Remote sensing is an essential tool for monitoring the extent and biophysical attributes of vegetation. Multi-sensor approaches, that can reduce the costs of developing high-quality datasets and improve predictive performance, are increasingly common. Despite this trend, the advantages of these data-fusion techniques are rarely reported beyond statistical performance. We use airborne lidar-derived

Building height serves as fundamental information for characterizing urban landscapes and morphology, as influencing various aspects of the urban environment. While traditional methods of obtaining building height are often limited by spatial coverage and proprietary constraints, remote sensing data provides an alternative for indirect estimation. Several height products developed across different

Vicarious calibration/validation (Cal/Val) is essential for ensuring the radiometric accuracy of spaceborne thermal infrared (TIR) sensors. Anticipating an increase in the number of TIR sensors in the near future, this study developed an automated vicarious radiometric validation method based on in situ water temperature measurements from 14 sites in lakes and bays across Japan, which are not specifically

Straw burning poses significant threats to local air quality and nearby public health by emitting harmful pollutants during specific seasons. Traditional satellite-based remote sensing techniques encounter difficulties in monitoring small-scale straw-burning events due to long revisit intervals and low spatial resolution. To address this challenge, unmanned aerial vehicles (UAVs) equipped with imaging

Accurate spatial distribution information of field features is critical for enabling autonomous agricultural machinery navigation. However, current perception systems exhibit limited segmentation performance in complex farm environments due to illumination variations and mutual occlusion among various regions. This paper proposes a low-cost UAV photogrammetry framework for centimeter-level 3D semantic

The rapid expansion of rubber plantations has led to significant ecological impacts, including deforestation and reduced carbon storage capacity, such as rubber-induced 4.1 million ha of forest loss in Southeast Asia (SEA). Remote sensing, essential for monitoring rubber expansion and supporting initiatives like REDD+ and EU deforestation regulations. However, due to the heterogeneity, dynamism, and

The physical dimension of cities and its spatial patterns play a crucial role in shaping society and urban dynamics. Understanding the complexity of urban systems requires a detailed assessment of their physical structure. Urban geography has long focused on framing typologies to represent common patterns in the urban fabric using various methodologies. However, only recent advancements in computational

Spatial and temporal data gaps present a significant challenge to high-frequency surface water mapping using satellite imagery. Utilizing observations from temporally close periods and multi-modal sensors for gap-filling is of critical importance. However, discontinuous pixel values inherent to conventional water maps hinder the application of deep learning methods, which are effective and popular

The increasing complexity and scale of remote sensing datasets, coupled with the challenges of accurately estimating algorithmic time efficiency, often lead to significant resource waste or even failure when using machine learning algorithms in urgent or resource-constrained scenarios. Accurate time efficiency estimation is critical for deploying effective algorithms, yet it remains challenging due

Identifying urban villages (UVs) is essential for supporting urban renewal and governance, as these informal areas present significant challenges to urban sustainability. Existing multi-modal deep-learning studies have accurately identified UVs in individual cities; however, their scalability is constrained by substantial computational demands and data availability. Currently, efficient deep-learning

Accurate building damage assessment is crucial for post-disaster response, yet existing methods struggle to capture complex spatial relationships and contextual features needed for distinguishing damage levels. To address this, we propose the Gated Adaptive Multi-scale Spatial-frequency Fusion Network (GAMSF), a two-phase framework for building localization and damage classification. GAMSF integrates

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop’s functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a

Upright maize straw left in the field during autumn and winter significantly contributes to severe air pollution in agricultural ecosystems due to burning. It is essential to obtain the spatial distribution of upright maize straw quickly and accurately for effective management and environmental protection. However, identifying upright maize straw using remote sensing is difficult because its spectral

Understanding the spatial and temporal distribution of irrigated cropland at the field scale is essential for managing irrigation water use and addressing the water-food nexus. While global and regional irrigation products exist, they often classify irrigated crops based on machine learning principles, where irrigated crops outperform rainfed ones. However, these methods typically lack mechanistic

Phase filtering is crucial for ensuring accurate phase unwrapping in the processing of Interferometric Synthetic Aperture Radar (InSAR) data. Traditional filtering methods often struggle to effectively suppress noise and accurately preserve phase edge information when processing InSAR interferometric phases with complex noise and large gradient deformations. In response to this challenge, we have proposed

Canadian prairies areamong the world’s most endangered ecosystems, and the identification of native grasslands is crucial for supporting grassland management and wildlife conservation. However, the current amount, distribution, and dynamic changes of native grassland remain uncertain, partly due to the difficulty of separating native grassland with other land cover types, especially tame grassland

Slow-moving landslides pose a substantial threat to communities and infrastructure, with annual creeping distances ranging from a few mm to hundreds of meters. To protect local communities from the landslide motion, landslide displacement retrieving or monitoring is necessary. However, traditional field investigations are time- and labor-consuming, which may limit the understanding of the landslide

Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. To enhance AGB estimation accuracy, we integrated unmanned aerial vehicle

Urban agglomerations, formed by multiple cities connected through transportation and information networks, play a pivotal role in the sustainable development of human settlements and communities. This study introduces a data-driven approach to analyze urban connectivity using high-precision 10-meter panchromatic nighttime light (NTL) data from SDGSAT-1, integrated with OpenStreetMap (OSM) road data

With the surge in cultural heritage tourism, personalized tourism route planning has emerged as a crucial strategy to enrich tourists’ experiences. Our objective is to establish a personalized tourism route recommendation and evaluation system. This system will leverage the Analytic Hierarchy Process (AHP) and Delphi method to assess the factors influencing the travel decisions of city-life lovers

Ground surface displacements monitored by interferometric synthetic aperture radar (InSAR) techniques are a useful indicator for revealing precursors and assessing risks of tailings dam failures. However, the long-term and large-magnitude consolidation settlements, which impose low risks on dam collapses, usually dominate InSAR displacement observations. The phenomenon would cause misleading in failure

The integration of machine learning (ML) algorithms with remote sensing (RS) data offers significant potential for enhancing dynamic forest characterization and change detection through high temporal density mapping. This study evaluates the effectiveness of RS-ML models for forest mapping in parts of southern Nigeria, with particular emphasis on assessing their temporal scalability using multi-sensor

Accurately monitoring impervious surface (IS) changes is vital for assessing the effects of urbanization on human activities and its broader environmental, economic, and social sustainability. However, optical building shadows pose significant challenges for IS extraction in high-density urban core areas. Even though combining optical and synthetic aperture radar (SAR) imagery makes it easier to identify

Coal fires present significant global environmental and energy challenges, posing substantial barriers to achieving carbon-neutral goals. Thermal Infrared Remote Sensing (TIRS) technology, which is used to retrieve land surface temperatures, plays a crucial role in detecting coal fires. However, its accuracy suffers from solar radiation interference. In addition, there is limited research focused specifically

Frequent cloud cover leads to gaps in remote sensing image time series, posing significant challenges for agriculture, grassland, and forest monitoring applications. This study proposes an innovative method to generate reflectance time series to address this issue, enhancing data reliability and application scope. We adopted a lightweight reconstruction model based on the architecture of deep learning

This study explores the potential of spatially explicit Harvester Production Reports (HPRs) for automatic annotation of Aerial Laser Scanning (ALS) data at tree-level, enabling accurate tree species classification using Convolutional Neural Networks (CNNs). By integrating HPRs into the modelling process, this approach provides a practical solution for addressing challenges in remote sensing data annotation

Freezing and thawing (FT) processes in the soil active layer play a critical role in high-latitude ecosystems, influencing carbon cycling, hydrology, and infrastructure stability. Accurate prediction of FT states is essential for assessing permafrost dynamics under a changing climate. This study introduces a comprehensive framework for FT state prediction that leverages MERRA-2 reanalysis climate data

Land subsidence is a significant issue in many coastal megacities, including Shanghai, where it poses risks to infrastructure and economic stability. Although numerous studies have used SAR datasets to monitor land subsidence in Shanghai, multi-decadal displacement measurements obtained from multi-sensor SAR data remain unavailable. Moreover, the contributions and variations of driving factors behind

Accurate identification of subsidence funnels is essential for assessing surface deformation in mining areas, preventing disasters, and optimizing resource management. However, recognizing subsidence funnels of varying sizes in large-scale interferograms poses significant challenges, particularly for small-sized funnels. Their indistinct features and susceptibility to background noise interference

Landslides are one of the major geohazards causing significant economic damage and loss of life, with impacts expected to increase due to climate change. Landslide inventory maps (LIMs) are essential for risk management and reduction, but they usually remain an overlooked issue, especially over very large areas, i.e. at a regional or national level. Nowadays, extensive interferometric satellite radar

High-resolution urban air temperatures are indispensable for analysing excess mortality during heatwaves. As a crucial method for obtaining high-resolution data, multi-source data integration has been widely used in urban temperature estimations. However, current research predominantly focuses solely on integrating official weather station observations, satellite products, and reanalysis datasets.

With regard to climate change and population growth, irrigated croplands need to be accurately delineated for sustainable water resource management. Owing to the lack of extensive training samples and the limitations of coarse spatiotemporal resolution data in complex agricultural regions, China’s irrigated croplands are difficult to map with a unified spatiotemporal framework. This study presents

The aboveground dry biomass (AGDB), leaf area index (LAI), chlorophyll density (CHD), and plant water content (PWC) are important growth physiological parameters that can reflect the growth status of winter wheat. Monitoring the growth physiological parameters of winter wheat by constructing vegetation index is a common method that can quickly obtain the growth physiological parameters of winter wheat

Wildfires, a natural phenomenon predating human civilization, present severe threats to ecosystems, socio-economic factors, and human health. Due to climate change and human influence, the frequency and intensity of global wildfires are on the rise, which emit gigantic amounts of emissions into the atmosphere and compound the world’s efforts to tackle global warming. This study introduces and summarizes

Amid climate change and urban expansion, urban floods (UFs) have become a significant concern. Effective UF management requires two key elements: (1) assessing susceptibility to identify flood-prone hotspots and (2) analyzing factor contributions to pinpoint primary drivers. While research on urban flood susceptibility (UFS) is well-established, studies on factor contributions usually limited to the

3D Transformers have achieved great success in point cloud understanding and representation. However, there is still considerable scope for further development in effective and efficient Transformers for large-scale LiDAR point cloud scene segmentation. This paper proposes a novel 3D Transformer framework, named 3D Learnable Supertoken Transformer (3DLST). The key contributions are summarized as follows

Population analysis units (PAUs), as fundamental spatial units accommodating population-related activities, hold significance in constructing spatiotemporal interaction networks to understand intra-unit population distribution and activity patterns as well as inter-unit interactions. However, existing networks are constrained by fixed scales and the absence of temporal dynamics, with insufficient consideration

Soil Organic Carbon (SOC) is a key property for soil health. Spectral reflectance such as multispectral and hyperspectral data could provide efficient and cost-effective retrieval of SOC content. However, constrained by the availability of hyperspectral satellite data, current works mostly use a small number of spaceborne hyperspectral imagery for SOC retrieval on a small scale. In this work, the first

Irrigated dryland in China, largely distributed and expanding in arid and semi-arid regions, always poses challenges to water resource and ecosystems, thus deeply impacts the implementation of SDG 2, 6, 15 and so on. Spatial-temporal patterns of irrigated dryland with high resolution help pinpoint hotspots and key issues worth to concern, however, are still lacking in research. This study developed

Satellite platforms have become the principal data source for large-scale rice mapping. Numerous studies investigated the advantages of integrating deep learning models with satellite data for rice mapping, but these studies are typically confined to specific regions and the quality of customized datasets varies. The absence of a global, standardized satellite dataset for rice mapping benchmarking

Glaciers in the European Alps are undergoing significant retreat and thickness decrease due to climate change, contributing to sea-level rise and impacting the mountain environment and its biodiversity, tourism and local economy. Unmanned Aerial Vehicle (UAV) photogrammetry offers cost-effective methods for high-resolution glacier monitoring. However, multi-temporal UAV-based data usually must be improved

Managing water resources in alpine, cold regions is increasingly complicated by climate change, which alters streamflow patterns and poses significant challenges to resource sustainability. This study addresses the critical issue of understanding how distinct hydrological watersheds respond to these climatic shifts. By examining four watersheds (BRW, the Burqin River Watershed; MRW, the Manas River

Accurate geospatial extent data of photovoltaic (PV) power plants is essential for assessing their socioeconomic benefits and environmental impacts. However, existing semantic segmentation models often result in over-smoothed edges and the inaccurate delineation of PV power plants. To address these limitations, we proposed a novel edge-enhanced Segment Anything Model (ESAM) tailored for PV power plant

Accurately estimating evapotranspiration (ET), gross primary productivity (GPP), and winter wheat yield (WWY) is of great significance for climate change research, agricultural development, and water resources management in the cultivation regions of winter wheat. In this study, the coupling strategy of the Noah-MP land surface model and the multi-pass land data assimilation scheme (MLDAS) has been

Water clarity is an important characteristic of water quality and ecosystem health in inland and coastal waters. Secchi disk depth (SDD) is a conventional in situ optical method of measuring this parameter. Assessments of SDD from remotely-sensed ocean color data benefit from wide spatial and temporal coverage but are subject to low accuracy resulting from unstable relationship between water transparency

Soil salinization is one of the main causes of low yield in cotton fields, leading to soil compaction and significantly affecting crop growth and nutrient absorption. There is an urgent need for rapid, non-destructive, and efficient methods to extract soil salinity spatial distribution information under cover conditions. These methods are essential for optimizing irrigation techniques, controlling

Solar-induced chlorophyll fluorescence (SIF) from hyperspectral imaging is strongly associated with agricultural indices, particularly leaf chlorophyll content (LCC), in crop phenotyping. However, confounding factors such as spectral resolution (SR), canopy structure, and illumination reduce SIF sensitivity and complicate its association with these indices. This study focused on examining the effect

This paper describes a novel approach that uses Synthetic Aperture Radar (SAR) satellite imagery to identify river sections with persistent, stable, high-flow water. Such sections represent candidate sites for the generation of green electricity through the deployment of hydrokinetic turbines. Canada-wide mapping of potential hydrokinetic turbine sites requires Earth Observation satellite technology

Droughts are hydrometeorological extreme events influenced by highly intricate land–atmosphere feedback mechanisms and climate variability. Deep learning models have recently succeeded in detecting extreme climate events and promise to uncover and understand droughts further. There are two main challenges of reliability and trustworthiness limiting their applications: miscalibration and inherent uncertainty

In recent years, research on synthetic aperture radar (SAR) target detection based on deep learning methods has made substantial progress in model accuracy. However, there are still limitations related to model size and computational complexity, which makes it challenging to deploy the model on edge devices, limiting its practical application. To this end, this paper proposes an automatic pruning method

Droughts are some of the most devastating natural extreme events in sub-Saharan with enormous economic impacts on agrarian economies such as Cameroon. However, knowledge and information on their dynamics and drivers in the five agroecological zones (AEZs) of Cameroon is very scanty leading to poor planning and management. Therefore, this study aimed to characterize agricultural droughts based on soil

In the background of global climate change, the trend of atmospheric pollution of the Tibetan Plateau region (TP), where ecosystem is susceptible to their synergistic effects, requires prompt attention. Nitrogen dioxide (NO2), as an important air pollutant of the TP with a profound social and ecological effect, lacks sufficient investigations. Current assessments on NO2 greatly rely on machine-learning

Assimilating crop biophysical traits (e.g., Leaf area index, LAI) derived from remote sensing data into a crop growth model provides an effective way for monitoring spatiotemporal variability of crop biomass and yield. However, traditional complex crop growth models generally require extensive input parameters and computation resources, limiting their applicability for large-area estimation using earth

The ongoing war in Ukraine has significantly disrupted agricultural land use, leading to reduced cropland areas, increased land abandonment, and heightened uncertainty in food production. This study presents a multi-year assessment of war-induced agricultural land use changes in Ukraine using machine learning-based classification applied to Sentinel-1 and Sentinel-2 satellite imagery. By leveraging

Nephrite, as a rare jewelry with a long history, holds great economic value and cultural significance. The Eastern Kunlun Range possesses immense potential for nephrite resources controlled by tectonic evolution and magmatic activities during geological periods. However, the harsh environment and backward prospecting technology have led to stagnation of the nephrite exploration in this area. Remote

Accurate crop yield estimation enables informed decisions that support efficient and sustainable food production systems. Despite some success in crop yield estimation using deep learning models, they are often referred to as “black boxes” due to their lack of interpretability. Meanwhile, most current models are designed to provide yield estimations without assessing the uncertainty and verifying the

Landsat and Sentinel-2 satellites offer high-resolution multispectral imagery widely used for applications such as vegetation phenology assessment, soil dynamics monitoring, and crop yield estimation. However, optical remote sensing data often encounter information gaps due to satellite orbital cycles and adverse atmospheric conditions, which significantly reduce image availability and complicate accurate

Style transfer has emerged as a prominent technique for transferring stylistic elements between images (e.g., a reference image and a map). However, current methods face two challenges when applied to create image maps, especially when the reference image and map are not spatially aligned (e.g., covering different regions). These challenges include aligning the semantic elements between maps and remote

Tunnel mapping systems are essential for tunnel inspection, integrating sensors like LiDAR, cameras, and odometers to enhance data accuracy. However, calibration is challenging due to mechanical constraints and repetitive sensor installations, especially for LiDAR-Camera alignment. Existing methods struggle in tunnels with poor lighting and low texture, and they fail to address irregular vibrations