Daytime land surface temperature (D-LST) plays a pivotal role in regulating net ecosystem exchanges and is characterized by rapid fluctuations. Thermal infrared satellite remote sensing (TIRS) is widely used to acquire D-LST data owing to its global coverage and high-frequency observations. However, the presence of cloud cover impedes the TIRS technique by obstructing ground thermal emissions. A prevalent

Aerosols, which offset a third of the greenhouse gas forcing, remain the primary source of uncertainty in climate monitoring. Satellite products, models, or reanalyses provide time series of Aerosol Optical Depth (AOD ), each with distinct strengths and weaknesses. This study evaluates the temporal stability of these datasets from 2003 to 2022 using spatially representative long-term AERONET measurements

Gross primary production (GPP) is a key indicator of plant growth and ecosystem health, and accurately capturing its diurnal variation is crucial for understanding vegetation responses to extreme heat and drought. However, the applicability of satellite-based semi-empirical models to diurnal GPP estimation remains limited. This study refined diurnal GPP estimation in humid temperate climates by leveraging

Snowmelt provides critical water resources that impact ecosystem health and hazard frequency; however, the timing of melt is difficult to infer across large spatial scales. While Synthetic Aperture Radar (SAR) has been used to detect snowmelt onset, the accuracy of different methodological approaches requires evaluation. We use Sentinel-1 SAR observations to estimate snowmelt timing at automated snow

In recent years, increasing wildfire activity in the western United States has led to significant emissions of smoke aerosols, impacting the atmospheric energy balance through their absorption and scattering properties. Single scattering albedo (SSA) is a key parameter that governs these radiative effects, but accurately retrieving SSA from satellites remains challenging due to limitations in sensor

The surveillance and detection of civil aircraft over a wide area has long been a technical challenge, with no available datasets and complete detection methods yet. The first global space-based three-channels thermal infrared flying civil aircraft dataset (TIFAD.v1) is established by this paper, covering 17 months, six continents, with 21,004 aircraft and 1252 contrail aircraft, integrating ADS-B

The impacts of climate change pose significant challenges to global forest ecosystems, particularly in Mediterranean evergreen forests dominated by Aleppo pine (Pinus halepensis Mill.). Since the CLMS 10-daily Gross Dry Matter Productivity (GDMP) product represents the potential productivity and, by definition, does not account for water stress, this study aims to evaluate and improve the Gross Primary

Compared with those provided by polar-orbiting satellites/sensors (e.g., Landsat/MODIS), new-generation geostationary satellites, such as Himawari-8 and Geostationary Operational Environmental Satellite-R series (GOES-R), offer temporally continuous and much more frequent observations of the land surface over the course of the diurnal cycle. In this study, Himawari-8 land surface temperature (LST)

42-years of Arctic and Antarctic Surface Temperatures from thermal Infrared satellite radiometers (AASTI) are presented as the Copernicus Climate Change Service Ice surface temperature record v1.1 dataset (C3S IST). It covers snow, ice and ocean surfaces with mean and max–min daily temperatures poleward of 50 degrees North and South, for the period 1982–2023. The C3S IST is provided as a Level 3 (L3)

Methane is an important greenhouse gas contributing to global warming and climate change. The effective identification of atmospheric plumes in spatial images of methane concentration data retrieved from remote sensing is a critical step in quantifying emissions and ultimately helping to mitigate climate change by reducing large methane emission sources. In this paper, we propose a comprehensive efficient

The hotspot-sun angular offset (the spherical circular angle between hotspot and sun positions, termed ΔCA) in urban thermal anisotropy (UTA) plays a pivotal role in advancing remote sensing of urban climates. However, its diurnal and monthly variations across scenarios influenced by sun position, urban morphology, and thermal inertia remain largely unknown. Here we filled this knowledge gap based

Hyperspectral remote sensing has great potential for monitoring chlorophyll-a concentration (Chla) in optically complex waters. However, various hyperspectral indices currently used for retrieving Chla in productive waters exhibit drawbacks due to interference from other water parameters, imperfect atmospheric correction, and the constraints imposed by discrete spectral bands. To address these issues

High spatial resolution Ice/snow Surface Temperature (IST) data provides prominent advantages for polar research, such as identification of sea ice lead, monitoring of surface melting on ice shelves and variations of polynyas. As the first satellite dedicated to sustainable development goals, SDGSAT-1 is equipped with 30 m thermal infrared bands, making it highly promising for monitoring fine scale

The Doppler shift observed by single-beam synthetic aperture radar (SAR) has been widely used to retrieve the radial velocity of ocean surface currents. However, operational marine forecasting centers require full surface current vector fields for data assimilation and forecast validation. To address this need, we propose a method to reconstruct the two-dimensional surface current vector from SAR Doppler

In recent years new sensors and products have been developed to advance our capabilities in assessing human activities based on the remote sensing of night lights. Correctly understanding patterns in human activity and land use based on night lights, is key to gauge our advancement in reaching the United Nations Sustainable Development Goals. In this paper I focus on five challenges in remote sensing

Soil moisture (SM) is a critical variable in land-atmosphere interactions. As an important passive microwave remote sensing dataset, the Fengyun-3B (FY-3B) SM has been applied in a variety of scientific studies and applications. However, due to the discontinuous coverage of satellite revisit orbits, the FY-3B SM contains a large range of data gaps, which greatly limit the applicability. To solve this

The importance of conservation agriculture (CA) is undeniable, both for improving soil health and offering a viable path towards achieving carbon neutrality. However, to date, survey statistics on the extent of conservation agriculture were based on farmer declarations or field inspections. This is a major impediment to the promotion or monitoring of conservation agriculture. Here, we collected the

Reservoir drought is a valuable indicator of regional hydrological drought severity; however, it has received limited attention because of the low quality of reservoir storage data. This study proposes a Remote Sensing-Based High-Resolution Reservoir Drought Index (RS-HRDI) that integrates recent high-resolution satellite observations with historical low-resolution records to construct a long-term

Methods based on upward canopy gap fractions are widely employed to measure in-situ effective LAI (Le) as an alternative to destructive sampling. However, these measurements are limited to point-level and are not practical for scaling up to larger areas. To address the point-to-landscape gap, this study introduces an innovative approach, named NeRF-LAI, for corn and soybean Le estimation that combines

Accurate and reliable ship target detection is of great significance for the sustainable development goals of ocean management. With the development of remote sensing technology, satellite imagery provides strong support for space-based tiny ship detection. However, remote sensing images have complex backgrounds, and it is challenging to separate and locate different numbers of small ship targets in

To assimilate passive microwave data in numerical weather prediction, a comprehensive understanding of the components of the radiative transfer equation is essential. Given the significant variability of emissivity in snow-covered regions — affected by frequency, polarisation, and the macro- and microstructural properties of snow — attention must be paid to the design of a forward model. However, existing

The Scanning Microwave Spectrometer (SCAMS) onboard the NIMBUS-6 satellite operated between 15 June 1975 and 1 June 1976. Its primary mission objective was to map tropospheric temperature profiles for improving weather predictions, measuring Brightness Temperature(s) (TBs) at five different frequencies (22.235, 31.65, 52.85, 53.85 and 55.45 GHz). However, the top-of-the-atmosphere emission measured

Plant water use strategies play a crucial role in regulating soil moisture, mediating plant-climate feedbacks, and influencing species competition and symbiotic relationships. However, the lack of long-term and large-scale studies on plant water sources has significantly limited comprehensive estimations of the spatiotemporal variations in plant water sources and their impacts on ecohydrological processes

Urban thermal anisotropy poses significant challenges for accurately retrieving land surface temperature (LST) in urban environments using wide-swath polar orbiters. Existing physical and kernel-driven models often require detailed urban structural and property information or rely on simultaneous multi-angle LST observations, limiting their applicability for normalizing directional LSTs across diverse

Nighttime light (NTL) imagery remotely sensed from outer space has been suggested to be a suitable proxy to investigate socioeconomic dynamics. Since the outbreak of COVID-19, many studies have used NTL imagery to estimate the impacts of the pandemic. However, finer-grained analytics are rarely achieved limited by the spatial resolution of major NTL data sources. In November, 2021, the Sustainable

Canopy chlorophyll content per ground area (CCC, g·m−2) is tightly related to vegetation photosynthesis and is a promising indicator of photosynthetic capacity. However, a global operational CCC product is not yet available. To fill this gap, we developed a two-step upscaling method to estimate global CCC from Sentinel-3 OLCI top-of-atmosphere (TOA) reflectance. In the first step, a physically-based

Machine learning models have steadily improved in estimating inherent optical properties (IOPs) from remote sensing observations. Yet, their generalization ability when applied to new water bodies, beyond those they were trained on, is not well understood. We present a novel approach for assessing model generalization across various scenarios, including interpolation within in situ observation datasets

Snow algae, microscopic organisms thriving in snow-covered environments, significantly affect snow albedo and broader climatic processes. This study introduces the Algae Presence Index (API), a novel spectral tool using Sentinel-2 multispectral imagery to detect and classify red and green algae on King George Island, Antarctica. From 2019 to 2023, we analyzed temporal and spatial variations in algae

Synthetic Aperture Radar (SAR) altimetry has been widely used for monitoring river water levels, especially over large and medium-sized rivers. However, challenges still remain in obtaining continuous and high-precision water levels over small rivers due to the altimeter's sparse along-track sampling, distorted waveforms, and river slopes. This study presents a new multi-parameter optimized sub-waveform

Land elevation data are indispensable for topographic mapping and geological disaster monitoring. However, the existing ICESat-2/ATL08 (V04) product has a coarse resolution (≥100 m) and is characterized by high uncertainty in mountainous areas; thus, it cannot be used to describe terrain relief characteristics accurately. In this study, a new method for extracting terrain surface elevation is proposed

This study presents the first attempt to observe wildfire enhancements of carbon monoxide (CO) and volatile organic compounds (VOCs) around sunrise and sunset from a hyperspectral infrared sounder in a dawn-dusk sun-synchronous orbit. The 2nd generation of High Spectral Infrared Atmospheric Sounder (HIRAS-II) on board FengYun-3E (FY-3E), the world's first civilian dawn-dusk orbit meteorological satellite

As one of the critical parameters in the land-atmosphere exchange processes, land surface temperature (LST) plays an essential role in various domains, such as climate change, urban heat island effect, disaster monitoring, and evaporation retrieval. Thermal infrared (TIR) remote sensing is one of the main approaches to obtaining LST on a large scale. For the sensors with only one TIR channel, the single-channel

Accurate knowledge of vegetation water content (VWC) and crop height is crucial for agricultural management, environmental monitoring, and for satellite-based retrieval algorithms for geophysical variables. Traditional methods to estimate VWC, primarily rely on optical indices, which has limitations of biomass saturation, and sensitivity to atmospheric conditions. This study introduces a novel application

This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial–spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D

Radiative transfer models (RTM) enable the simulation of remote sensing observations and can therefore be useful for sensitivity analyses and model inversions, for example to determine the biophysical properties of vegetation. For this purpose, the calculation of observation derivatives is crucial. In this study, we propose to differentiate vegetation RTM based on Monte Carlo Ray tracing, PolVRT, as

Restoring natural ecosystems has the potential to remove billions of tons of CO2 annually through the end of the century, but rigorously measuring the climate impacts of restoration activities on the ground remains elusive. Ecosystem restoration interventions across hundreds or thousands of smallholder properties require robust above-ground biomass (AGB) products at high spatial (deca-metric: 10–30 m)

Satellite land surface temperature (Ts) provides valuable information on vegetation drought stress via its physical linkage to plant stomatal activity and transpiration. New-generation geostationary satellites offer opportunities to monitor sub-diurnal variations in Ts and thus track plant physiological stress response occurring at sub-daily timescales. Nevertheless, the potential of satellite Ts and

An up-to-date, detailed global urban land use map is essential for disclosing urban structures and dynamics as well as their differences across different regions. However, generating an accurate global urban land use map remains challenging due to the complex diversity of land use types and the uneven availability of data. Existing methods, which either rely solely on remote sensing imagery or treat

Ecological niche models (ENMs) are crucial for identifying habitat distribution patterns, understanding habitat preferences, and formulating effective conservation policies. However, accurately quantifying the three-dimensional (3D) structure of habitats, a fundamental component, presents challenges. These estimations heavily depend on the quality of original samples (presence/absence), yet reliable

Accurate and reliable prediction of leaf traits is crucial for understanding plant adaptations to environmental variation, monitoring terrestrial ecosystems, and enhancing comprehension of functional diversity and ecosystem functioning. Currently, various approaches (e.g., statistical, physical models) have been developed to estimate leaf traits through hyperspectral remote sensing and leaf spectroscopy

Due to their importance in monitoring and modelling Earth's climate, the Global Climate Observing System (GCOS) designates leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) as essential climate variables (ECVs). The Simplified Level 2 Biophysical Processor (SL2P) has proven particularly popular for decametric (i.e. 10 m to 100 m) retrieval of these ECVs

Landslide susceptibility assessment (LSA) plays a vital role in disaster prevention and mitigation. Recently, numerous data-driven LSA approaches have emerged. Nonetheless, most of them neglected the rapid oscillations within the landslide-prone environment, primarily due to significant changes in external triggers such as rainfall, which would render landslides susceptible to varying causations over

Invasions by Spartina species have posed serious threats to coastal ecosystems worldwide. Since the introduction of Spartina alterniflora (S. alterniflora) in China in 1979, it has expanded across 68,000 ha of coastal wetlands by 2020. In 2022, the Chinese government issued the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)”, aiming for nationwide eradication

In spite of the efforts made for canopy water content (CWC) mapping in the community, including vegetation water proxy from microwave-based vegetation optical depth (VOD) as well as optical-based indices, there is still no operational CWC product from optical satellites up to now. To fill this gap, this study proposes a unified algorithm for CWC mapping at both decametric and coarse spatial resolution

Land surface temperature (LST) plays an important role in Earth energy balance and water/carbon cycle processes and is recognized as an Essential Climate Variable (ECV) and an Essential Agricultural Variable (EAV). LST products that are issued from satellite observations mostly depict landscape-scale temperature due to their generally large footprint. This means that a pixel-based temperature integrates

Identifying where maritime activities take place, and quantifying their potential impact on marine biodiversity, is important for the sustainable management of marine areas, spatial planning and marine conservation. Detection and monitoring of small vessels, such as pleasure crafts, has been challenging due to limited data availability with adequate temporal and spatial resolution. Here, we develop

The Light Use Efficiency (LUE) model, epitomized by the Carnegie-Ames-Stanford Approach (CASA) model, is extensively applied in the quantitative estimation and analysis of vegetation Net Primary Productivity (NPP). However, the classic CASA model is marked by significant complexity: the estimation of environmental stress, in particular, necessitates multi-source observation data and model parameters

One of the core applications of satellite-based classification maps is area estimation. Regardless of the algorithms used, maps will always contain errors stemming from imperfect input and training/calibration data, incomplete data coverage, and spectral and/or temporal confusion between land cover and land use classes. Because of omission and commission errors, the pixel-counting area estimator will

Near-surface air temperature (Ta) is a critical meteorological variable, and obtaining its precise spatiotemporal distribution is essential for numerous scientific domains beyond meteorology and hydrology. Despite the promising advancements in Ta mapping using machine learning, these models often suffer from inadequate generalization capabilities due to their heavy reliance on data. A critical limitation

The Land Surface Temperature (LST) is well suited to monitor biosphere–atmosphere interactions in forests, as it depends on water availability and atmospheric/meteorological conditions above and below the canopy. Satellite-based LST has proven integral in observing evapotranspiration, estimating surface heat fluxes and characterising vegetation properties. Since the radiative regime of forests is complex

Ocean primary production is a key process that regulates marine ecosystems and the global climate, but its estimation is still affected by multiple uncertainties. Typically, the chlorophyll-a concentration (CHL) is used to characterise this process, as it is considered as a proxy of phytoplankton biomass. To date, the most common observing systems for studying CHL are ocean colour satellites and Biogeochemical-Argo

Protected areas (PAs) in Amazon forests are vital in preserving tropical forest ecosystems and mitigating forest degradation. However, the increasing frequency and severity of fires in these regions necessitate a comprehensive understanding of post-fire vegetation recovery trajectories, which is essential to evaluate the effectiveness and resilience of PAs in the face of ongoing climate change. Recovery

Land Surface Phenology (LSP) has been widely derived from polar-orbiting satellite observations to characterize terrestrial vegetation dynamics. However, the uncertainty of LSP detections over large areas is always a big concern because of cloud contamination in the satellite time series, particularly in persistently cloudy regions. The Advanced Baseline Imager (ABI) onboard Geostationary Operational

Smoke from wildfires across North American high latitudes can travel long distances, degrading regional air quality. Hourly fire emissions are a crucial input of air quality models. However, they are unavailable for fires at high latitudes. The Advanced Baseline Imager (ABI) onboard NOAA's Geostationary Operational Environmental Satellites (GOES)-R Series satellites detects fires across North America

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, launched to the International Space Station on June 29, 2018, currently provides high spatial resolution thermal observations in five bands with a revisit time of 1–5 days. The ECO2LSTE product, which provides the land surface temperature (LST) and emissivity (LSE) retrieved using the temperature and emissivity

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a key indicator of photosynthetic activity and primary productivity in terrestrial ecosystems. While moderate-coarse spatial resolution FAPAR products have enabled global vegetation studies, their pixel sizes smooth fine-scale heterogeneity and limit applications needing a detailed spatial characterization. Landsat provides multispectral

Using satellite radar interferometry, we investigate surface deformation in the Great Aletsch Glacier region from 2015 to 2021. By applying a statistical blind source separation method on displacement timeseries, our study reveals irreversible trends near large slope instabilities, potentially indicating slope responses to the glacier’s retreat. Moreover, annual cyclic deformation indicates significant