Search Results (382)

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

(1) Background: Wetlands are ecologically significant ecosystems that support biodiversity and contribute to essential environmental functions such as water purification, carbon storage and flood regulation. However, these ecosystems face increasing pressures from land-use change and degradation, prompting the need for scalable and accurate classification methods to support conservation and policy efforts. In this research, our motivation was to test whether high-spatial-resolution PlanetScope imagery can be used with pixel-based machine learning to support the mapping and monitoring of wetlands at a national scale. (2) Methods: This study compared four machine learning classification models—Random Forest (RF), XGBoost (XGB), Histogram-Based Gradient Boosting (HGB) and a Multi-Layer Perceptron Classifier (MLPC)—to detect and map wetland areas across New Zealand. All models were trained using eight-band SuperDove satellite imagery from PlanetScope, with a spatial resolution of ~3 m, and ancillary geospatial datasets representing topography and soil drainage characteristics, each of which is available globally. (3) Results: All four machine learning models performed well in detecting wetlands from SuperDove imagery and environmental covariates, with varying strengths. The highest accuracy was achieved using all eight image bands alongside features created from supporting geospatial data. For binary wetland classification, the highest F1 scores were recorded by XGB (0.73) and RF/HGB (both 0.72) when including all covariates. MLPC also showed competitive performance (wetland F1 score of 0.71), despite its relatively lower spatial consistency. However, each model over-predicts total wetland area at a national level, an issue which was able to be reduced by increasing the classification probability threshold and spatial filtering. (4) Conclusions: The comparative analysis highlights the strengths and trade-offs of RF, XGB, HGB and MLPC models for wetland classification. While all four methods are viable, RF offers some key advantages, including ease of deployment and transferability, positioning it as a promising candidate for scalable, high-resolution wetland monitoring across diverse ecological settings. Further work is required for verification of small-scale wetlands (<~0.5 ha) and the addition of fine-spatial-scale covariates. Full article

A Digital Elevation Model (DEM) provides accurate topographic data for planetary exploration (e.g., Moon and Mars), essential for tasks like lander navigation and path planning. This study proposes the first latent diffusion-based algorithm for DEM generation, leveraging a conditional decoder to enhance reconstruction accuracy from RGB satellite images. The algorithm performs the diffusion process in the latent space and uses a conditional decoder module to enhance the decoding accuracy of the DEM latent vectors. Experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of reconstruction accuracy, providing a new technical approach to efficiently reconstruct DEMs for extraterrestrial planets. Full article

The increasing demand for critical raw materials, such as antimony—a semimetal with strategic relevance in fire-retardant applications, electronic components, and national security—has made the identification of European sources essential for the European Union’s strategic autonomy. Remote sensing offers a valuable tool for detecting alteration minerals associated with subsurface gold and antimony deposits that reach the surface. However, the coarse spatial resolution of the most freely available satellite data remains a limiting factor. The PlanetScope satellite constellation presents a promising low-cost alternative for the academic community, providing 3 m spatial resolution and eight spectral bands. In this study, we evaluated PlanetScope’s capacity to detect Fe³⁺-bearing iron oxides—key indicators of hydrothermal alteration—by applying targeted band ratios (BRs) in northern Portugal. A comparative analysis was conducted to validate its performance using established BRs from Sentinel-2, ASTER, and Landsat 9. The results were assessed through relative comparison methods, enabling both quantitative and qualitative evaluation of the spectral similarity among sensors. Spatial patterns were analyzed, and points of interest were identified and subsequently validated through fieldwork. Our findings demonstrate that PlanetScope is a viable option for mineral exploration applications, capable of detecting iron oxide anomalies associated with alteration zones while offering finer spatial detail than most freely accessible satellites. Full article

Miami, Florida, renowned for its cultural richness and coastal beauty, also faces the concerning challenges created by urban heat islands (UHIs). As one of the hottest cities of the United States, Miami is facing escalating temperatures and threatening heat-related vulnerabilities due to urbanization and climate change. Our study addresses the critical issue of mapping and investigating UHIs in complex urban settings. This study leveraged Planet satellite data and Landsat data to conceptualize and develop appropriate mitigation strategies for UHIs in Miami. Utilizing the Planet satellite imagery and Landsat data, we conducted a combined study of land cover and land surface temperature variations within the city. This approach fuses remotely sensed data to identify the UHI hotspots. This study aims for dynamic approaches for UHI mitigation. This includes studying the status of green spaces present in the city, possible expansion of urban green spaces, the propagation of cool roof initiatives, and exploring the recent climatic trend of the city. The research revealed that built-up areas consistently showed higher land surface temperatures while zones with dense vegetation have lower surface temperatures, supporting the role of urban green spaces in surface temperature reduction. This research can also set a robust model for addressing UHIs in other cities facing rapid urbanization and experiencing mounting temperatures each passing year by helping in assessing LST, land cover, and related spectral indices as well. Full article

Mediterranean subtidal reefs host ecologically significant habitats, including forests of Cystoseira spp., which form complex benthic communities within the photic zone. These habitats are increasingly degraded due to climate change, invasive species, and anthropogenic pressures, particularly in the eastern Mediterranean. In support of habitat monitoring under the EU Natura 2000 directive and the Nature Restoration Regulation, this study investigates the utility of high-resolution satellite remote sensing for mapping subtidal brown algae and associated benthic classes. Using imagery from the SuperDove sensor (Planet Labs, San Francisco, CA, USA), we developed an integrated mapping workflow at the Natura 2000 site GR2420009. Aquatic reflectance was derived using ACOLITE v.20250114.0, and both supervised classification and spectral unmixing were implemented in the EnMAP Toolbox v.3.16.3 within QGIS. A Random Forest classifier (100 fully grown trees) achieved high thematic accuracy across all habitat types (F1 scores: 0.87–1.00), with perfect classification of shallow soft bottoms and strong performance for Cystoseira s.l. (F1 = 0.94) and Seagrass (F1 = 0.93). Spectral unmixing further enabled quantitative estimation of fractional cover, with high predictive accuracy for deep soft bottoms (R² = 0.99; RPD = 18.66), shallow soft bottoms (R² = 0.98; RPD = 8.72), Seagrass (R² = 0.88; RPD = 3.01) and Cystoseira s.l. (R² = 0.82; RPD = 2.37). The lower performance for rocky reefs with other cover (R² = 0.71) reflects spectral heterogeneity and shadowing effects. The results highlight the effectiveness of combining classification and unmixing approaches for benthic habitat mapping using CubeSat constellations, offering scalable tools for large-area monitoring and ecosystem assessment. Despite challenges in field data acquisition, the presented framework provides a robust foundation for remote sensing-based conservation planning in optically shallow marine environments. Full article

Impact cratering determined by collisions with meteorites and asteroids is considered one of the main natural processes in the Solar System, modifying the planets and their satellites surface during time. The Earth includes in its impact record a small number of such events due to active plate tectonics, sedimentation, and volcanism, with these geological processes destroying and burying their impact geomorphological signatures. To enlarge the Earth’s impacts database, new concepts and research methods are necessary, as well as the reinterpretation of old geological and geophysical models. Geomorphological, Geological, and Geophysical (3G) signatures in concealed impacted areas are discussed in this paper; the first offers the target characteristics, while the others give means for detecting their unseen remnants. The 3G signatures have been applied to the Transylvanian Depression, a fascinating geological structure, with difficulties in explaining the direct overlapping of regionally developed thick tuff and thick salt layers, and undecided interpretation of the regional magnetic anomaly. Large and deep sedimentary basins, such as the Precaspian, Alexandria and Transylvanian depressions, are interpreted to have started as impacted areas during the Permian or the Lower Neogene. Geophysical and geological existing information have been reinterpreted, offering a new way in understanding deeply located geological structures. Full article

Wildfires threaten ecosystems, biodiversity, and human livelihood while exacerbating climate change. Accurate identification and monitoring of burned areas (BA) are critical for effective post-fire recovery and management. Although satellite multi-spectral imagery offers a practical solution for BA monitoring, existing methods often prioritize specific spectral bands while neglecting full spectral shape information, which encapsulates overall spectral characteristics. This limitation compromises adaptability to diverse vegetation types and environmental conditions, particularly across varying spatial scales. To address these challenges, we propose the time-series spectral-angle-normalized burn index (TSSA-NBR). This unsupervised BA extraction method integrates normalized spectral angle and normalized burn ratio (NBR) to leverage full spectral shape and temporal features derived from Sentinel-2 time-series data. Seven globally distributed study areas with diverse climatic conditions and vegetation types were selected to evaluate the method’s adaptability and scalability. Evaluations compared Sentinel-2-derived BA with moderate-resolution products and high-resolution PlanetScope-derived BA, focusing on spatial scale and methodological performance. TSSA-NBR achieved a Dice Coefficient (DC) of 87.81%, with commission (CE) and omission errors (OE) of 8.52% and 15.58%, respectively, demonstrating robust performance across all regions. Across diverse land cover types, including forests, grasslands, and shrublands, TSSA-NBR exhibited high adaptability, with DC values ranging from 0.53 to 0.97, CE from 0.03 to 0.27, and OE from 0.02 to 0.61. The method effectively captured fire scars and outperformed band-specific and threshold-dependent approaches by integrating spectral shape features with fire indices, establishing a data-driven framework for BA detection. These results underscore its potential for fire monitoring and broader applications in detecting surface anomalies and environmental disturbances, advancing global ecological monitoring and management strategies. Full article

Global climate change has led to a marked increase in the frequency of record-breaking extreme rainfall events, which often surpass historical benchmarks and pose significant challenges to conventional geological hazard risk assessment methods. This study used a record-breaking extreme rainfall event in Zhenba County, Shaanxi Province, in July 2023 as a case study to develop a tailored risk assessment framework for geological hazards under extreme rainfall conditions. By integrating high-resolution Planet satellite imagery, millimeter-scale surface deformation data derived from SBAS-InSAR, and detailed field investigation results, a comprehensive disaster inventory containing 1012 landslides was compiled. The proposed framework integrates cumulative extreme rainfall metrics with subtle ground deformation indicators and applies four advanced machine learning algorithms—DNN, XGBoost, RF, and LightGBM—for multidimensional hazard assessment. Among these, the DNN model exhibited the highest performance, achieving an AUC of 0.82 and Kappa coefficients of 0.833 (training) and 0.812 (prediction). Further analysis using SHAP values identified distance to rivers, cumulative rainfall, and the Topographic Wetness Index (TWI) as the most influential factors governing landslide occurrence under extreme rainfall conditions. Validation using representative case studies confirmed that the framework effectively identifies high-hazard zones, particularly in areas severely impacted by debris flows and landslide deformation zones. These findings provide a robust scientific foundation and technical basis for early warning, disaster prevention, and mitigation strategies in geologically complex regions increasingly affected by extreme rainfall events. Full article

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article

We revisit 77 relaxed (extra)solar multibody (sub)systems containing 2–9 bodies orbiting about gravitationally dominant central bodies. The listings are complete down to (sub)systems with 5 orbiting bodies and additionally contain 33 smaller systems with 2–4 orbiting bodies. Most of the multiplanet systems (68) have been observed outside of our solar system, and very few of them (5) exhibit classical Laplace resonances (LRs). The remaining 9 subsystems have been found in our solar system; they include 7 well-known satellite groups in addition to the four gaseous giant planets and the four terrestrial planets, and they exhibit only one classical Laplace resonant chain, the famous Galilean LR. The orbiting bodies (planets, dwarfs, or satellites) appear to be locked in/near global mean-motion resonances (MMRs), as these are determined in reference to the orbital period of the most massive (most inert) body in each (sub)system. We present a library of these 77 multibody subsystems for future use and reference. The library listings of dynamical properties also include regular spacings of the orbital semimajor axes. Regularities in the spatial configurations of the bodies were determined from patterns that had existed in the mean tidal field that drove multibody migrations toward MMRs, well before the tidal field was erased by the process of `gravitational Landau damping’ which concluded its work when all major bodies had finally settled in/near the global MMRs presently observed. Finally, detailed comparisons of results help us discern the longest commonly-occurring MMR chains, distinguish the most important groups of triple MMRs, and identify a new criterion for the absence of librations in triple MMRs. Full article

As the planet most similar to Earth in the solar system, Mars holds an important role in exploring significant scientific problems, such as the evolution of the solar system and the origins of life. Research on Mars mainly rely on planetary remote sensing technology, among which thermal infrared remote sensing is of great studying significance. This technology enables the recording of Martian thermal radiation properties. However, the current spatial resolution of Mars thermal infrared remote sensing images remains relatively low, limiting the detection of fine-scale thermal anomalies and the generation of higher-precision surface compositional maps. While updating extraterrestrial exploration satellites can help enhancing the spatial resolution of thermal infrared images, this method entails high cost and long update cycles, making improvement difficult to conduct in the short term. To address this issue, this paper proposes a super-resolution reconstruction method for Mars thermal infrared remote sensing images integrating multi-source data. First, based on the principle of domain adaptation, we introduced a method using highly correlated visible light images as auxiliary to enhance the spatial resolution of thermal infrared images. Then, a multi-sources data integration method is designed to constrain the thermal radiation flux of resulting images, ensuring the radiation distribution remains consistent with the original low-resolution thermal infrared images. Through both subjective and objective evaluations, our method is demonstrated to significantly enhance the spatial resolution of existing Mars thermal infrared images. It optimizes the quality of existing data, increasing the resolution of the original thermal infrared images by four times. In doing so, it not only recovers finer texture details to produce better visual effects than typical super-resolution methods, but also maintains the consistency of thermal radiation flux, with the error after applying the consistency constraint reduced by nearly tenfold, ensuring the applicability of the results for scientific research. Full article

The 1992 reform of the European Common Agricultural Policy (CAP) introduced the Land Parcel Identification System (LPIS), a geodatabase of land parcels used to monitor and regulate agricultural subsidies. Traditionally, the LPIS has relied on high-resolution aerial orthophotos; however, recent advancements in very-high-resolution (VHR) satellite imagery present new opportunities to enhance its effectiveness. This study explores the feasibility of utilizing PlanetScope, a commercial VHR optical satellite constellation, to map agricultural parcels within the LPIS. A test was conducted in Umbria, Italy, integrating existing datasets with a series of PlanetScope images from 2023. A segmentation workflow was designed, employing the Normalized difference Vegetation Index (NDVI) alongside the Edge segmentation method with varying sensitivity thresholds. An accuracy evaluation based on geometric metrics, comparing detected parcels with cadastral references, revealed that a 30% scale threshold yielded the most reliable results, achieving an accuracy rate of 83.3%. The results indicate that the short revisit time of PlanetScope compensates for its lower spatial resolution compared to traditional orthophotos, allowing accurate delineation of parcels. However, challenges remain in automating parcel matching and integrating alternative methods for accuracy assessment. Further research should focus on refining segmentation parameters and optimizing PlanetScope’s temporal and spectral resolution to strengthen LPIS performance, ultimately fostering more sustainable and data-driven agricultural management. Full article

The integration of precision agriculture technologies, such as remote sensing through drones and satellites, has significantly enhanced real-time crop monitoring. This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. A dataset of 40 sampling points was built by combining remote sensing data with field measurements, and seven vegetation indices were calculated for each point. Feature selection was performed using three different methods (F-score, Recursive Feature Elimination, and model-based selection), and the most informative features were then processed through Principal Component Analysis. Eight regression models were trained and evaluated using leave-one-out cross-validation. The best-performing models were Random Forest (R² = 0.96 in training, R² = 0.65 in testing) and k-Nearest Neighbours (R² = 0.74 in training, R² = 0.94 in testing), with kNN demonstrating superior generalization capability on unseen data. These findings highlight the potential of combining drone and satellite imagery for modelling basil agronomic traits, offering valuable insights for optimizing crop management strategies. Full article