Evapotranspiration (ET) is a crucial component of both the water cycle and energy balance, with vegetation being a key factor influencing ET. Vegetation impacts ET primarily through two modes: vegetation growth change (VGC) and vegetation type conversion (VTC). Despite the different mechanisms by which VGC and VTC influence ET, previous studies have rarely differentiated their individual effects. This

The 3D ionosphere structure is of interest in many fields such as radio frequency communication and global navigation satellite system (GNSS) applications. However, the limited temporal and spatial coverage of measurements poses a challenge for 3D electron density modeling. To overcome this challenge, we explore the use of kriging interpolation technique. The kriging interpolation is performed to obtain

The spherical radial basis function (SRBF) approach, widely used in gravity modeling, is theoretically surveyed from a viewpoint of random field theory. Let the gravity potential be a random field which is represented as an integral functional of another random field, namely an isotropic Gaussian random field (IGRF) on a sphere inside the Bjerhammar sphere with the SRBF as the integral kernel. When

Two-view correspondence learning plays a pivotal role in the field of computer vision. However, this task is beset with great challenges stemming from the significant imbalance between true and false correspondences. Recent approaches have started leveraging the inherent filtering properties of convolution to eliminate false matches. Nevertheless, these methods tend to apply convolution in an ad hoc

In recent years, coseismic velocity from high-rate global navigation satellite systems (GNSS) carrier phase data has been widely utilized to estimate instrumental seismic intensity, thereby guiding earthquake early warning and emergency response. However, using carrier phase data only yields displacement, displacement increment, and average velocity but not instantaneous velocity at the epoch level

Efficient landslide mapping is crucial for disaster mitigation and relief. Recently, deep learning methods have shown promising results in landslide mapping using satellite imagery. However, the sample sparsity and geographic diversity of landslides have challenged the transferability of deep learning models. In this paper, we proposed a universal adapter module that can be seamlessly embedded into

The use of synthetic aperture radar (SAR) has greatly improved our ability to capture high-resolution terrestrial images under various weather conditions. However, SAR imagery is affected by speckle noise, which distorts image details and hampers subsequent applications. Recent forays into supervised deep learning-based denoising methods, like MRDDANet and SAR-CAM, offer a promising avenue for SAR

Satellite-derived bathymetry (SDB) is a vital technique for the rapid and cost-effective measurement of shallow underwater terrain. However, it faces challenges of image noise, including clouds, bubble clouds, and sun glint. Consequently, the acquisition of no missing and accurate bathymetric maps is frequently challenging, particularly in cloudy, rainy, and large-scale regions. In this study, we propose

Central Asia is a substantial source of long-range-transported dust, yet the historical and geological variability of dust activity in this region remains poorly understood. This study presents a Holocene record of dust activity from a 6.2-m loess section located near Tashkent in the westerlies-dominated region of eastern Uzbekistan, Central Asia. Utilizing the quartz optically stimulated luminescence

The current GNSS meteorology literature focuses on ground-based and space-based GNSS observations separately, without exploring potential synergies. In this study, we propose combining the two data sources using GNSS tomography to overcome current limitations in (1) horizontal resolution of GNSS space-based, (2) low vertical resolution of GNSS ground-based tropospheric retrievals when the number of

Global Navigation Satellite Systems (GNSS) applications require computation of the geometric range between the satellite vehicle at the time-of-signal transmission and the receiver antenna location at the time-of-signal reception. This computation requires attention to the frames of reference due to the rotation of the Earth-Centered Earth-Fixed (ECEF) frame during the time-of-signal propagation. Three

Automatic co-registration of multi-epoch Unmanned Aerial Vehicle (UAV) image sets remains challenging due to the radiometric differences in complex dynamic scenes. Specifically, illumination changes and vegetation variations usually lead to insufficient and spatially unevenly distributed common tie points (CTPs), resulting in under-fitting of co-registration near the areas without CTPs. In this paper

Building change detection (CD) plays a crucial role in urban planning, land resource management, and disaster monitoring. Currently, deep learning has become a key approach in building CD, but challenges persist. Obtaining large-scale, accurately registered bi-temporal images is difficult, and annotation is time-consuming. Therefore, we propose B3-CDG, a bi-temporal building binary CD pseudo-sample

3D reconstruction is an important part of digital city, high-accuracy 3D modeling method has been widely studied as an important pathway to visualizing 3D city scenes. However, the problems of image resolution, noise, and occlusion result in low quality and smooth features in the mesh model. Therefore, the model needs to be refined to improve the mesh quality and enhance the visual effect. This paper

The accurate paleoelevation reconstruction of the Qilian Mountains is critical to advancing our understanding the integrity of the Tibetan Plateau (TP) uplift model, its deep structural mechanisms, and corresponding connections with climatic, environmental, and biodiversity changes. Recently, the first quantitative reconstruction of the paleomidrange (i.e., average elevation of the basin and mountains)

Geocoding is the procedure of finding the mapping between the Synthetic Aperture Radar (SAR) image and the imaged scene. The inverse form of the Range-Doppler (RD) model has been adopted to approximate the geocoding results. However, with advances in SAR imaging geodesy, its imprecise nature becomes more perceptible. The forward RD model gives reliable solutions but is time-consuming and unable to

The early Toarcian (∼183 Ma) was characterized by pronounced climate warming associated with massive release of 13C-depleted carbon to the exogenic system, as evidenced by globally recognized negative carbon-isotope excursions (N-CIE) in biospheric carbon reservoirs. Global warming during this interval triggered a variety of environmental perturbations, of which large-scale marine deoxygenation (as

This study aims to elucidate the environmental changes signified by biogenic components, assess fluctuations in upwelling over the past century, and evaluate the potential risks associated with variations in coastal upwelling intensity on the degradation of fisheries within the Zhoushan Fishing Ground. High-resolution sediment records were established to reveal long-term variations in Zhoushan coastal

Automatic Target Recognition (ATR) from Synthetic Aperture Radar (SAR) data covers a wide range of applications. SAR ATR helps to detect and track vehicles and other objects, e.g. in disaster relief and surveillance operations. Aircraft classification covers a significant part of this research area, which differs from other SAR-based ATR tasks, such as ship and ground vehicle detection and classification

We investigate the contributions of natural (geogenic) and anthropogenic sources of lead (Pb) to the Nee Soon Swamp Forest, a relatively undisturbed catchment in Singapore. Using a Bayesian statistical source tracking approach with Pb isotopes preserved in sediment sections, our findings reveal a predominance of geogenic Pb in contemporary sediments, despite the significant presence of anthropogenic

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring

Clouds and shadows often contaminate optical remote sensing images, resulting in missing information. Consequently, continuous spatiotemporal monitoring of the Earth’s surface requires the efficient removal of clouds and shadows. Unlike optical satellites, synthetic aperture radar (SAR) has active imaging capabilities in all weather conditions, supplying valuable supplementary information for reconstructing

The exotic mangrove species Sonneratia apetala has been colonizing coastal China for several decades, sparking attention and debates from the public and policy-makers about its reproduction, dispersal, and spread. Existing local-scale studies have relied on fine but expensive data sources to map mangrove species, limiting their applicability for detecting S. apetala in large areas due to cost constraints

Building age is crucial for inferring building energy consumption and understanding the interactions between human behavior and urban infrastructure. Limited by the challenges of surveys, some machine learning methods have been utilized to predict and fill in missing building age data using building footprint. However, the existing methods lack explicit modeling of spatial effects and semantic relationships

Forest management policies play a crucial role in enhancing the carbon sequestration capacities of forests. China's current harvest-prohibitive policies may result in the development of old-growth forests and the eventual saturation of their carbon stocks. This study combines empirically derived forest biomass growth models, spatially explicit information on forest age and live biomass carbon stock

The aeolian deserts of northwestern China are major landscape types in the arid regions of the Northern Hemisphere, and collectively they are one of the largest dust source regions on Earth. However, owing to occurrence of sedimentary hiatuses within aeolian deposits in the deserts, we lack clear knowledge of the history of Holocene aeolian activity and the interaction between aeolian processes and

The accuracy of paleothermometers is a prerequisite for understanding the past sea surface temperature (SST) changes in the tropical seas. Here, we analyzed the SST estimates reconstructed by four lipid proxies with common linear and newly advanced models in parallel in a sediment core collected from the southern South China Sea (SCS). After excluding the impact of terrestrial input, all of the four

To address the numerous challenges existing in current remote sensing scene generation methods, such as the difficulty in capturing complex interrelations among geographical features and the integration of implicit expert knowledge into generative models, this paper proposes an efficient method for generating remote sensing scenes using hybrid intelligence and low-rank representation, named Word2Scene

One of the main products of multi-view stereo (MVS) high-resolution satellite (HRS) images in photogrammetry and remote sensing is digital surface model (DSM). Producing DSMs from MVS HRS images still faces serious challenges due to various reasons such as complexity of imaging geometry and exterior orientation model in HRS, as well as large dimensions and various geometric and illumination variations

Accurate localization of pedestrians and mobile robots is critical for navigation, emergency response, and autonomous driving. Traditional localization methods, such as satellite signals, often prove ineffective in certain environments, and acquiring sufficient positional data can be challenging. Single image localization techniques have been developed to address these issues. However, current deep

Rapidly intensifying global land drought poses severe threats to human societies, economies, and ecosystems. While previous studies have primarily investigated long-term drought trends, the frequency and concurrence of trend turnings have been largely neglected. In this study, we address this gap by employing the Running Slope Difference (RSD)-t-test to quantify trend turning frequency in Afro-Eurasian

Sun-induced chlorophyll fluorescence (SIF) from plants offers an effective proxy for estimating gross primary productivity (GPP) by modeling SIF-GPP relationships, a widely used method to evaluate the global carbon sink. However, most SIF-GPP models ignore SIF differences between shaded and sunlit leaves, resulting in GPP underestimation, particularly in dense vegetation. This study aims to partition

In the context of climate change, the functionality of ecosystems is primarily influenced by the availability of water and energy supply. However, there is limited research that comprehensively uses energy indicators to explore how climate change affects the water and energy limiting states of ecosystems. Here we evaluated the historical and future water and energy limitations using the Ecosystem Limitation

Wildfires are increasing in risk and prevalence. The most destructive wildfires in decades in Australia occurred in 2019–2020. However, there is still a challenge in developing effective models to understand the likelihood of wildfire spread (susceptibility) and pre‐fire vegetation conditions. The recent launch of NASA's ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution

With climate change, the frequency and intensity of wet and dry events are increasing, as is the uncertainty of their impact on tree growth. There have been studies on the effects of wet and dry conditions on tree growth, but there is a lack of comparisons of tree adaptation strategies to different types of wet and dry events in heterogeneous environments. Therefore, we explored differences in the

The El Niño–Southern Oscillation (ENSO) is the most influential climatic phenomenon affecting global ecosystems, water use, and agriculture on an interannual scale. However, limited instrumental records make it difficult to fully understand the characteristics of ENSO events. In this study, we used both monthly-resolved Porites coral δ18O records, i.e., living Porites corals of δ18O–HYDL4 (1992–2015)

Foreland fold-and-thrust belts (FTBs) are shaped by the coupled influence of surface processes (e.g., erosion, sedimentation) and deep geological processes (e.g., plate rheology, flexure and kinematics) across various timescales. These processes record multiple phases of orogenic evolution and are crucial for understanding the interplay between mountain building and the sedimentary basin filling. In

Remotely sensed evapotranspiration (ET) at a high spatial resolution (30 m) has wide-ranging applications in agriculture, hydrology and meteorology. The original optical trapezoid model for ET (O_OPTRAM-ET), which does not require thermal remote sensing, shows potential for high-resolution ET estimation. However, the non-automated O_OPTRAM-ET heavily depends on visual interpretation or optimization

Chemical weathering plays an important role in modulating Earth's climate. However, chemical weathering is usually influenced by multiple factors simultaneously (e.g., climate, topography and lithology), and it is difficult to understand how these variables modulate the effect of chemical weathering on atmospheric pCO2. In this study, we collected and analyzed water chemistry and multiple isotopes

The traditional atmospheric correction models employed with the near-infrared iterative schemes inaccurately estimate aerosol radiance at high solar zenith angles (SZAs), leading to a substantial loss of valid products for dawn or dusk observations by the geostationary satellite ocean color sensor. To overcome this issue, we previously developed an atmospheric correction model suitable for open ocean

For many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic

Stereo matching of satellite images presents challenges due to missing data, domain differences, and imperfect rectification. To address these issues, we propose cascaded recurrent networks with masked representation learning for high-resolution satellite stereo images, consisting of feature extraction and cascaded recurrent modules. First, we develop the correlation computation in the cascaded recurrent

Paleoceanographic studies rely heavily on an accurate stratigraphy as well as the interpretation of the oxygen isotope (δ18O) records generated from microfossils. In this study, we present new planktonic foraminiferal assemblage and stable oxygen isotope records of both planktonic and benthic foraminifera from sedimentary deposits at the IODP Site U1443 on the northern Ninety East Ridge in the equatorial

Accurate image segmentation is essential for image-based estimation of vegetation canopy traits, as it minimizes background interference. However, existing segmentation models often lack the generalization ability to effectively tackle both ground-based and aerial images across a wide range of spatial resolutions. To address this limitation, a cross-spatial-resolution image segmentation model for rice

Peatlands are a key carbon sink and play an important role in the global carbon cycle and climate change. Understanding the process of peatland initiation is essential to understand the carbon sink functions of peatlands. While the climate is widely recognized as the main driver that controls the peatland initiation, the importance of topography, a limiting factor that influencing local climate conditions

A greater frequency of sea fog/low clouds may be observed in the Arctic under the effects of global warming, which strongly affects the safety of navigation in the Arctic. Using moderate-resolution imaging spectroradiometer (MODIS) infrared multiband remote sensing data, in this paper, we construct a detection algorithm for sea fog and low clouds in the daytime during the summer period in the Arctic

Using bi-temporal remote sensing imagery to detect land in urban expansion has become a common practice. However, in the process of updating land resource surveys, directly detecting changes between historical land use maps (referred to as “maps” in this paper) and current remote sensing images (referred to as “images” in this paper) is more direct and efficient than relying on bi-temporal image comparisons

Yi (2023) proposed a new deep Western Pacific ventilation hypothesis using beryllium (10Be/9Be) and 230Thex dates, magnetostratigraphy, and element-based tuning data from two box cores and one gravity core. However, given the low sedimentation rates of these cores and uncertain stratigraphy, we suggest that some interpretations require further clarifications and corrections. The low sedimentation rates

The loess-paleosol sequences on the Chinese Loess Plateau (CLP) are among the best terrestrial archives for recording orbital-scale global paleoenvironmental and East Asian monsoon changes during the Quaternary Period. Dust provenance and climate patterns vary across the CLP due to its vast size. However, whether available climate proxies were influenced by varying signals from the different dust source

A scheme for satellite remote sensing is proposed to detect nighttime fog and low stratus (FLS) by combining visible, mid-infrared, and far-infrared channels. The S-NPP/VIIRS dataset and ERA5 reanalysis data are primarily used, and a comprehensive threshold system is established through statistical analysis, simulation calculations, and sensitivity experiments. 98 cases of nighttime FLS occurring from