between the groups for the studied parameters were demonstrated. The temperature of the outer surface of the wall was calculated according to A.M. Shklover’s formula. The measured values of the temperature of the outer surface of the wall were compared with the calculated ones. It was shown that there is a good correlation between the measured and calculated temperatures for different degrees of cloudiness. At the same time, for days with no or slight cloudiness (Group I), when direct solar radiation predominates, the differences reach 1.7 °C; smaller differences are observed for days with average cloudiness (Group II) during daytime hours, with a maximum difference of 0.5 °C; and on days with continuous cloudiness (Group III), when only scattered radiation is present for daytime hours, the maximum difference is 0.3 °C. Statistically significant differences were found between the measured and calculated temperatures for groups of days, divided by the degree of cloudiness, for the experimental period of a day from 10 a.m. to 5 p.m., which indicates the possibility of considering amendments to A.M. Shklover’s formula for sunny days. The results of comparing the measured and calculated heating temperatures of the facade surface also indirectly confirm the correctness of the author’s calculations of the incoming solar radiation, taking into account the effect of the surrounding buildings. The results obtained can be used to study the inertia and durability of building structures under solar radiation. Full article

The walnut is an important nut that has numerous uses worldwide. However, due to dwarf and close plantation methods as well as continuous cloudy or rainy days that occur during periods of walnut oil accumulation, the walnut fruit exhibits varying degrees of stress under low-light conditions. However, the effects of shade on metabolites and genes in walnut embryos remain unclear in the literature. The purpose of this study is to investigate the lipid biosynthesis process that occurs in walnut embryos under shade treatment via the use of metabolomics and transcriptomics analyses. The results indicate that the oil content decreases significantly under shaded conditions, while the protein content increases significantly. The expression levels of fatty acid desaturase 2 (FAD2) and stearoyl-ACP-desaturase (SAD) involved in the lipid biosynthesis mechanism were significantly reduced in the shaded group, which resulted in reductions in oleic (C18:1), linoleic (C18:2), and α-linolenic (C18:3) acids. The reduced oil content was consistent with the downregulation of genes associated with the lipid biosynthesis mechanism. In the amino acid biosynthesis process, the upregulated cysteine synthase (cscK) and anthranilate synthase beta subunit 2 (trpG) genes promoted the accumulation of L-aspartic acid and L-citrulline. The increase in protein content was consistent with the upregulation of genes related to amino acid biosynthesis. Thus, our study provides new insights into the regulatory mechanisms of shade underlying overall walnut fruit quality. Full article

We propose a methodology to derive the aerosol optical depth (AOD) and Angstrom exponent (AE) from calibrated images of an all-sky camera. It is based on a machine learning (ML) approach that establishes a relationship between AERONET measurements of AOD and AE and different signals derived from the principal plane radiance measured by an all-sky camera at three RGB channels. Gaussian process regression (GPR) has been chosen as machine learning method and applied to four models that differ in the input choice: RGB individual signals to predict spectral AOD; red signal only to predict spectral AOD and AE; blue-to-red ratio (BRR) signals to predict spectral AOD and AE; red signals to predict spectral AOD and AE at once. The novelty of our approach mostly relies on obtaining a cloud-screened and smoothed signal that enhances the aerosol features contained in the principal plane radiance and can be applied in partially cloudy conditions. In addition, a quality assurance criterion for the prediction has been also suggested, which significantly improves our results. When applied, our results are very satisfactory for all the models and almost all predictions are close to real values within ±0.02 for AOD and ±0.2 for AE, whereas the MAE is less than 0.005. They show an excellent agreement with AERONET measurements, with correlation coefficients over 0.92. Moreover, more than 87% of our predictions lie within the AERONET uncertainties (±0.01 for AOD, ±0.1 for AE) for all the output parameters of the best model. All the models offer a high degree of numerical stability with negligible sensitivities to the training data, atmospheric conditions and instrumental issues. All this supports the strength and efficiency of our models and the potential of our predictions. The optimum performance shown by our proposed methodology indicates that a well-calibrated all-sky camera can be routinely used to accurately derive aerosol properties. Together, all this makes the all-sky cameras ideal for aerosol research and this work may represent a significant contribution to the aerosol monitoring. Full article

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas. Full article

The quality of the natural sound environment is important for the well-being of humans and for urban sustainability. Therefore, it is important to study how the soundscape of the natural environment affects humans with respect to the different densities of vegetation, and how this affects the frequency of singing events and the sound pressure levels of common birds that generate natural sounds in a commonly visited urban park in Abuja, Nigeria. This study involves the recording of birdsongs, the measurement of sound pressure levels, and a questionnaire evaluation of sound perception and the degree of acoustic comfort in the park. Acoustic comfort, which affects humans, describes the fundamental feelings of users towards the acoustic environment. The results show that first, there is a significant difference between the frequency of singing events of birds for each category of vegetation density (low, medium, and high density) under cloudy and sunny weather conditions, but there is no significant difference during rainy weather. Secondly, the measured sound pressure levels of the birdsongs are affected by vegetation density. This study shows a significant difference between the sound pressure levels of birdsongs and the vegetation density under cloudy, sunny, and rainy weather conditions. In addition, the frequency of singing events of birds is affected by the sound pressure levels of birdsongs with respect to different vegetation densities under different weather conditions. Thirdly, the results from the respondents (N = 160) in this study indicated that the acoustic perception of the park was described as being pleasant, vibrant, eventful, calming, and not considered to be chaotic or annoying in any sense. It also shows that the human perception of birdsong in the park was moderately to strongly correlated with different densities of vegetation, and that demographics play an important role in how natural sounds are perceived in the environment under different weather conditions. Full article