The intensity and spectral properties of solar-induced chlorophyll fluorescence (SIF) carry valuable information on plant photosynthesis and productivity, but are also influenced by leaf and canopy structure. Physically based models provide a quantitative means to investigate how SIF intensity and spectra propagate and scale from the photosystem to the leaf and to the canopy levels. However, the validation

Sea state information is critical for a broad range of human activities (e.g. shipping, marine energy, marine engineering) most of them being concentrated along the coastal zone. Satellite altimeter records of significant wave heights (SWH) represent the largest source of sea state observations available to date. However, the quality of altimeter observations is reduced in the coastal zone due to surface

To improve the data availability of SAR mode altimeters in coastal zones, we propose a new Adaptive Subwaveform Coastal Retracker (ASCR) and include the empirical coastal retracker ITAS (Improved Threshold Adaptive Subwaveform) and the full-waveform coastal retracker MSCR (Modified SAMOSA+ Coastal Retracker) for comparison in this study. The Sentinel-3A/B altimeter data during the period between January

The physical drivers of Sentinel-1 C-band backscatter observations during snow accumulation are still uncertain. To investigate these, backscatter fluctuations (in co-polarization VV, cross-polarization VH, and cross-polarization ratio VH-VV) were temporally and spatially linked to modeled surface (0–10 cm) soil moisture (SM) and soil temperature (T) (here referred to as soil dynamics) and modeled

Forest floor vegetation plays a crucial role in ecosystem processes of temperate and boreal forests. Remote sensing offers a valuable tool to characterize the forest floor through reflectance spectra. While passive optical airborne and satellite data have been used to map spectral properties of forest understory, these sensors are limited by cloud cover, especially in high latitudes. To date, LiDAR

Surface ozone (O3) is a critical ambient pollutant that poses significant risks to both human health and ecosystems. However, there is a scarcity of high-spatial-resolution hourly surface O3 data, which is crucial for understanding its diurnal variations. In this study, we employed a best-performing spatiotemporal artificial intelligence (AI) model to estimate 24-hourly 1-km-resolution surface O3 concentrations

Over the past two decades, climate change has led to the intensification of the hydrologic cycle and greatly altered global land evapotranspiration (ET). Existing low-resolution evapotranspiration datasets, though valuable for global estimates, does not fully capture spatial heterogeneity and local-scale effects, necessitating the need for higher-resolution assessment of field-scale ET for enhanced

Ponds are an important source of greenhouse gases (GHGs) to the atmosphere, yet evaluating their role in global biogeochemical cycling is currently hampered by limitations in quantifying their global distribution. Existing satellite-derived estimates of lake distributions have difficulty identifying small lakes (5–10 ha) and ponds (<5 ha) due to limitations in satellite resolution and challenges extracting

Urban vegetation phenology is essential for understanding climate change impacts on urban ecosystems, offering insights into ecological and health implications. Although previous studies have explored the response patterns of vegetation phenology along the urban-rural gradient, quantitative analysis considering the neighborhood and scale effects is still insufficient. In this study, we comprehensively

Milne Glacier is a marine-terminating glacier located on the northern coast of Ellesmere Island in the Canadian High Arctic, a region that has experienced extensive ice-mass loss over the last two decades. Milne Glacier flows into Milne Fiord where it transitions from grounded to floating at its grounding line. The glacier rests on a retrograde slope and is therefore potentially vulnerable to marine

Identifying materials and retrieving their properties from spectral imagery is based on their spectral reflectance calculated from the ratio of reflected radiance to the incident irradiance. However, obtaining the true reflectances of materials within a vegetation canopy is challenging given the varying illumination conditions across the canopy – i.e., the irradiance incident on a surface inside the

The pronounced impact of topography on meteorological conditions has largely limited evapotranspiration (ET) remote sensing techniques to relatively flat terrains. This study addresses this limitation by adapting and assessing the performance of two common ET models based on thermal infrared data in rugged mountainous regions: a physically-based energy balance model (TSEB-PT), and a contextual model

A large part of the surface of the Greenland Ice Sheet (GrIS) and the margins of Antarctica are melting every summer, affecting their surface mass balance. Wet/dry snow status has been detected for decades using the peaks of brightness temperature at 19 GHz, and more recently at L-band (1.4 GHz) using both the SMOS and SMAP missions. SMOS owns a longer time series than SMAP with data since 2010, but

Forests provide important ecosystem functions such as carbon sequestration and climate regulation, particularly in countries with high forest cover. Climate change-induced extreme weather events have a negative impact on many forest ecosystems. In Germany, for instance, the drought of the years 2018 until 2020 resulted in signs of damage in almost 80% of trees. This decline in forest vitality has additionally

Satellite products provide the best way to monitor the solar radiation reaching the Earth’s surface on a global scale. However, their capability to monitor solar radiation trends needs to be constantly evaluated. This depends on their temporal stability and the accurate representation of all processes driving solar radiation. This study evaluates these aspects by comparing and cross-comparing different

The dependence of L-band Vegetation Optical Depth (L-VOD, τ ) on Vegetation temperature TV is investigated for 1165 boreal forest grid cells selected for latitudes > 55° and high radiometric forest fraction FFO≥90% . SMOS Level-3 Brightness Temperatures (BT) at ascending orbits acquired from 2011 to 2022 are used. This is a spatio-temporal extension of our previous study on τTV made over the “Sodankylä

Land surface temperature (LST) is the key parameter for characterizing the water and energy balance of the Earth’ surface. At present, thermal infrared (TIR) remote sensing provides the most efficient way to obtain accurate LST regionally and globally. Among existing satellites, the Landsat-9 could observe the Earth's surface via two TIR channels, making it possible to generate the global LST product

This paper describes an approach to analyze ground deformation data collected by InSAR (Interferometric Synthetic Aperture Radar) imaging the volcano feeding system (VFS) beneath a caldera. The approach is applied to the Campi Flegrei caldera in southern Italy, a densely populated area at high risk for volcanic eruption. The method is a 4D tomographic inversion that considers a combination of 3D pressure

China successfully launched the Atmospheric Environment Monitoring Satellite (AEMS) equipped with an Atmospheric Carbon Dioxide Lidar (ACDL) on April 16, 2022, which is the world's first satellite based on Integrated Path Differential Absorption (IPDA) technique to detect the atmospheric CO2 column-weighted dry-air mixing ratio (XCO2). In order to accurately and quickly process the AEMS measurements

The authors regret that several errors were identified in the captions of Fig. 1, 11 and 15, as well as in a referenced citation in Section 3.3 and 4.4 of this publication.

Warming in the Third Pole region accelerates glacier and snow melt, leading to a rise in glacial lake numbers and sizes. However, accurately measuring their water level changes poses challenges, hindering precise volume assessments and evaluation of glacier mass balance contributions. Here, we took the Ak-Shyirak glaciers and the largest Petrov proglacial lake in the Central Tien Shan as a case study

The terrestrial water storage anomaly (TWSA), derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-on (GRACE-FO) satellite, presents a remarkable opportunity for extreme weather detection and the enhancement of environmental protection. However, the practical utility of GRACE data is challenged by an 11-month data gap and several months of missing data

Sun-induced chlorophyll fluorescence (SIF) has recently emerged as a proxy for canopy photosynthesis of vegetation and offers a promising approach for scalable remote crop monitoring. Effective application of SIF for crop monitoring requires better understanding of the processes that cause SIF-photosynthesis decoupling at leaf and canopy scales. To answer this challenge, we developed a novel automated

The early detection of forest pests and diseases is a primary focus of remote sensing applications for forest health monitoring. Pine Wilt Disease (PWD), which causes significant damage to pine resources in many countries and regions, has been a key area where the close-range hyperspectral remote sensing has demonstrated its advantages for early diagnosis. However, it remains unclear whether PWD can

The Earth's climate is changing rapidly and unexpectedly, causing more frequent, longer and more severe droughts, with lasting impacts on plants, ecosystems, communities and people. Consequently, this is leading to an increased importance of monitoring the climate and water storage trends in different regions. This information on a global scale is already commonly derived using satellite-based geodetic

The split-window (SW) and temperature-and-emissivity separation (TES) algorithms have been widely used for land surface temperature (LST) estimation from thermal infrared (TIR) observations for various missions. However, the SW algorithm requires prior estimates of land surface emissivity (LSE). The TES algorithm encompasses an atmospheric correction module, which increases the complexity and uncertainty

Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on

Identifying changes in volcanic unrest and tracking eruptive activity are fundamental for volcanic surveillance and monitoring. Magmatic gases, particularly sulphur dioxide (SO2), play a crucial role in influencing eruptive styles, making the monitoring of SO2 emissions essential. Recent advancements in satellite remote sensing technology, including higher spatial resolution and sensitivity, have enhanced

Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry

Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian

Information on rice planting areas is critically important for food and water security, as well as for adapting to climate change. Mapping rice globally remains challenging due to the diverse climatic conditions and various rice cropping systems worldwide. Synthetic Aperture Radar (SAR) data, which is immune to climatic conditions, plays a vital role in rice mapping in cloudy, rainy, low-latitude regions

Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations

Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid

The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth’s dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product’s overall performance using multiple

Remote sensing has the potential to dramatically advance river discharge monitoring globally, but precision of primary data (water surface elevation (WSE) and river width) remains a limiting factor. WSE can be measured from altimeters, and river width from imagers, but the measurements historically have not been made concurrently from space. This is changing with the advent of the Surface Water and

Urban heat island (UHI) is among the most pronounced human impacts on Earth. To formulate locally adapted mitigation strategies, a comprehensive understanding of the influencing mechanisms of UHI at high resolution is imperative. Based on surface energy balance, we attributed surface UHI (SUHI) into five biophysical terms (surface radiation, anthropogenic heat, convection, evapotranspiration and heat

Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping with reliable individual tree attributes. Although deep learning techniques

Detecting early signs of impending eruptions and monitoring the evolution of volcanic phenomena are fundamental objectives of applied volcanology, both essential for timely assessment of associated hazards. Thermal remote sensing proves to be a cost-effective, yet reliable, information source for these purposes, especially for the hundreds of volcanoes still lacking conventional ground-based monitoring

Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and

Flood maps based on Earth Observation (EO) data inform critical decision-making in almost every stage of the disaster management cycle, directly impacting the ability of affected individuals and governments to receive aid as well as informing policies on future adaptation. However, flood map validation also presents a challenge in the form of class imbalance between flood and non-flood classes, which

Interregional transport plays a significant role in haze formation with varying and disputable contribution extent. Current research on quantitatively analyzing interregional atmospheric pollution transport has mainly relied on meteorological and chemical models. However, these models are typically affected by uncertainties due to the assumptions and simplifications inherent in the numerical simulations

Accurate detection sensitivity characterization of remote methane monitoring technologies is critical for designing, implementing, and auditing effective emissions monitoring and mitigation programs. Several research groups have developed test methods based on single/double-blind controlled release protocols and regression-based data analysis techniques to create probability of detection (PoD) models

The Kuroshio has a fundamental impact on the regional oceanography of the northwestern Pacific. But identification of the Kuroshio path (KP), an abstraction of the course along which the Kuroshio mainstream moves, has not yet been established in a systematic manner. We optimally track the KP and study its variability in the northwestern Pacific south of ∼31°N, where eddy activity is rich. An automatic

Accurate estimation of the surface-emitted longwave radiation (SELR) has important scientific value in understanding its spatiotemporal dynamics and surface thermal environment. Thermal infrared (TIR) images with high spatial resolution offer enhanced data support for studying SELR of complex surfaces, such as urban surface. This study proposes a new urban-oriented hybrid (UoHy) method, which considers

Diffuse solar radiation (DR) constitutes a vital component of solar energy reaching the surface of the Earth. The demand for extensive temporal and spatial coverage of DR data has intensified in the realms of solar energy harvesting, agriculture, and climate change. However, until now, long-term DR observations have only been available from 17 stations across mainland China. Consequently, there is

Distance and incidence angle effects play crucial roles in determining the raw intensity captured by light detection and ranging (LiDAR) systems. For these two effects, the emergence of hyperspectral LiDAR necessitates a deep theoretical exploration of potential coupling relationships and wavelength dependence. From a theoretical standpoint, this study provides a systematic demonstration, based on

This work uses a mixture of observations from surface remote sensing (AERONET) and satellite remote sensing (OMI) to uniquely compute the atmospheric column loading of black carbon (BC) mass concentration density (MCD) and number concentration density (NCD) on a grid-by-grid, day-by-day basis at 0.25°x0.25° over rapidly developing and biomass burning (BB) impacted regions in South, Southeast, and East

The adjacency effect distorts the top-of-atmosphere (TOA) spectral signals of coastal and inland waters and is a major challenge for optical remote sensing of nearshore aquatic environments. We introduce a closed-form expression that corrects for the adjacency effect prior to atmospheric correction. The method is included in an open-source Python tool, which ingests level-1 imagery and calculates the

The stability of a climate data record (CDR) is essential for evaluating long-term trends in surface temperature using remote sensing products. In the case of a satellite-derived CDR of land surface temperature (LST), this includes the stability of processing steps prior to the estimation of the target climate variable. Instability in the masking of cloud-affected observations can result in non-geophysical

Spaceborne lidars have demonstrated outstanding global ocean observation in terms of sampling at day- and night-time and penetrating thin cloud and aerosol layers. A spaceborne high-spectral-resolution lidar (HSRL) has the potential to provide accurate optical properties by decreasing the number of assumptions in the retrieval algorithm in comparison with classical elastic spaceborne lidar. In this

Rapid and accurate assessment of building damage in sudden-onset disasters is crucial for effective humanitarian assistance and disaster response. However, the occurrence of disasters is highly uncertain, e.g., unexpected geographic location and hazards, which challenge the conventional building damage assessment model on generalization and transferability. Unfortunately, there is little public literature

The Sustainable Development Science Satellite 1 (SDGSAT-1) provides a novel nighttime light (NTL) data product with medium spatial resolution, captured by its unique Glimmer Imager (GLI) sensor. Unlike traditional NTL products, the exceptional resolution of SDGSAT-1 NTL data allows for distinct visualization of urban road networks. Although recent studies have validated the effectiveness of SDGSAT-1

Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable

In situ samples are essential for crop mapping, but the collection of samples is time-consuming and labor-intensive, and the samples are usually only valid for the current year, due to the crop rotation across years. In this paper, we discuss an alternative solution, i.e., whether using transfer learning to mine useful information from historical products can achieve cross-year crop mapping without

Azimuth or along-track (approximately north-south) motion is critical in constructing three-dimensional ground motion with synthetic aperture radar (SAR) satellites orbiting the Earth in sun-synchronous polar orbit. The main problem of measuring azimuth motion with short-wavelength SAR data is decorrelation. A fleet of newly launched and upcoming long-wavelength L-band SAR satellites bring new opportunities

The rapid increase in Urban Construction Sites (UCSs) due to urbanization has become a global trend. UCSs are crucial for timely tracking of urban expansion and renewal progress, understanding settlement environments and human activities, and achieving Sustainable Development Goals (SDGs) 3 and 11. However, distinguishing UCSs from other land covers remains challenging, whether using spatial texture

In order to facilitate the shift towards sustainable practices and to support the transition to renewable energy, there is a requirement for faster and more accurate predictions of solar irradiance. Surface solar energy predictions are essential for the establishment of solar farms and the enhancement of energy grid management. This paper presents a novel approach to forecast surface solar irradiance