Search Results (1,618)

Precise estimation of net radiation (Rn) is fundamental to understanding surface energy balance and is critical for accurately determining crop water requirements, especially using remote sensing and geospatial techniques. The core objective of this study is to evaluate multi-satellite-based net radiations on major cropped areas of the Punjab and Sindh provinces of Pakistan. In this study, overlapping scenes from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, and Sentinel 2 were used from 2016 to 2020 along with three temperature products MOD11A1, Landsat 8 (brightness temperature), and ERA5. The multi-satellite-based net radiation estimations on overlapping days were compared with the Global Land Data Assimilation System (GLDAS) dataset. The models based on Landsat 8 and Sentinel 2 data exhibited good performance, with a Nash–Sutcliffe Efficiency (NSE) of 68.9%, a mean error (ME) of 13.918 W/m², and a bias of 50.669 W/m². The results indicated that Landsat 8 and Sentinel 2 data produced reliable estimations of net radiation, while MODIS data tended to overestimate due to its higher spatial resolution and broader coverage area. Landsat 8-based estimations are good compared to others, as it has good correlation coefficient and lower RMSE values. The study concludes that Landsat 8 provides the most reliable estimates of net radiation for determining crop water requirements, outperforming other datasets in accuracy. The findings underscore the importance of using high-resolution multi-satellite data for precise agricultural water management, recommending its use in future studies and water resource planning in Pakistan. Full article

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal’s urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model’s potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally. Full article

Soil moisture (SM) is a crucial factor in land-atmosphere interactions and climate systems, affecting surface energy, water budgets, and weather extremes. In the Three-River-Source Region (TRSR) of China, rapid climate change necessitates precise SM monitoring. This study employs a novel UNet-Gan model to integrate and downscale SM data from 17 CMIP6 models, producing a high-resolution (0.1°) dataset called CMIP6_UNet-Gan. This dataset includes SM data for five depth layers (0–10 cm, 10–30 cm, 30–50 cm, 50–80 cm, 80–110 cm), four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The UNet-Gan model demonstrates strong performance in data fusion and downscaling, especially in shallow soil layers. Analysis of the CMIP6_UNet-Gan dataset reveals an overall increasing trend in SM across all layers, with higher rates under more intense emission scenarios. Spatially, moisture increases vary, with significant trends in the western Yangtze and northeastern Yellow River regions. Deeper soils show a slower response to climate change, and seasonal variations indicate that moisture increases are most pronounced in spring and winter, followed by autumn, with the least increase observed in summer. Future projections suggest higher moisture increase rates in the early and late 21st century compared to the mid-century. By the end of this century (2071–2100), compared to the Historical period (1995–2014), the increase in SM across the five depth layers ranges from: 5.5% to 11.5%, 4.6% to 9.2%, 4.3% to 7.5%, 4.5% to 7.5%, and 3.3% to 6.5%, respectively. Full article

The optimization of land use structure is a key measure to promote the allocation of land resources, ensure sustainable land development, and address the human-land conflict. Since the 21st century, land use in China has exhibited spatiotemporal dynamic development characteristics in response to socio-economic growth and changes in regional geographical conditions. The academic community, both domestically and internationally, has enriched and refined the research system on China’s land use, driven by the need to optimize its land use structure. This study systematically reviews relevant land use research literature from 2000 to 2024, utilizing bibliometric analysis and visual mapping to conduct phased evaluations and an overall review. The existing LUCC research framework in China is extensive, with a strong focus on land use issues in the context of rapid development. Building on this review and incorporating practical needs, theoretical innovation, interdisciplinary integration, and expansion across multiple fields, we aim to propose a framework for future land resource research. This framework includes: (i) Establishing a Multi-functional Land Use System: This approach promotes the coordinated development of ecological and social benefits of land use. (ii) Enhancing Effective Assessment and Management of Ecological Risks: Such efforts contribute to optimizing spatial planning and ensuring land security. (iii) Addressing Low Land Use Efficiency: Focusing on this issue will enable more precise management aligned with regional characteristics. (iv) Exploring the Application of Multi-disciplinary and Cross-field Technologies in Land Use Efficiency Assessment: This integration will advance spatial planning research. (v) Expanding Research on Multi-functional Land Use and Multi-element Integration: This direction fosters coordination across various planning frameworks, promoting synergies in land use research. Full article

Phosphorus (P) is critical for maximizing agricultural production and represents an appreciable input cost. Geologic sources of P that are most easily mined are a finite resource, while P transported from agricultural land to surface waters contributes to water quality degradation. Improved knowledge of P timing needs by corn (maize) can help inform management decisions that increase P use efficiency, which is beneficial to productivity, economics, and environmental quality. The objective of this study was to evaluate P application timing on the growth and yield components of corn. Corn was grown in a sand-culture hydroponics system that eliminated confounding plant–soil interactions and allowed for precise control of nutrient availability and timing. All nutrients were applied via drip irrigation and were therefore 100% bioavailable. Eight P timing treatments were tested using “low” (L) and “sufficient” (S) P concentrations. In each of the three growth phases, solution P application levels were changed or maintained, resulting in eight possible combinations, LLL, LLS, LSL, LSS, SLL, SSL, SLS, and SSS, where the first, second, and third letters indicate P solution application levels from planting to V6, V6 to R1, and R1 to R6, respectively. All other nutrients were applied at sufficient levels. Sacrificial samples were harvested at V6, R1, and R6 and evaluated for various yield parameters. Plants that received sufficient P between V6 and R1 produced a significantly higher grain yield than plants that received low P between V6 and R1 regardless of the level of P supply before V6 or after R1. The grain yield of plants that received sufficient P only between V6 and R1 did not differ significantly from plants that received only sufficient P (SSS), due to (1) a greater ear P concentration at R1; (2) an efficient remobilization of assimilates from the stem and leaf to grains between R1 and R6 (source–sink relationship); (3) a higher kernel/grain weight; and (4) less investment into root biomass. Full article

Drones, with their ability to vertically take off and land with their stable hovering performance, are becoming favorable in both civilian and military domains. However, this introduces risks of its misuse, which may include security threats to airports, institutes of national importance, VIP security, drug trafficking, privacy breaches, etc. To address these issues, automated drone detection systems are essential for preventing unauthorized drone activities. Real-time detection requires high-performance devices such as GPUs. For our experiments, we utilized the NVIDIA Jetson Nano to support YOLOv9-based drone detection. The performance evaluation of YOLOv9 to detect drones is based on metrics like mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score. Experimental data revealed significant improvements over previous models, with a mAP of 95.7%, a precision of 0.946, a recall of 0.864, and an F1-score of 0.903, marking a 4.6% enhancement over YOLOv8. This paper utilizes YOLOv9, optimized with pre-trained weights and transfer learning, achieving significant accuracy in real-time drone detection. Integrated with the NVIDIA Jetson Nano, the system effectively identifies drones at altitudes ranging from 15 feet to 110 feet while adapting to various environmental conditions. The model’s precision and adaptability make it particularly suitable for deployment in security-sensitive areas, where quick and accurate detection is crucial. This research establishes a solid foundation for future counter-drone applications and shows great promise for enhancing situational awareness in critical, high-risk environments. Full article

This study investigated the accuracy and precision of a commercially available PNT solution that uses DGNSS-SBAS technology. Time and position data were sampled at a frequency of 20Hz during both a short and long trajectory of a simulated controlled dry-land slalom, as well as during a real-world on-water slalom exercise. The primary objective was to assess the positional accuracy, availability, integrity, and service continuity of the PNT solution while evaluating its ability to differentiate between trajectories. Additionally, the simulated results were compared with an on-water real-world slalom test to validate the findings. The results of the controlled dry-land slalom test indicate that the PNT solution provided accurate measurements with an overall mean ± SD Hrms of 0.20 ± 0.02 m. The integrity measures, HDOD and PDOP, were found to be ideal to excellent, with values of 0.68 ± 0.03 and 1.36 ± 0.07, respectively. The PNT solution utilised an average of 20 ± 1 satellites from the constellation, resulting in an accuracy of <1.5% when measuring the known trajectory of 50 simulated slalom runs. The data from the real-world on-water slalom test supported these findings, providing similar or improved results. Based on these findings, a PNT solution using DGNSS-SBAS can be considered an effective means of tracking athlete trajectory in the sport of canoe slalom. Future research should be conducted to quantify its efficacy more precisely. Full article

In recent years, land subsidence has become a crucial factor affecting urban safety and sustainable development, especially in Wan’an Town. To accurately monitor and analyze the land subsidence in Wan’an Town, this study uses the PS-InSAR technique combined with an improved DEM for detailed research on land subsidence in Wan’an Town. PS-InSAR, or Permanent Scatterer Interferometric SAR, is suitable for high-precision monitoring of surface deformation. The natural neighbor interpolation method optimizes DEM data, improving its spatial resolution and accuracy. In this study, multiple periods of SAR imagery data of Wan’an Town were collected and preprocessed through radiometric calibration, phase unwrapping, and other steps. Using the PS-InSAR technique, the phase information of permanent scatterers (PS points) on the surface was extracted to establish a deformation model and preliminarily analyze the land subsidence in Wan’an Town. Concurrently, the DEM data were optimized using the natural neighbor interpolation method to enhance its accuracy. Finally, the optimized DEM data were combined with the surface deformation information extracted through the PS-InSAR technique for a detailed analysis of the land subsidence in Wan’an Town. The research results indicate that the DEM data optimized by the natural neighbor interpolation method have higher accuracy and spatial resolution, providing a more accurate reflection of the topographical features of Wan’an Town. The research found that the optimized DEM provided a more accurate reflection of Wan’an Town’s topographical features. By combining PS-InSAR data, subsidence information from 2016 to 2024 was calculated. The study area showed varying degrees of subsidence, with rates ranging from 6 mm/year to 10 mm/year. Four characteristic deformation areas were analyzed for causes and influencing factors. The findings contribute to understanding urban land subsidence, guiding urban planning, and providing data support for geological disaster warning and prevention. Full article

One of the consequences of the globalisation of food markets is the effort enabling the control of food security and its origin. This might be traced by using different chemical composition analyses. However, for Central Europe, there is a lack of knowledge about the original reference values as well as their heterogeneity among the lands and countries. This study focused on characterizing the mineral profiles of apple tree fruits and comparing these profiles among different districts in Central Europe. The fruits of the apple cultivars ‘Gala’ and ‘Golden Delicious’ originated in the Czech Republic and Poland. The mineral and isotopic content of the apple fruit flesh was analysed using ICP-MS. The data were processed using the ANOVA test and compositely analysed using the PCA and LDA models. The results show relatively high variation in element distribution, particularly ⁸⁷Sr/⁸⁶Sr, Mn, Zn, Cu, Ca, P, and B, ranging between 20.6 and67.9% for both cultivars on average. However, their high variability within particular districts complicates the resolution of the LDA model. The reasons are linked to the geomorphological and pedological heterogeneity of the analysed districts as well as the particular sensitivity of the set of chosen primers to agronomic practices and tree performance. For this region, only partial separation among districts could be obtained by P, Ca, and Cu content, as well as the isotopic ratio of ¹⁰B/¹¹B. However, the resolution of the geographical discrimination needs to be improved by an enhanced set of primers, the use of more precise analytical techniques for the Sr isotopic ratio, or by multiple chemical analyses. Furthermore, the heterogeneity of the analysed districts could be tackled by more detailed analyses at the level of micro-regions. Full article

Land-based sources of marine outfalls are a major source of marine pollution. The monitoring of land-based sources of marine outfalls is an important means for marine environmental protection and governance. Traditional on-site manual monitoring methods are inefficient, expensive, and constrained by geographic conditions. Satellite remote sensing spectral analysis methods can only identify pollutant plumes and are affected by discharge timing and cloud/fog interference. Therefore, we propose a smart monitoring method for land-based sources of marine outfalls based on an improved YOLOv8 model, using unmanned aerial vehicles (UAVs). This method can accurately identify and classify marine outfalls, offering high practical application value. Inspired by the sparse sampling method in compressed sensing, we incorporated a multi-scale dilated attention mechanism into the model and integrated dynamic snake convolutions into the C2f module. This approach enhanced the model’s detection capability for occluded and complex-feature targets while constraining the increase in computational load. Additionally, we proposed a new loss calculation method by combining Inner-IoU (Intersection over Union) and MPDIoU (IoU with Minimum Points Distance), which further improved the model’s regression speed and its ability to predict multi-scale targets. The final experimental results show that the improved model achieved an mAP50 (mean Average Precision at 50) of 87.0%, representing a 3.4% increase from the original model, effectively enabling the smart monitoring of land-based marine discharge outlets. Full article

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R²) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions. Full article

Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1DTempCNN and LSTM models, and spectral features using the 1DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS. Full article

China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems. Full article

Traditional methods of pest control for sweet potatoes cause the waste of pesticides and land pollution, but the target detection algorithm based on deep learning can control the precise spraying of pesticides on sweet potato plants and prevent most pesticides from entering the land. Aiming at the problems of low detection accuracy of sweet potato plants and the complex of target detection models in natural environments, an improved algorithm based on YOLOv8s is proposed, which can accurately identify early sweet potato plants. First, this method uses an efficient network model to enhance the information flow in the channel, obtain more effective global features in the high-level semantic structure, and reduce model parameters and computational complexity. Then, cross-scale feature fusion and the general efficient aggregation architecture are used to further enhance the network feature extraction capability. Finally, the loss function is replaced with InnerFocaler-IoU (IFIoU) to improve the convergence speed and robustness of the model. Experimental results showed that the mAP0.5 and model size of the improved network reached 96.3% and 7.6 MB. Compared with the YOLOv8s baseline network, the number of parameters was reduced by 67.8%, the amount of computation was reduced by 53.1%, and the mAP0.5:0.95 increased by 3.5%. The improved algorithm has higher detection accuracy and a lower parameter and calculation amount. This method realizes the accurate detection of sweet potato plants in the natural environment and provides technical support and guidance for reducing pesticide waste and pesticide pollution. Full article

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas. Full article