Search Results (7,184)

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET₀ framework, where reference ET (ET₀) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R² = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day⁻¹) and minima in winter (January–February: 2.0–2.6 mm day⁻¹). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET₀ estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy. Full article

Building change detection from bi-temporal remote-sensing imagery underpins urban planning, infrastructure monitoring, and disaster assessment. Existing deep-learning methods achieve high accuracy but rely on large parameter counts, while pixel-level supervision provides limited boundary guidance. We propose POCA-lite, a lightweight encoder–decoder with an inference-coupled geometry branch: three geometric prediction heads—distance transform, boundary, and center heatmap—whose outputs are fused back into the decoder via a feedback pathway active at both training and inference. On the LEVIR-CD benchmark under a unified retraining protocol, multi-seed evaluation shows that POCA-lite matches SNUNet in mean F1 while using 47% fewer parameters and 53% fewer FLOPs. Boundary F1 improves by 9.22 pp over the no-geometry baseline. Decomposition ablations reveal two complementary improvement sources: geometric supervision alone recovers 85% of the total gain, while the feedback fusion pathway recovers 92%; their combination achieves the full result. Geometry-aware targets outperform a generic multitask control. Cross-architecture transfer to SNUNet yields +1.06 pp F1. However, cross-dataset evaluation on WHU-CD shows that the method underperforms SNUNet on dense urban morphology, and zero-shot cross-dataset transfer is not established. These results indicate that inference-coupled geometric supervision is effective for lightweight, boundary-sensitive change detection on domains with well-separated building morphology, but its applicability is scope-bounded. Full article

Object detection in optical remote sensing imagery faces persistent challenges from severe instance overlap, extreme spatial density, and motion or atmospheric blur. These degradations cause conventional detectors to over-mix neighboring instance features and fail to separate closely packed objects. To address these limitations, we propose SARC-DETR, a detection framework that augments the RT-DETR architecture with two complementary plug-in modules: Similarity Adaptive Convolution (SAC) and Receptive Field Cross Convolution (RCC). SAC introduces a reproducing-kernel-Hilbert-space (RKHS) motivated similarity gate that selectively suppresses responses inconsistent with local feature prototypes, thereby reducing cross-instance interference in overlapped and blurred regions. RCC constructs a large directional receptive field through orthogonal strip-based aggregation and content-adaptive fusion, enabling efficient long-range context capture without quadratic complexity overhead. Both modules can be integrated into existing DETR-style detectors without modifying the detection head or training protocol. On VisDrone2019-DET, SARC-DETR improves ${\mathbf{AP}}^{\mathbf{val}}$ from 29.7 to 34.8, ${\mathbf{AP}}_{50}^{\mathbf{val}}$ from 49.5 to 56.2, and ${\mathbf{AP}}_S^{\mathbf{val}}$ from 19.2 to 24.8. On DIOR, AP rises from 57.9 to 68.4, and on NWPU VHR-10, from 44.4 to 66.5, demonstrating robust cross-dataset generalization. After structural reparameterization, the additional overhead is less than 0.75 M parameters and 0.36 G FLOPs, confirming deployment suitability for UAV and satellite-based remote sensing applications. Full article

Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas (FEAs) in Lithuania by integrating airborne LiDAR, Sentinel-2 time series, historical orthophotos, and national geospatial datasets. The workflow combines (i) LiDAR-derived canopy height model generation and object-based segmentation, (ii) rule-based aggregation of vegetation segments according to legal forest criteria, (iii) multi-index Sentinel-2 change detection to exclude recent disturbances, and (iv) deep learning-based classification of historical orthophotos to assess stand age. Three detection approaches were evaluated—LiDAR-based, land parcel identification system (LPIS)-based, and their combination. A total of 111,754.4 ha of FEAs were identified outside official forest land, of which 76,204.6 ha meet the minimum age criterion for classification as forest land under national legislation. The designation of these areas as forest land would increase national forest cover from 33.9% to 35.0%. The LiDAR-based approach achieved the highest overall accuracy after dataset refinement (91.5%), while the combined approach yielded the highest precision (97.1%). Accuracy improved notably when reference points affected by definitional conflicts and temporal inconsistencies were excluded, indicating that apparent detection errors were largely attributable to reference data limitations rather than algorithmic failure. The proposed framework offers a scalable solution for wall-to-wall identification and monitoring of unregistered forest resources, with direct applications for national forest inventories and LULUCF reporting. Full article

Deploying deep learning models for high-resolution remote sensing image segmentation remains challenging in resource-constrained scenarios due to the high computational cost of dense prediction and the structural vulnerability of thin objects such as roads. To address these challenges, we propose LiteRoadSegNet, a lightweight and deployment-oriented segmentation framework that achieves a favorable balance among efficiency, accuracy, and structural preservation. The proposed model adopts a compact encoder–decoder architecture composed of a lightweight hierarchical vision transformer and a streamlined decoder, enabling efficient multi-scale feature representation under limited computational budgets. To enhance structural consistency without increasing inference overhead, we further design a low-cost semantic–topological dual-branch contrastive learning scheme which enhances feature discriminability and preserves road connectivity during training. In addition, to improve deployment robustness in cross-region scenarios, we incorporate a lightweight test-time adaptation strategy based on Adaptive Batch Normalization (AdaBN) and sliding-window inference. This strategy enables seamless adaptation to unlabeled target domains without requiring model retraining. Extensive experiments demonstrate that LiteRoadSegNet achieves competitive segmentation performance and superior topology preservation while maintaining a small model footprint and high inference efficiency, making it well suited for large-scale remote sensing applications under resource-constrained environments. Full article

Laser sources emitting light at 1064 nm enable key applications in lidar, quantum photonics, and remote sensing, where high-extinction-ratio intensity modulation is desired to suppress the leakage light at the “off” states during modulation. Here we demonstrate a 1064 nm thin-film lithium niobate (TFLN) Mach–Zehnder electro-optic modulator featuring a half-wave voltage–length product of 2.1 V·cm and a measured electro-optic 3 dB bandwidth exceeding 10 GHz. By optimizing the waveguide and MMI-based interferometer design to improve device balance, we achieve an extinction ratio exceeding 30 dB without thermal tuning. This high extinction ratio enables high-contrast optical modulation at 1064 nm, which is essential for optical switching and other photonic applications requiring high on–off contrast. Full article

In Morocco, snow constitutes a crucial freshwater resource, particularly in the Atlas Mountains, where seasonal snowpack significantly contributes to surface water availability, groundwater recharge, and down-stream water supply. However, snow monitoring in these regions remains challenging due to the scarcity and uneven distribution of ground-based snow depth measurements, especially at high altitudes. This lack of observations limits the accurate assessment of snowpack dynamics and hampers hydrological modeling and water resource management. In this study, we assessed the performance of an empirical approach to estimate snow depth from satellite-derived fractional snow cover (FSC) obtained from MODIS observations. Five empirical FSC snow depth models, including linear and nonlinear exponential formulations, are developed and applied across multiple regions of the Moroccan Atlas Mountains. Model coefficients are calibrated independently for each region using three complementary optimization techniques, nonlinear least squares regression, genetic algorithms, and simulated annealing. Model skill was evaluated during calibration and validation using the Kling–Gupta Efficiency (KGE), Pearson correlation coefficient (R), and absolute error metrics (RMSE and MAE). Results show substantial performance differences across formulations and regions. The most flexible exponential model achieved highest efficiency (KGE up to 0.87; R > 0.85) and 0.26 cm (MAE) under moderate snow conditions. Linear formulations exhibited limited robustness, whereas exponential models better captured snow depth dynamics, particularly in high-altitude areas with deep and persistent snowpacks. These results highlight the potential of FSC-based empirical modeling as a practical and operational solution for snow depth estimation in data-scarce mountainous regions of Morocco. Full article

Bi-temporal change detection (CD) in remote sensing (RS) aims to map image pairs at different times into a shared feature space to discriminate variant regions effectively. However, factors such as cloud interference may disrupt the feature distribution of RS images and cause pseudo-change problems. Existing public change detection datasets also pay less attention to such pseudo-change phenomena. To address the pseudo-change problems of CD applications, we propose a Domain Adversarial Anti-Interference Change Detection Network (DAAINet), which uses ResNet to extract multi-scale features from the original input images. Semantic features are then obtained and fed into a subsequent graph convolution module after soft clustering, by introducing a domain adversarial structure to align the feature space in RS images. In the graph convolution module, the association of node context is utilized to predict the adjacency relationship between objects. We collected data and constructed a real-world dataset called “Cloud Interference Change Detection” (CICD), which focuses on real bi-temporal remote sensing image data containing cloud interference and includes pseudo-changes caused by factors such as the presence of temporary objects and illumination changes. Experimental results demonstrate that our method is more robust and efficient compared to other state-of-the-art methods on two public CD datasets, and achieves state-of-the-art performance on the noise-corrupted CICD dataset, surpassing prior methods by up to 5.67%p in IoU and 1.42%p in recall. Full article

Enormous land exploitation is triggering strong urban growth, and this phenomenon is exacerbating the already existing problem of rising land surface temperatures. This leads to increased human activities and a disruption of the balance of natural ecosystems. The application of thermal remote sensing techniques is, in this context, helpful in learning about the condition of the earth’s surface and monitoring how it changes over time. This paper utilizes thermal data from 2000, 2010 and 2020, with supplementary data from 2024, to assess current trends in two different seasonal conditions (rainy period and low rainy period). Two different areas (urban and rural) of the central Vietnamese Province of Thua Thien-Hue have been analyzed to compare them. Processing Landsat-5 TM, Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Sentinel-2 satellite images, a heat map of the study area was defined, considering hot spots and cold spots. As support for this analysis, spectral indexes have been developed for a better comprehension of the land cover change over the years and to provide a validation of the thermal analysis. This paper aims to assess the threat posed by the intensification of the urban heat island effect on cultural heritage sites. The case studies are represented by areas where there are urban growing and cultural heritage sites to be preserved, such as the UNESCO-listed Hue Citadel. Full article

Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R²) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. Full article

By partitioning agricultural fields into units with similar yield-limiting factors, Management Zone (MZ) delineation provides the spatial basis for variable-rate application of inputs such as nitrogen, seed, and irrigation. To evaluate the operational implementation of MZ methodologies, this paper analyzes 137 peer-reviewed papers published between 2000 and 2025, extracting data on agronomic contexts, sensing inputs, computational workflows, and validation strategies. Our analysis reveals a clear methodological shift: while early studies relied heavily on data such as soil properties, recent literature is dominated by multisource data fusion that combines static soil proxies (e.g., apparent electrical conductivity) with dynamic remote sensing vegetation indices. Methodologically, the literature relies heavily on similarity-based clustering, specifically fuzzy c-means and k-means, often applied to raw spatial grids or Principal Component Analysis (PCA) transformations. Although machine learning and optimization-based approaches have increased in recent years, rigorous agronomic and economic validation remains limited, while internal cluster validity indices (e.g., FPI, NCE) and inferential statistical tests (e.g., ANOVA) are widely used to assess delineated zones, only 13 of the reviewed papers explicitly evaluated the economic or environmental net returns of the delineated zones. To transition MZ delineation from a classification problem to an operational decision-support tool, the current literature suggests a need to shift validation efforts away from internal clustering metrics toward multi-year yield stability assessments and direct economic cost–benefit analyses. Full article

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the interaction of physical, chemical, and biological factors. Therefore, timely and accurate monitoring is essential for early warning and scientific research. This paper comprehensively reviews recent advances in HAB monitoring technologies, with a focus on two core components: sensors and monitoring platforms. First, organized around key environmental parameters, it summarizes the principles, applications, and limitations of in situ sensors, such as multi-parameter water quality sondes, Imaging Flow Cyto-bots (IFCB), and Environmental Sample Processors (ESP), as well as laboratory-based analytical techniques such as HPLC-MS for measuring physical, chemical, and biological indicators. Second, it compares the technical characteristics of three major monitoring platforms (including field surveys, remote sensing, and autonomous systems) and discusses their potential for synergistic application. Finally, this review proposes a future framework for an integrated “Space–Air–Ground–Sea” intelligent monitoring network and explores possible pathways to address current challenges through cross-platform data fusion, sensor miniaturization, intelligentization, and artificial intelligence-driven decision support. This review aims to provide a comprehensive reference for the optimization and innovation of HAB monitoring technologies and to promote the development of the field toward greater integration, intelligence, and real-time monitoring capability. Full article

Remote sensing image classification is one of the fundamental tasks in the field of remote sensing and plays a critical role in Earth observation applications. However, the inherent multi-scale characteristics of this task pose significant challenges to scene classification. To address these issues, we propose a novel framework that integrates the Contrastive Language–Image Pre-training (CLIP) model, multi-scale data, and efficient inference strategy. The proposed framework transfers general-purpose features learnt from natural images to remote sensing image classification. Specifically, this framework leverages the rich feature representations learnt by the CLIP model in the contrastive learning procedure and adopts it as the backbone network of the model to extract fine-grained and multi-scale features for remote sensing images. That is, the model can learn local fine-grained details but also encode global contextual information useful for the classification of visually similar scene categories. Afterwards, AdapterFormer module is inserted into the few selected layers of CLIP model, which can effectively enhance model performance and have low computational overhead. This helps efficient knowledge sharing and introduces new features at the model level. Furthermore, to alleviate possible performance deterioration brought about by multi-scale feature variation, a multi-scale training set is constructed at data level, providing complementary multi-scale information. Through the synergy of all these strategies above, the proposed method greatly improves the classification performance of multi-scale remote sensing images. Extensive experiments on the MEET dataset (it includes 80 fine categories and more than 800,000 samples) show that the proposed method greatly improves the performance. Compared with general-purpose classification networks and remote sensing-related models, the proposed method always gets state-of-the-art results. Full article

DDM is the primary Level-1 observable of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R). Over the past decade, the discretization strategy of Delay-Doppler Map (DDM) systems has been primarily optimized for ocean remote sensing. This study highlights the impact of discretization effects in DDM sampling on land applications. The discretization effect in the Doppler dimension is first evaluated by comparing simulated and observed DDM slices at the Doppler bin corresponding to the DDM peak. The results indicate that the noise in DDM observations can be approximated as additive thermal noise. Based on an ideal autocorrelation function template, a matched filtering analysis is then applied to estimate the optimized specular point delay and reconstruct the peak power. Using multi-constellation observations from Tianmu-1, the results show that the original DDM peak delay exhibits a systematic delay relative to the optimized specular point delay, with biases of approximately 0.02 chips for GPS and GLONASS, and 0.17 chips for BDS (BeiDou) and Galileo. For BOC(1,1) signals in BDS and Galileo, the reflectivity remains underestimated by ~1.4 dB even at a delay sampling interval of 1/8 chip. The results indicate that under coherent scattering conditions over land, direct use of the DDM peak leads to underestimation of reflectivity due to discretization. The correction proposed in this study reduces the relative differences in reflectivity observations among the four GNSS systems. This study suggests that peak under-sampling should be considered in GNSS-R applications, and higher delay sampling resolution is required for land observations. Full article

Cross-modal image–text retrieval enables searching and retrieving of semantically relevant data across heterogeneous modalities, acting as a pivotal technology for interpreting massive remote sensing (RS) data. Despite recent progress, most existing methods in remote sensing cross-modal image–text retrieval (RSCIR) rely on high-dimensional real-valued embeddings, which suffer from excessive storage overhead and slow retrieval speeds, severely limiting their scalability in real-world applications. Conversely, while hashing techniques offer efficiency, traditional methods often fail to preserve the fine-grained semantic consistency required for complex RS scenes, leading to significant performance degradation. To bridge this gap, this paper proposes a novel framework named ConHash (Cross-modal Contrastive Hashing), which transfers the discriminative power of pre-trained vision–language models into a compact binary Hamming space. Specifically, ConHash comprises three synergistic components: (1) a hash module designed to project continuous embeddings into a latent discrete space while reducing information loss; (2) a hash-aware contrastive constraint that enforces cross-modal alignment directly in the hash space; and (3) a collaborative hybrid optimization strategy that jointly constrains real-valued embeddings and hash representations. Extensive experiments on RSICD and RSITMD demonstrate that ConHash achieves a favorable balance between accuracy and efficiency. Using 512-bit hash codes with L1 quantization loss as the main configuration, ConHash achieves mR values of 21.69% and 35.79% on RSICD and RSITMD, respectively. It also provides up to 3.50× retrieval speedup and a 32× theoretical storage reduction compared with 512-dimensional float32 embeddings, making it suitable for scalable remote sensing retrieval applications. Full article