Accurate and detailed spatial–temporal soil information is crucial for soil quality assessment worldwide, particularly in the countries with large populations and extensive agricultural areas. Using remote sensing technology to generate bare soil reflectance composites has been shown as a prerequisite for effectively modeling soil properties. However, most bare soil extraction methods rely on the single-period

Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which further limits ocean research at the global scale. Here, we divide the global mid-ocean ridge system into

Pine Wilt Disease (PWD) is a forest infectious disease that inflicts substantial economic losses to China’s forestry. Its rapid spread and the significant challenges associated with its control make early detection of infected trees crucial for disaster prevention. Unmanned aerial systems (UASs) hyperspectral imaging (HSI) and light detection and ranging (LiDAR) technologies provide high-resolution

Information on the sowing areas and yields of crops is important for ensuring food security and reforming the agricultural modernization process, while crop classification and identification are core issues when attempting to acquire information on crop planting areas and yields. Obtaining information on crop planting areas and yields in a timely and accurate manner is highly important for optimizing

As the network structures continue to innovate and evolve, significant achievements have been achieved in hyperspectral image super-resolution tasks. However, how to further explore the spectral domain potential from prior knowledge and channel-enhanced structures to achieve better performance has inspired the following two works: Firstly, to systematically compare prior knowledge of spectral with

Improving the accuracy of nearshore bathymetric measurements is essential for understanding coastal environments, resource management, and navigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the first laser satellite that uses the photon-counting technique. The ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which enables higher-accuracy measurements

Urban green spaces (UGS) provide ecological and habitat benefits such as carbon sequestration, oxygen production, humidity increase, noise reduction, and pollution absorption. UGS maps derived from remote sensing images serve as the fundamental data for urban planning and carbon sequestration assessments. However, the spatial resolution of remote sensing image and the pattern of urban structures significantly

Accurate and continuous PM2.5 data is essential for effective prevention of PM2.5 pollution. Despite the achievements of deep learning methods in estimating PM2.5 concentrations, existing neural network models have relied too much on the self-learning capability and have ignored geographic patterns of PM2.5. Few have taken a geographic perspective when modeling PM2.5, resulting in lower model interpretability

Spectral reconstruction technology extracts rich detail information from limited spectral bands, thereby enhancing both of the image quality and the resolution capabilities. It finds application in non-destructive testing, elevating the precision and robustness of detection. Current studies primarily focus on improving the local information perception of convolutional neural networks or modeling long-distance

The European Space Agency (ESA) under the Climate Change Initiative (CCI) has developed a multi-satellite global, daily Soil Moisture (SM) dataset that has paved the ways for agricultural drought studies. To evaluate the performance of this ESACCI SM, two SM-based indices i.e. parametric distribution-based Standardized Soil Moisture Index (SSMI) and non-parametric distribution-based Empirical Standardized

To attain sustainable development goals and understand urban growth patterns, continuous and precise monitoring of built-up area heights is essential. This helps reveal how urban form evolution impacts the thermal environment. Previous research often used isolated images, ignoring the temporal dimension of thermal infrared and reflectance data from Landsat sensors. Additionally, cost-effective and

The process of crop type mapping generates land use maps, which serve as critical tools for efficient evaluation of production factors and impacts of agricultural practice. Yet, despite the necessity for comprehensive solutions in space and time, the state of research still exhibits significant limitations in these two dimensions: (1) From a temporal perspective, the primary focus of past research

The accurate estimation of Above Ground Biomass (AGB) is the basis for plantation forest carbon trading. This study focused on Picea crassifolia artificial plantations, extracting individual tree crown diameters and heights using Unmanned Aerial Vehicles (UAV) data and calculating the individual tree biomass using allometric growth equations. These results were then used to train a satellite image

Well-facilitated farmland (WFF) construction is greatly responsible for agricultural sustainable development. How to quantitatively plan the WFF construction distribution and schedule is still challenging. This study thus introduced a simple but robust method, and took the typical dryland Yulin city to spatially identify its potential WFF construction areas and temporally determine construction priorities

Understanding spatial interactions in urban environments has become critical in the context of spatio-temporal big data. However, Spatial–temporal big data often exhibit non-uniformity, necessitating the imputation of spatial interaction relationships derived from the analysis of such data. Previous studies often used simplified grid-based or TAZ approaches that ignore the complex interactions for

Mapping water bodies from remotely sensed imagery is crucial for understanding hydrological and biogeochemical processes. The identification of water extent is mainly dependent on optical and synthetic aperture radar (SAR) images. However, the use of remote sensing for water body mapping is often undermined by the mixed pixel dilemma inherent to traditional hard classification approaches. At the same

The Qinghai-Xizang Plateau, hosting the world’s largest alpine pastures, plays a pivotal environmental role in Asia. These ecosystems face alterations driven by both climate change and anthropogenic activities, resulting in the widespread consideration of grassland degradation. From 2000 to 2020, the northern Xizang Plateau experienced a pronounced escalation in drought conditions, particularly following

Land surface emissivity (LSE) is a crucial variable in thermal infrared (TIR) remote sensing, providing unique information about the land surface across different channels. It is essential for applications such as surface energy budget estimation, resource exploration, and land cover change monitoring. However, current methods for retrieving LSE have certain limitations in terms of applicability or

Automatic 3D reconstruction from spaceborne charge-coupled device (CCD) optical imagery is still a challenge as the rational functional model (RFM) based reconstruction pipeline failed to amount to the advances of pinhole based approaches in computer vision and photogrammetry. As a consequence, the accuracy and completeness of the reconstructed surface by RFM based pipeline improved slightly recent

Detecting soil arsenic (As) contamination is crucial for designing efficient soil remediation strategies; however, traditional laboratory-based As detection techniques are time- and labour-intensive and are unsuitable for large-scale spatial analyses. To address this issue, we combined machine learning (ML) with visible-near-infrared (vis-NIR) spectroscopy to develop an efficient framework for As detection

Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) are key health indicators of the coastal ocean and seas. The former is linked to primary productivity, while the latter is associated with water quality; both are influenced by change in climate. Recent studies have highlighted a declining trend in Chl-a levels along the Mediterranean coastal region. River discharge plays an important role in regulating

High-quality land-use/land-cover mapping with optical remote sensing images yet presents significant work. Even though fully convolutional semantic segmentation models have recently contributed to popular solutions, the lack of annotation data may lead to severe degradations in their inference performance. Besides, the category confusion in high-resolution representations will further exacerbate the

Extraction of built-up areas from polarimetric synthetic aperture radar (PolSAR) images plays a crucial role in disaster management. The polarimetric orientation angles (POAs) of built-up areas exhibit diversity, and built-up areas with POA close to 45° are often misclassified as vegetation. To address this problem, a polarimetric feature suitable for the extraction of built-up areas with large POAs

The dynamics of surface water in the Yangtze Plain is complex, influenced by the coupled impacts of climate change and intensifying human activities. However, remote sensing observations often encounter challenges in this region due to persistent cloud cover, impeding comprehensive studies of water dynamics. This study introduces a novel Monthly Surface Water Mapping (MSWM) approach combining time

The determination of river velocity is important for hydromorphological analyses and river monitoring systems. Indirect measurements of river velocity using videos recorded by unmanned aerial vehicles (UAV) allow fast and cost-effective processing of information about the river stretch. This paper presents a method for computing flow velocity of the river surface using deep supervised model RAFT to

Agricultural drought threatens food security and agricultural sustainable development. There have been numerous spectral indices from remote sensing images developed for monitoring crop drought. However, most present spectral indices are focusing on crop growth and Land Surface Temperature (LST), and the crop canopy water content are in less consideration simultaneously. Additionally, the Normalized

Satellite-based field delineation has entered a quasi-operational stage due to recent advances in machine learning for computer vision. Transfer learning allows for the resource-efficient transfer of pre-trained field delineation models across heterogeneous geographies. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck. The key innovation

Accurately estimating fractional cover of vegetated ecosystems over large areas is essential for many scientific studies, including climate change, land cover and land use, etc. Taking both accuracy and large spatial coverage into account, different methods have been proposed, such as upscaling from high to low spatial resolution remote sensing images, and harmonized data from varied sources. In this

The launch of GEDI opens a new era of forest structure monitoring using full-waveform LiDAR from space. Simulation of GEDI waveform is of great importance for the algorithm design and forest structure metric estimation. DART is a universal 3D radiative transfer model for simulating remote sensing signals by modeling light propagation in 3D landscape, with DART-RC adopting forward ray tracing and DART-Lux

Spatially continuous fine particulate matter (PM) mapping with hourly updated is essential for monitoring environmental pollution and promoting public health. The intensive observation of geostationary satellite enables accurate estimation of PM at a fine-scale. However, current estimation models are still limited by their weak transferability and hard to provide a robust hourly PM estimation. In this

China has progressively elevated old city transformation and urban renewal to a policy priority, positioning them as new endogenous drivers of urban development. It raises the demand for real-time insight into the spatiotemporal distribution of old and renewed urban areas. We propose a weakly supervised mapping framework with adaptive adjustments city by city without relying on high-precision training

The Jamuna is a major braided river of Bangladesh and poses enormous challenges related to flooding, erosion, and sedimentation. Effective river management requires an understanding of the Jamuna’s sediment transport dynamics and channel morphology. Yet, developing that understanding is hindered by a lack of topographic data for the river corridor. In this study, the waterline method was applied to

This paper presents a novel approach to tracking ships in Synthetic Aperture Radar (SAR) images based on an improved lightweight YOLOv8 Nano (YOLOv8n), specially devised to improve efficiency without compromising accuracy. In our method, we replaced the heavy backbone and neck of YOLOv8 with HGNetv2 and slim-neck, respectively. We also implemented a lightweight decoupling head using EMSConvP. Additionally

Penguins play an essential biochemical role in the Antarctic ecosystem, being the study of their dynamics of utmost importance to understand their environment, behaviour and populational trends in the current climate change scenario. In this study, we used multi-rotor Unmanned Aerial Vehicles (UAVs) along the coast of the chinstrap penguin () colony of Vapour Col (Deception Island, Antarctica) to map

The successful launch of the Sentinel-1 satellite in 2014 brought a large amount of free SAR images to researchers and scholars, and its application in the fields of ocean monitoring, land use change, natural disaster monitoring and emergency response is becoming increasingly mature and precise. The main applications of InSAR can be categorized into surface deformation monitoring and DEM generation

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional

This study introduces an alternate spatial-temporal modeling-assisted InSAR time-series analysis method for mine subsidence mapping, aiming to address the large deformation gradients and decorrelation issues. The approach employs the iterative Modeling-Assisted Phase Unwrapping (MA-PU) algorithm for spatial phase unwrapping, and integrates it with temporal models to derive the deformation time-series

Assessing the quality of UAV-HSIs (Unmanned aerial vehicle hyperspectral images) is crucial for evaluating sensor performance, identifying distortion types, and measuring data inversion accuracy. Due to the absence of reference images, UAV-HSI quality assessment leans towards no-reference image quality assessment (NR-IQA), offering versatile applications. NR-IQA methods of remote sensing images using

High-resolution Digital Elevation Models (DEMs) are essential for precise geographic analysis. However, obtaining high-resolution DEMs in regions with dense vegetation, complex terrain, or satellite imagery voids presents substantial challenges. This study introduces a deep learning approach using three-dimensional (3D) terrain features combined with Conditional Generative Adversarial Networks (CGANs)

Leaf area index (LAI) is one of key variables for depicting vegetation structures in land ecosystems. Land surface models necessitate uniform LAI inputs at varying spatial scales to ensure accurate outputs at multiscale levels, however, operational satellite LAI products are acquired only at low spatial resolutions, inhibiting their application at finer spatial scales. Spatial downscaling methods are

Salt marshes are pivotal in the global carbon cycle, serving as significant contributors to the blue carbon sink. Accurately estimating carbon stock in salt marshes relies on precise vegetation classification. Here, we developed the Salt Marsh Vegetation Classification Network (SVCN), a deep learning algorithm designed to classify three primary vegetation canopies (S. alterniflora, P. australis, and

With the rapid development of LiDAR and artificial intelligence technologies, 3D point cloud semantic segmentation has become a highlight research topic. This technology is able to significantly enhance the capabilities of building information modeling, navigation and environmental perception. However, current deep learning-based methods primarily rely on voxelization or multi-layer convolution for

Accurate long-term monitoring of wetlands using satellite archives is crucial for effective conservation. While new methods based on temporal profile classification have been useful for long-term monitoring of wetlands, their advantages over traditional classification methods have not yet been demonstrated. This study aimed to compare continuous change detection (using the continuous change detection

Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO algorithms. This paper presents a novel Universal Adversarial Defense approach in Remote Sensing Imagery (UAD-RS), leveraging pre-trained diffusion models

Increasing population and the consequent rise in the demand for food and water resources pose significant challenges for the future of agriculture in Egypt. Rapid large-scale agricultural expansion has occurred in the country to meet the growing demand, but agricultural loss from urban infringement and field abandonment remains prevalent. Documenting the full spectrum of changes within Egypt’s agricultural

China’s coastal mudflats, threatened by artificial reclamation and climate change, are undergoing drastic changes and their accurate mapping is important for their conservation and restoration. Traditional classification methods, which require large samples and complex classifiers, tend to have low computational efficiency and poor generalization ability; thus, they are unsuitable for the rapid and

Rapid and accurate assessment of affected areas is crucial for post-earthquake rescue efforts, as earthquakes can lead to extensive damage and casualties. The post-earthquake damage assessment method based on SAR coherence is widely utilized, but issues such as inadequate consideration of decorrelation factors and underutilization of preseismic coherence can negatively impact assessment outcomes. To

Accurate lane maps with semantics are crucial for various applications, such as high-definition maps (HD Maps), intelligent transportation systems (ITS), and digital twins. Manual annotation of lanes is labor-intensive and costly, prompting researchers to explore automatic lane extraction methods. This paper presents an end-to-end large-scale lane mapping method that considers both lane geometry and

Reconstructing semantic indoor scenes is a challenging task in augmented and virtual reality. The quality of scene reconstruction is limited by the complexity and occlusion of indoor scenes. This is due to the difficulty in estimating the spatial structure of the scene and insufficient learning for object location inference. To address these challenges, we have developed PesRec, an end-to-end multi-task

Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective

Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser scanning (ALS) could accurately measure FCH at the plot-level. Spaceborne lidar system (SLS) allows for global sampling of FCH at the footprint-level

Continuous operating reference station (CORS) networks, underpinned by GNSS technology, support various technological services including comprehensive surveying, navigation, and remote sensing. The stability of these CORS networks is crucial for maintaining the high accuracy and reliability of data services. In this paper, we focus on analyzing the GPS deformation array of Vancouver Island, Canada

The hierarchical structure of geographic or urban space can be well-characterized by the concept of living structure, a term coined by Christopher Alexander. All spaces, regardless of their size, possess certain degrees of livingness that can be mathematically quantified. While previous studies have successfully quantified the livingness of small spaces such as images or artworks, the livingness of

The North Pacific Ocean plays a pivotal role as a carbon sink within the global carbon cycle. However, a comprehensive understanding of the spatiotemporal dynamics of carbon dioxide concentration and its determinants in this domain remains elusive due to its vast dimensions and the intricacies of influencing factors, with previous research on carbon dioxide partial pressure in the North Pacific Ocean

Intensifying hydroclimatic changes in North Africa are causing unprecedented floods, droughts, and land degradation patterns that are increasingly associated with human casualties, socioeconomic instabilities, and outflow migrations. These patterns’ and their future forecasts remain largely unquantified, aggravating the impacts on several populous areas. To address this deficiency, we employ pixel-based

High spatial resolution XCO2 data is key to investigating the mechanisms of carbon sources and sinks. However, current carbon satellites have a narrow swath and uneven observation points, making it difficult to obtain seamless and full-coverage data. We propose a novel method combining extreme gradient boosting (XGBoost) with particle swarm optimization (PSO) to construct the relationship between OCO-2

Extracting buildings from True Digital Ortho Maps often encounters occlusions and misidentifications, making it challenging to obtain complete, regular, and accurate building contours. To address this issue, we developed a building recognition process based on the Segment Anything Model, and proposed a novel regularization algorithm for building contour inference and fitting, which quantifies the confidence

Geo-text data, which combine geographical locations with textual information (e.g., geo-tagged tweets), are typically visualized using tag maps. Since tags are rich in attribute information, tag maps are an intuitive method of visualizing how attribute domains carried by tags vary across space. However, users may be interested not only in the overall spatial distribution of tags but also in exploring