Intertidal zones are transitional areas between marine and terrestrial systems and are significant for ecological systems, socio-economic activities, and coastal erosion mitigation. However, intertidal topography is often degraded and lost by natural coastal processes and anthropogenic impacts. Accurate and frequent mapping of intertidal topography is essential for effective coastal management. Previous

The increased demand for forest management, particularly thinning, in Japan is a direct consequence of aging planted forests. However, forest inventories (FI) in Japan lack crucial details regarding the developmental stage or ecological status of forests, often only providing tree species, age, and owner information. The relative yield index (Ry) is a forest density index widely used in the forestry

According to few recent studies, exogenous organic matters (EOM) can be detectable on either emerging vegetation or bare soil using optical and radar remote sensing techniques. Nevertheless, these image processing approaches considered one single EOM, one season and/or year only and were limited to one surface condition prior to spreading. So far no method addressed the simultaneously tracking of both

Flood monitoring is a complex task involving multimodal data mining and multitask collaboration. In order to leverage the role of multimodal data in flood management, conducting visual-language pretraining (VLP) in the field of flood disaster monitoring and obtaining foundational pretraining models that are suitable for multiple downstream flood-related tasks is an urgent problem that needs to be addressed

Crop yield estimation is vital for agricultural management but often struggles with predicting extreme values that can significantly impact operations and markets. Traditional models face challenges with these extremes, leading to biased and inaccurate predictions. To address this challenge, our study introduces two innovative strategies. First, we propose a cost-sensitive loss function, ExtremeLoss

The Valencian Anchor Station (VAS) (Spain) is an outstanding site operating as a central location for calibrating and validating numerous remote sensing instruments and products. Hence, an accurate characterization of its land cover is required. This research conducts a land cover classification within the VAS station (10 × 10 km2) and its surrounding area (30 × 30 km2) for 2021 using multi-temporal

Effective measurement and monitoring of habitat fragmentation are essential for achieving the United Nations’ Sustainable Development Goals, especially given the ongoing global expansion of urbanization. However, current researches primarily address habitat connectivity or landscape dissection separately, while neglecting the impact of landscape dissection on habitat pattern. In this study, we adopt

The coherence estimation errors in phase linking can be mitigated through the weighted alignment of interferometric pairs and the intermediate filtering of data subsets. The Sequential Estimator (SE) serves as a representative method. It divides the coherence-weighted matrix into smaller subsets, using image compression and recursive estimation to enhance phase linking. However, the SE method has inherent

Soil Organic Carbon (SOC) is vital for the global carbon cycle, agricultural sustainability, and climate change. Process-based models like Rothamsted carbon model (RothC) simulate SOC dynamics, but their accuracy relies on revisited soil samples for calibration, which are often scarce, especially in environmentally heterogeneous regions. This study proposes a novel approach to generate pseudo revisited

Trend and attribution analysis of vegetation greenness is crucial to explain and predict ecosystem responses to climate change. The common practice to detect and explain greenness pattern from remote sensing time series is mostly based on pixel-by-pixel analysis, which often fails to account for spatial autocorrelation and may lead to spurious patterns. Here we applied the Partitioned Autoregressive

Suspended sediments are vital indicators of water quality, so understanding the dynamic processes and influence factors of suspended sediment concentration (SSC) is crucial in estuaries or coastal waters. This study presents a comprehensive strategy for coupling several methods of remote sensing, field observation, and numerical simulation to systematically study spatio-temporal variability of suspended

Personal laser scanning has evolved into a cutting-edge technology for obtaining fast and accurate biometric measurements of individual trees in a forest. However, recent studies assessing tree species labels on single tree point clouds have been insufficiently accurate in complex forest ecosystems; moreover, explainability of machine-learning methods used in published studies has been insufficient

This study introduces a flood index-enhanced deep learning model for coastal inundation mapping leveraging dual-polarization and bitemporal Sentinel-1 synthetic aperture radar (SAR) imagery. The proposed model, FIE-Net, introduces two key innovations to improve flood mapping accuracy. First, it integrates the Ratio Image (RI), a primary flood index derived from bitemporal SAR data, which provides a

Boreal drained-peatland forests provide diverse, interlinked ecosystem services (ESs), critical for informed decision-making in forest management. We mapped five ESs: bilberry yield, visual amenity, biodiversity conservation, carbon storage, and timber production using Landsat 8–9, Sentinel-2, and PlanetScope data. By combining these five ESs variables, we calculated a summed-ESs variable to capture

Accurate estimation of impervious surface area (ISA) at the subpixel level is essential for understanding urbanization and its environmental impacts. In recent years, point-of-interest (POI) data has demonstrated unique value for urban studies. However, its potential for improving subpixel ISA estimation has yet to be fully realized. This research seeks to overcome the challenges of fusing POI data

Mangroves are critical for climate mitigation and biodiversity conservation, yet their spatiotemporal dynamics and physiological responses to hydrometeorological drivers remain poorly understood. This study extracted three essential biodiversity variables (area distribution, phenology, and physiological traits) and further revealed their dependencies on hydrometeorological conditions. We developed

Precision management of soil organic carbon (SOC) is crucial for regulating the global carbon cycle and ensuring food security. Currently, SOC prediction remains challenging due to unresolved moisture disturbances, underutilized multimodal remote sensing data, and uncertain model transferability. To address these challenges, a new paradigm integrating moisture elimination with advanced hybrid deep

Satellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving global aerosol optical depth (AOD) which named the Aerosol domain-Adaptive Network (AAdaN). The framework

Up-to-date mapping of built-up areas is of paramount importance for urban planning, environmental monitoring, and disaster management. In recent years, there has been a growing interest in employing supervised machine learning and deep learning methods to map built-up areas using satellite SAR and optical data. However, the laborious and expensive task of gathering and maintaining a vast array of diverse

Accurate snow cover parameter assessment and mapping at a fine resolution can have profound implications for our understanding of the planet’s water balance and energy dynamics. Passive microwave (PMW) remote sensing is among the most effective methods for snow depth (SD) retrieval. However, significant uncertainties persist in SD retrieval and mapping due to snow characteristic variations, forest

Spatial association reveals the interconnected nature of geographical phenomena, describing the interactions and influences of environmental variables across geographic space. Path analysis can explore complex causal relationships between variables by analyzing path coefficients. However, in large-scale studies, path analysis methods are often affected by local effects, which can influence the accuracy

Terrace agriculture plays a vital role in mountainous regions by preventing soil erosion, optimizing land use, and supporting local ecosystems. However, research on the global distribution of terraces is limited due to the lack of unified automatic identification model. Despite the rapid advancements in deep-learning architectures in recent years, their performance in extracting terrace maps still

Knowledge graphs (KGs) can describe the nature and relationships of geographic entities and are an essential knowledge base for realizing geospatial digital twins (GDTs). However, existing KGs make it challenging to describe dynamic geographic entities under geographic spatiotemporal evolution accurately. Furthermore, they are constrained by the professional backgrounds of their users, which hinders

Gross primary productivity (GPP) plays a crucial role in the carbon exchange between the atmosphere and terrestrial ecosystems. Eddy covariance (EC) method can obtain accurate GPP at the site level, but the sparse distribution limits representativeness. Satellite solar-induced chlorophyll fluorescence (SIF) serves as emerging data of large-scale GPP, yet there are still limitations in its conversion

Root zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great significance for agricultural management and drought assessment. Current deep learning-based RZSM prediction

Deep learning based subway passenger flow prediction was widely employed to promote prediction accuracy, which is crucial for subway management and commercial infrastructure planning. However, the existing work ignored the semantic similarity inherent in the subway stations function, which can extract passengers and enhance prediction accuracy. In this work, a Semantic-Augmented Spatio-temporal Graph

High-resolution land cover (LC) data are essential for ecological monitoring and resource management, especially in heterogeneous landscapes containing diverse LC types. With the growing of available LC products, a comprehensive evaluation of their classification accuracy and spatial consistency is important for users’ selection and application. In this study, we compared eight widely used LC products

Timely monitoring of crop conditions is essential for optimizing and assessing agricultural management. Vegetation indices (VIs) derived from remote sensing data can be useful for assessing crop conditions on a large spatial scale. Traditional crop condition assessments compare a VI in the current year to a baseline VI, averaged over multiple years. However, comparing VIs across years by calendar day

Reservoirs play a significant role in the mobilization of water resources in Burkina Faso, contributing to the management and availability of water for various purposes. Operational management of reservoirs requires accurate and timely water level information, which remote sensing can provide cost-effectively and with limited resources. In this study, the surface area of 20 reservoirs is first determined

The wave phenomena in SAR images are able to provide water depth information. SAR ocean images are often characterized by unclear wave texture and strong speckle noise, which will hinder the bathymetric inversion. Denoising and texture enhancement are two strategies to improve image quality. However, noise reduction may blur textures, while texture enhancement may amplify noise. To address this, we

Accurate estimation of evapotranspiration (ET) is essential for effective water resource management, particularly in arid and semi-arid areas. Advancements in remote sensing technology have made ET models indispensable, offering high-resolution spatial and temporal assessments. Contextual models such as the Surface Energy Balance Algorithm for Land (SEBAL) are particularly valuable for ET estimation

Under global warming, the Yellow River Basin (YRB), serving as an ecological barrier and climate-sensitive region in northern China, faces severe challenges such as frequent extreme droughts and floods, as well as intensifying water resource supply–demand conflicts. To systematically assess the evolution of droughts and floods in the YRB, this study utilizes observational data from 137 meteorological

Artisanal and Small-Scale mining (ASM) provides essential livelihoods for many in developing countries but often lacks regulation, leading to environmental issues such as water pollution and deforestation. Timely and accurate mapping of ASM activities is vital for responsible mining that benefits the environment and local communities. Synthetic Aperture Radar (SAR) is crucial for regular ASM monitoring

Buildings are crucial to cities, and tomographic synthetic aperture radar (TomoSAR) is an important tool for monitoring the heights, linear deformations and thermal amplitudes of buildings. However, existing TomoSAR height inversion methods do not fully leverage a priori knowledge, compromising the accuracy of deformation estimation; deep learning-based methods involve the integration of multiple steps

In the processing of Interferometric synthetic aperture radar (InSAR) data, two-dimensional (2-D) phase unwrapping (PU) is crucial for ensuring the quality of InSAR data inversion. Traditional methods, based on the assumption of phase continuity, often struggle with abrupt terrain changes and the influence of severe noise, leading to poor performance or failure. To address these challenges, this paper

Soil moisture products with long-term, high spatial continuity, and high accuracy are essential for meteorological management and hydrological monitoring. Microwave remote sensing retrieval and land surface model simulation are the two primary sources of global-scale soil moisture data, but each has inherent limitations, making it difficult to balance accuracy and spatial coverage. In this paper, to

In recent advancements, deep learning-based methods for change detection have demonstrated rapid capabilities to identify alterations across extensive regions, underscoring significant research and application potential in remote sensing change detection. Nonetheless, these methods currently encounter limitations in feature extraction, often leading to blurred edges and challenges in identifying small-scale

Up-to-date cadastral maps are vital to local governments in administrating real estate in cities. With its growing availability, remote sensing imagery is the cost-effective data for updating semantic contents on cadastral maps. In this study, we address the problem of updating buildings on cadastral maps, as city renewal is mainly characterized by new construction and demolition. While previous works

Iron tailings are crystalline powders predominantly composed of iron (Fe) and silicon dioxide (SiO2). Spatially characterizing the physical and chemical properties of iron tailings is greatly important for optimal utilization and proper disposal of tailings. Visible-near infrared-shortwave infrared (VIS-NIR-SWIR; 350–2500 nm) spectroscopy offers a rapid, non-destructive, and cost-effective method for

Efficient and accurate extraction of road networks from high-resolution satellite images is essential for urban planning, construction, and traffic management. Recently, various road datasets and advances in deep learning models have greatly enhanced road extraction techniques. However, challenges remain when trying to apply existing research to rural areas. Specifically, most public road datasets

Accurately extracting forest land and understanding its spatial distribution are crucial for forest monitoring and management. However, variations in tree species, human activities, and natural disturbances create diverse and distinct forest land characteristics in remote sensing images, posing challenges for precise forest land extraction. To address these challenges, we propose a spatial-semantic

Recent studies have demonstrated the potential of light detection and ranging (LiDAR) from uncrewed aerial vehicles (UAVs) for assessing forest structures. Maximizing data completeness and representativeness is essential to accurately retrieve key structural parameters. However, knowledge on how data acquisition approaches affect canopy volume exploration is sparse. This study investigated the effects

Fall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified

Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification. However, classical CNN-based methods primarily concentrate on information within a fixed-size neighborhood and a standard square region, neglecting long-range and global information. As non-Euclidean data, the topological structure enables flexible construction

The movement of a vessel generates cold and warm wake patterns with temperature gradients on the sea surface, which provide detection possibilities for satellite-based infrared detection systems. This work analyzes the temporal characteristics of ship wake dissipation on the sea surface, based on multi-source data from low Earth orbit satellite thermal infrared imaging, Automatic Identification System

Accurate and effective coastal wetland classification using hyperspectral remote sensing technology is crucial for their conservation, restoration, and sustainable development. However, the large scale variance of land covers in complex wetland scenes poses challenges for existing methods and leads to misclassifications. Additionally, existing methods encounter difficulties in practical wetland classification

The normalized difference vegetation index (NDVI) is a vital metric for assessing vegetation growth, yet accurate prediction remains challenging, particularly in regions with complex geographic and climatic conditions. Machine learning methods offer promise but are often hindered by sensitivity to model structure, input parameters, and training samples. To address these limitations, this study developed

High-resolution maps of climate and ecosystem variables are essential for supporting terrestrial carbon stocks and fluxes estimation, climate change mitigation, and ecosystem degradation assessment. These maps are usually created using remotely sensed data obtained from various types of imagery and sensors. The remote sensing data typically serve as covariates to deliver spatially explicit information

Forest disturbance regimes are changing across the globe under the influence of climate change and other global change factors, with potentially substantial consequences for tree mortality rates. Tree mortality has been assessed using field and aerial surveys and, more recently, frequently using satellite remote sensing-based techniques. Rapid detection of tree mortality is often important in decision-making

Recent advances in deep learning have enhanced crop classification, yet current research still underutilizes the hierarchical information of crop types, limiting classification accuracy. As categories are subdivided, the sample imbalance intensifies, posing a challenge to fine classification of crops. To address this, we propose the Class Semantic Guided Hierarchical Segmentation Framework (SemHi framework)

Interactive segmentation of remote sensing images enables the rapid generation of annotated samples, providing training samples for deep learning algorithms and facilitating high-quality extraction and classification for remote sensing objects. However, existing interactive segmentation methods, such as SAM, are primarily designed for natural images and show inefficiencies when applied to remote sensing

Unsupervised Domain Adaptation (UDA) has emerged as a powerful technique for addressing the distribution shift across various Remote Sensing (RS) applications. However, most UDA approaches require access to source data, which may be infeasible due to data privacy or transmission constraints. Source-free Domain Adaptation addresses the absence of source data but usually demands a large amount of target

The artificial intelligence (AI) weather forecast foundation models can infer and generate precise global atmospheric state forecasts on the user’s device and with speed over 10,000 times faster than the operational Integrated Forecasting System (IFS), and it is making increasingly significant contributions to geodetic applications represented by the Global Navigation Satellite System (GNSS). However

High-accuracy digital elevation models (DEMs) are essential for remote sensing and geospatial analysis, yet integrating multi-source data over large and complex terrains remains challenging. To address these challenges, this study presents the Multi-source Data Joint Processing (MDJP) framework. This framework establishes a systematic way for correcting DEM errors of varying quality and integrating

The imaging features of oil spills in synthetic aperture radar (SAR) images have significant differences due to factors such as marine environment, SAR sensors, oil film thickness and types, which makes it difficult to obtain a generalized model, and the limited number of SAR images obtained from new oil spill events hampers the effective training of deep learning networks. To solve these issues, an

Wildfires significantly reshape the landscape of the Mediterranean basin, altering forest composition, structure, and diversity. Consequently, detailed fuel mapping is crucial for improving fire risk assessment and enhancing fire behavior modeling, as wildfires typically ignite from surface fuels and may spread vertically to canopy fuels due to canopy fuel continuity. This study generates a fuel type

Considering the importance of greenspace for the health and life of urban citizens, different levels of greenspace exposure (GE) have received increasing attention. However, the understanding of human travel-related greenspace exposure is still limited, especially the lack of quantitative description of the fine-grained dynamics of greenspace exposure for active travel. Therefore, this study aims to

Under anthropogenic disturbances and global warming, coral reef ecosystems are degrading, and there is growing concern about the changes in benthic habitats in shallow coral reef waters. As an essential parameter, bottom reflectance can be used to indicate the health of benthic habitats in coral reefs. However, accurately determining bottom reflectance from satellite data remains challenging. This

Land Surface Temperature (LST) is a critical parameter for climate studies and land surface process models as it indicates ground surface temperature variations across landscapes and timescales. However, satellite-based LST products derived from infrared sensors suffer from substantial missing values due to extensive cloud covers on the Earth’s surface. Traditional methods rely heavily on numerical

The automated extraction of building facade footprints (BFFs) is a critical task in surveying and remote sensing. Existing methods primarily use mobile laser scanning point cloud as the data source, with limited methods utilizing airborne laser scanning (ALS) data. This is mainly because current methods require explicit building extraction and wall detection, and the facade points in ALS point clouds