Built-up heights serve as a nexus in understanding the complex relationship between urban forms and socioeconomic activities. With the advent of remote sensing technology, built-up height mapping from satellite observations has become available over the past years. However, the absence of high-precision sample data poses a significant limitation to built-up height mapping at large (regional or global)

Melt ponds are a common phenomenon on the surface of Arctic sea ice during the summer, and their low albedo strongly influences the energy balance of the Arctic sea ice. Estimating Melt Pond Fraction (MPF) and Melt Pond Depth (MPD) using optical remote sensing is crucial for a better understanding of rapid climate change in the Arctic region. However, current retrieval algorithms for monitoring Arctic

Arctic permafrost soils contain a vast reservoir of soil organic carbon (SOC) vulnerable to increasing mobilization and decomposition from polar warming and permafrost thaw. How these SOC stocks are responding to global warming is uncertain, partly due to a lack of information on the distribution and status of SOC over vast Arctic landscapes. Soil moisture and organic matter vary substantially over

Nighttime light (NTL) remote sensing has become an important tool to study human activities and their impact on the environment. However, accurately and quantitatively measuring NTL has remained a challenge. In this study, we propose using radiant flux as a more precise measure of NTL energy intensity, which takes into account both radiance and image pixel area. To achieve this, we develop a conversion

The three-runway system expansion project of the Hong Kong International Airport (HKIA) began with the land reclamation to the north of its original runway. To facilitate quick stabilization, the Deep Cement Mixing (DCM) in this project was featured as the novel reclamation method firstly applied in Hong Kong. Understanding ground deformation and underground consolidation is crucial for subsequent

Timely and reliable rapeseed mapping is crucial for vegetable oil supply and bioenergy industry. Synthetic Aperture Radar (SAR) remote sensing is able to track rapeseed phenology and map rapeseed fields in cloudy regions. However, SAR-based rapeseed mapping is challenging in mountainous areas due to the highly fragmented farming land and terrain-induced distortions on SAR signals. To address this challenge

Accurate building height estimation is essential to support urbanization monitoring, environmental impact analysis and sustainable urban planning. However, conducting large-scale building height estimation remains a significant challenge. While deep learning (DL) has proven effective for large-scale mapping tasks, there is a lack of advanced DL models specifically tailored for height estimation, particularly

Hydraulic traits mediate trade-offs between growth and mortality in plants yet characterizing these traits at the community level remains challenging, particularly in the Amazon, where they vary widely across species and environments. While previous studies have used reflectance-based estimates, hydraulic traits, which arise from wood and/or whole-plant anatomy and physiology, have not been comprehensively

Soil and vegetation temperatures are crucial for various fields, including ecology, agriculture, and climate change. However, there remains a lack of entirely observation-based global datasets for these two component temperatures. To fill this gap, this study developed a multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT). This novel method not only

Remote sensing of solar-induced vegetation chlorophyll fluorescence (SIF) has a rich history of more than 50 years of research covering active and passive techniques from leaf, canopy, and satellite scale. Current satellite-derived SIF products primarily focus on the far-red spectral range, with variations in techniques dependent on sensor capabilities. However, these retrieval methods often rely on

The NASA TROPICS mission encompasses a constellation of CubeSats equipped with microwave radiometers, dedicated to investigating tropical meteorology and storm systems. In a departure from traditional microwave sounders, the TROPICS Microwave Sounder (TMS) employs new frequencies at F-band near 118 GHz and features an additional G-band channel at 205 GHz. We have expanded the capabilities of the Microwave

Accurate crop yield prediction is of utmost importance for informed decision-making in agriculture, aiding farmers, industry stakeholders, and policymakers in optimizing agricultural practices. However, this task is complex and depends on multiple factors, such as environmental conditions, soil properties, and management practices. Leveraging Remote Sensing (RS) technologies, multi-modal data from

Most existing soil moisture (SM) products from earth observations and land surface models over the Qinghai-Tibetan Plateau (QTP) have coarse resolutions or are mostly generated with high spatial resolutions based on downscaling methods. The former could hinder the applications in hydrological and ecological analyses at the regional scale and the performance of the latter could be limited by the intricate

Space geodetic techniques have achieved centimeter to even millimeter precision in measuring earth surface deformation. However, a large data gap remains in the offshore area. Offshore man-made structures (e.g., oil/gas platforms) anchored to the ocean bottom provide an opportunity to study seafloor motion in some areas. Although satellite InSAR (Interferometric Synthetic Aperture Radar) has been widely

Canopy height mapping is critical for assessing forest structure, forest resilience, carbon stocks, habitat, and biodiversity, all of which are threatened by changing climate and weather extremes. While current tools utilizing lidar (e.g., GEDI) and multispectral imagery (e.g., Landsat, Sentinel-2, airborne imagery) produce canopy height products, significant challenges remain, particularly in capturing

Accurate and timely mapping of rural settlements using medium-resolution satellite imagery, such as Landsat data, is crucial for evaluating rural infrastructure, estimating ecological service values, assessing the quality of life for rural populations, and promoting sustainable rural development. Current mapping techniques, including pixel-based and object-based classifications, primarily focus on

Earth Observation (EO) data can provide added value to nations’ assessments of vegetation aboveground biomass density (AGBD) with minimal additional costs. Yet, neither open access to global-scale EO datasets of vegetation heights or biomass, nor the availability of computational power, has proven sufficient for their wide uptake in climate policy-related assessments. Using Mexico as an example, one

Over the last five decades, Arctic sea ice has been shrinking in area and thickness. As a result, increased marine traffic has created a need for improved sea ice forecasting on seasonal to annual time-scales. In this study, we introduce a novel UNET-based deep learning model to forecast sea ice concentration up to 12 months. Based on yearly hindcast validation, the UNET 3-, 6-, 9-, and 12-month predictions

High-quality and continuous satellite data are essential for land surface studies such as monitoring of land surface phenology, but factors such as cloud contamination and sensor malfunction degrade the quality of remote sensing images and limit their utilization. Filling gaps and recovering missing information in time series of remote sensing images are vital for a wide range of downstream applications

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission provides billions of lidar samples of canopy structure over the Earth's temperate and pantropical forests. Using the GEDI sample data alone, gridded height and biomass products have been created at a spatial resolution of 1 km or coarser. However, this resolution may be too coarse for some applications. In this study, we present a new method

This study introduces an Entropy-based index: the Lorenz-entropy (LE) index, which we have developed by integrating Light Detection And Ranging (LiDAR), econometrics, and forest ecology. The main goal of the LE is to bridge the gap between theoretical entropy concepts and their practical applications in monitoring vertical structural complexity of tropical forest ecosystems. The LE index quantifies

Global Positioning System (GPS) instruments capture the daily crustal 3D deformation responding elastically to terrestrial water storage (TWS) variations, providing a powerful tool for hydrological studies. Here, we further expand the application of GPS in flood potential assessment. GPS vertical and horizontal crustal deformation are inverted into TWS variations using a 3D-Inversion model, and then

The authors regret that several errors were identified in the winter wheat yield maps in the article. These errors were caused by a problem with the procedure of exporting maps from the Google Earth Engine cloud platform. They did not affect the scatterplots and relevant yield prediction accuracies presented in the article, since the accuracies were determined from the plot-level remotely sensed data

Soil spectral reflectance is a necessary input for land surface and radiative transfer models, and can be used to infer soil properties. Numerous soil reflectance inversion models have been developed based on mechanistic approaches, each with their own limitations. Mechanistic models based on radiative transfer theory are usually based on only a few input soil properties, whereas data-driven approaches

Accurate estimation of evapotranspiration (ET) is essential for the precise quantification of energy and water budgets under climate change. Remote sensing ET models provide an effective way to map ET across different spatial and temporal scales. However, conductance-based ET models such as PML_V2 are associated with limited or no water stress constraints on soil evaporation and canopy transpiration

Land surface temperature (LST) is an important parameter that critically contributes to Earth’ s climate. Thermal anisotropy is a major challenge that must be addressed while generating long-term LST products from satellites. For instance, the differences between GOES-16 and GOES-17 LST products caused by thermal anisotropy have not yet been resolved, which impacts the high-frequency monitoring of

Subpixel mapping (SPM) addresses the widespread mixed pixel problem in remote sensing images by predicting the spatial distribution of land cover within mixed pixels. However, conventional pixel-based spectral unmixing, a key pre-processing step for SPM, neglects valuable spatial contextual information and struggles with spectral variability, ultimately undermining SPM accuracy. Additionally, while

Understanding the optical properties of dorsiventral leaves and quantifying leaf biochemical traits through physical models are important for interpreting canopy radiative transfer and monitoring plant growth. Previous models, such as the dorsiventral leaf model (DLM), have effectively accounted for the inner asymmetry of the leaf but neglected the asymmetry of surface structures between the upper

Deep convective clouds (DCCs) are crucial in the Earth's energy budget. Although the abundant DCC-generated ice-phase anvil and cirrus theoretically have a warming effect, the reported observations of their cloud radiative effect (CRE) by previous studies are unexpectedly negative. Here, we find that the apparent contradiction between theory and observations resulted from neglecting the radiative contribution

Using remote sensing to measure suspended sediment concentration (SSC) in mountainous rivers can compensate for the scarcity of in situ sediment observations, providing valuable direct supplementation to observational records. However, for inland rivers, remote sensing SSC assessments face challenges such as data quality, long-term water body changes, environmental noise, flood events, and the transferability

Volcanoes are dynamic systems whose surfaces constantly evolve. During volcanic eruptions, which can pose great threat to local communities, significant changes to the local topography occur as edifices build up and/or collapse and lava, tephra and other eruptive products are deposited. Monitoring such changes in topography is crucial to risk assessment and the prediction of further eruptive behaviour

Satellite earth observation (EO) data plays a vital role quantifying vegetation structural and functional metrics across spatio-temporal scales. However, the degree of coupling between satellite derived spectral signals and the rate of photosynthesis, as estimated by Gross Primary Productivity (GPP), both before and after bushfire remain understudied, yet these are a critical part of the global carbon

Airborne laser scanning (ALS) is widely used in studies of Earth surface change and has potential to inform targeted landscape remediation over large areas. Leveraging this capability requires geomorphic change detection methods that exploit the full 3D information contained in ALS point clouds but remains challenging over large areas (i.e., > 10 km2). We developed a methodology for geomorphic change

LiDAR is a developed technology that has been widely used to measure the Earth's surface by acquiring accurate three-dimensional (3D) information. DART (Discrete Anisotropic Radiative Transfer) model developed a new LiDAR modeling method based on the Monte Carlo bidirectional path tracing mode named DART-Lux. Using the DART-RC (Ray Carlo) mode as a reference, DART-Lux shows consistency and efficiency

Large volumes of meltwater produced on the northern Greenland Ice Sheet (GrIS) are directly routed into proglacial rivers, forming continuous supraglacial-proglacial catchments. Thereby, estimating proglacial river discharge is crucial for better understanding of northern Greenland hydrology and mass balance. We propose a method for estimating proglacial river discharge solely from space by combining

This study proposed an innovative joint inversion algorithm that synchronized Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (TIR) radiance data for accurate estimates of clear-sky precipitable water vapor (PWV). The algorithm consists of three parts: (1) simplifying the NIR radiative transfer equation by assuming linear reflectance change with wavelength

Anisotropy of artificial light at night (ALAN) has been revealed from satellite observations, as Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) provides multi-angle measurements of ALAN. However, the knowledge behind this phenomenon is very limited. In this study, we hypothesize that vegetation phenology impacts the anisotropy of ALAN, which is defined as the change in radiant

The product of near-infrared reflectance of vegetation and photosynthetic active radiation (NIRvP) is a new tool for monitoring gross primary productivity (GPP) dynamics in terrestrial ecosystems, due to the discovered linear correlation between NIRvP and GPP. While remote sensing-based NIRvP is considerably influenced by sensor geometry, such geometry impacts on the NIRvP-GPP relationship remain underexplored

Litter on the forest floor, or from a fire perspective the litter fuel load (LFL), is a key driver of the occurrence and spread of surface fires and an important regulator of forest fire behavior. High-quality spatiotemporal LFL data are essential for modeling fire behavior and assessing fire risk in forest ecosystems. Traditionally, LFL is estimated from ground-based measurements, but they are difficult

Management of forest genetics is shifting from a paradigm focused on increasing timber volume to a prioritization of climate adaptation. Functional traits related to foliar structure, photosynthetic and photoprotective pigments, and stress underlie climate adaptation and have spectral signatures that can be quantified with remote sensing. Common-garden trials present an opportunity to assess the genetic

The calculation of the shoot Silhouette area to Total needle Area Ratio (STAR) provides a method for assessing the light interception efficiency of a coniferous shoot. We illustrate the effectiveness of a close-range, blue light 3D scanning system as a new, affordable, and highly efficient technique for estimating STAR values. The distributions of STAR and contour length of shoots for most of the diverse

Surface ozone (O3) has become a primary pollutant affecting urban air quality and public health in mainland China. To address this concern, we developed a nation-wide surface maximum daily average 8-h (MDA8) O3 concentration dataset for mainland China (ChinaHighO3) at a 10-km resolution with a start year of 2013, which has been widely employed in a wide range of studies. To meet the increasing demand

Passive microwave remote sensing provides unique pan-Arctic light- and cloud-independent daily coverage of lead fraction (LF) for Arctic winter and spring. In this study, we conducted a quantitative assessment of various sea ice concentration (SIC) data products and LF retrieval algorithms to evaluate their accuracy in deriving lead fractions at both overall and pixel-wise levels. Our results indicate

Satellite-based measurements have emerged as an effective method for the top-down estimates of anthropogenic CO2 emissions. Changes in the column-averaged dry-air mole fractions of CO2 (XCO2) in the atmosphere reflect contributions from both human activities and natural processes, posing challenges in accurately extracting anthropogenic XCO2 signals and quantifying urban CO2 emissions. Here, we introduce

With the rapid advancement of remote sensing technology, the recognition of agricultural field parcels using time-series remote sensing images has become an increasingly emphasized task. In this paper, we focus on identifying crops within scattered, irregular, and poorly defined agricultural fields in many Asian regions. We select two representative locations with small and scattered parcels and construct

Soil moisture (SM) is a key variable in hydrometeorology and climate systems. With the growing interest in capturing fine-scale SM variability for effective hydroclimate applications, spaceborne L-band bistatic radar systems using Global Navigation Satellite System-Reflectometry (GNSS-R) technology hold great potential to meet the demand for high spatiotemporal resolution SM data. Although primarily

Satellite-derived normalized difference vegetation index (NDVI) is inevitably contaminated by clouds and aerosols, causing large uncertainties in depicting the seasonal and interannual variations of terrestrial ecosystems, and potentially misrepresents their responses to climate change and climate extremes. Although various methods have been developed to reconstruct NDVI time series using the similarity

Community Open Spaces (COS) refer to the fine-grained and micro-open areas within communities that offer residents convenient opportunities for social interaction and health benefits. The mapping of COS using Very High Resolution (VHR) imagery can provide critical community-scale data for monitoring urban sustainable development goals (SDGs). However, the three-dimensional structure of COS often results

Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP

Separation of soil effects from top-of-canopy (TOC) reflectance is crucial for quantitative remote sensing of vegetation. Soil affects TOC reflectance via the soil-vegetation interaction and the direct reflection by soil. Various vegetation indices have been developed semi-empirically to mitigate the interferences caused by soil for specific applications, such estimating biomass and monitoring vegetation

Plant functional traits are key drivers of ecosystem processes. However, plot-based monitoring of functional composition across both large spatial and temporal extents is a time-consuming and expensive undertaking. Airborne and satellite remote sensing platforms collect data across large spatial expanses, often repeatedly over time, raising the tantalising prospect of detection of biodiversity change

Successful surface soil moisture (SM) retrieval from space has been enabled by microwave satellite measurements of Earth's upwelling brightness temperature (TB). Nevertheless, correction for the impact of vegetation on TB emission remains a challenge for SM retrieval algorithms. Such correction is often performed in a simplified manner. For example, the Single Channel Algorithm (SCA) uses ancillary

Terrestrial vegetation is a crucial component of Earth's biosphere, regulating global carbon and water cycles and contributing to human welfare. Despite an overall greening trend, terrestrial vegetation exhibits a significant inter-annual variability. The mechanisms driving this variability, particularly those related to climatic and anthropogenic factors, remain poorly understood, which hampers our

Runoff variability in glacierized transboundary river basins over High Mountain Asia (HMA) directly affects the stability of water supply for more than one billion people in Asia. However, limited by insufficient in-situ gauges and imprecise hydrological model output, it is still a challenge to accurately monitor and comprehensively analyze the HMA runoff change. In this paper, we construct a water

Solar-induced chlorophyll fluorescence (SIF) is a promising tool to estimate gross primary production (GPP), but the retrieval of SIF is commonly noisy and highly sensitive to various interference factors. Particularly, the retrieval of SIF in the red band (RSIF) is more challenging than in the far-red SIF (FRSIF) due to the weaker fluorescence signal and the weaker absorption depth of oxygen at the