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

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

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

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

A top-of-atmosphere (TOA) reflectance model based on the temporal characteristics of stable land surface targets, such as pseudo-invariant calibration sites (PICSs), was developed to monitor the stability of satellite visible imagers without requiring ground measurements. This model accounts for variations in the viewing zenith angle, solar zenith angle, and atmospheric parameters. To better represent

Accurate estimations of global land surface energy balance components [including net radiation (Rn), latent heat flux (LE), soil heat flux (G) and sensible heat flux (H)] are crucial for quantifying the exchange of heat and water between the land surface and atmosphere. In this study, a novel and practical model for Coordinated estimates of 4-day 500 m global land Surface Energy Balance components

Potential large seismic tsunamis could pose catastrophic damage to coastal cities, but conventional disaster risk assessment is difficult to realize precise management limited by the scale of publicly available data. This study aims to propose a refined probabilistic tsunami inundation risk assessment model assisted by high-resolution Sustainable Development Science Satellite 1 (SDGSAT-1) glimmer imagery

We compare five daily MODIS-derived snow albedo products to terrain-corrected, in situ data from sites in California and Colorado, USA, and to snow albedo derived from airborne hyperspectral imagery over several basins in California and Colorado. The MODIS-derived products we consider are NASA standard products MOD10A1, MCD43A3, and MCD19A3D, along with STC-MODSCAG/MODDRFS and MODIS SPIReS. These products

Satellite remote sensing has greatly advanced the study of land surface phenology, providing crucial insights into large-scale vegetation dynamics in the context of climate change. Multi-sensor satellite-derived vegetation proxies, such as the normalized difference vegetation index (NDVI) in optical mode, solar-induced chlorophyll fluorescence (SIF) in fluorescence mode, and vegetation optical depth

Satellite-derived land surface temperature (LST) is a directional variable and has significant angular anisotropy. This characteristic contributes to enhance the differences among different satellite-derived LST products, and therefore increases the challenge of using multi-sensor and multi-decadal data to provide a long-term and angle-consistent LST climate data record. The kernel-driven model can

Forest attribute maps relying on remotely sensed data are increasingly required for local decision-making related to the use of forest resources. Such maps always have uncertainty, which can be challenging to quantify. The objective of this work is to introduce the conformal prediction methodology to uncertainty quantification in forest attribute mapping, particularly for the k-NN method. We compare

Vegetation Optical Depth (VOD) serves as a crucial tool for monitoring vegetation characteristics and plays a vital role in terrestrial ecosystems. The vegetation optical depth dataset from AMSR-E/2 using multi-channel collaborative algorithm (MCCA-AMSR VOD) possesses a longer time series spanning from 2002 to 2022. However, due to the limitations of satellite orbital scanning gap and retrieval algorithms

Permafrost degradation on the Tibetan Plateau (TP) has triggered widespread retrogressive thaw slumps (RTSs), affecting hydrology, carbon sequestration and infrastructure stability. To date, there is still a lack of long-term monitoring of RTSs across the TP, the thaw dynamics and comprehensive driving factors remain unclear. Here, using time-series Landsat imagery and change detection algorithm, we

Wildfire maintains boreal forest health by catalyzing nutrient cycling and forest succession. However, increased annual burned area due to climate warming may facilitate forest loss and soil carbon release, which makes it important to monitor circumboreal burned area. Our goal was to characterize regional changes in circumboreal burned area from 1983 to 2020 using Advanced Very High Resolution Radiometer

Close-range LiDAR devices are considered to have great potential for enhancing forest inventory (FI) estimates. However, this potential is still being explored in the case of ground-based LiDAR devices, especially when the target is focused on relatively large spatial scales, such as stand level. This study explored the performance of close-range LiDAR devices in terms of bias and error, particularly

Accurate and timely forest disturbance mapping at large-scale is crucial for ecosystem protection and management. Sentinel-1 SAR data, with its all-weather capability and fine spatial-temporal resolutions, offers unique advantages for timely mapping of forest disturbance. Although deep learning models have been used for this purpose, they still struggle to fully exploit Sentinel-1 data's potential

The unprecedented urbanization in China has driven rapid urban and rural development in recent decades. While existing studies have extensively focused on horizontal urban expansion, research on vertical urban expansion patterns remains limited. To address this gap, this study proposed a Multi-Temporal Built-up Height estimation Network (MTBH-Net) to estimate 30-m China Multi-Temporal Built-up Height

We developed an ET model, namely the Enhanced and Spatial-Temporal Improvement of MODIS EvapoTranspiration (ESTIMET), for local-to-regional ET monitoring and applications in the tropics, based on the original MOD16 evapotranspiration (ET) algorithm. The main distinguishing features of ESTIMET are providing a near-real-time product with increased spatial (from 500 to 250 m) and temporal (from 8-day

The differential path tracing method and automatic differentiation can effectively calculate the derivatives of the loss function, enabling the estimation of surface properties such as reflectivity and transmissivity from sensor images. However, their full potential has not been completely explored in remote sensing. We developed a differentiable radiative transfer model (DRTM) to efficiently simulate

Landfast ice, sea ice fastened to coastal land or ice shelves, generally undergoes distinctive thermodynamic growth and less active dynamic deformation due to its prolonged attachment to the land, resulting in a thicker and smoother surface compared to drifting pack ice. In 2019, large landfast ice floes were detached from the Ronne Ice Shelf, and the broken pieces started to drift into the Weddell

Integrating passive microwave (PMW) and thermal infrared (TIR) remote sensing to generate all-weather land surface temperature (LST) is essential for effective land thermal monitoring. Previous studies have attempted to adapt TIR-interactive kernel-driven downscaling techniques into the PMW-TIR integration process. However, large-scale spans often introduce significant uncertainties in the generated

Leaf chlorophyll content (LCC) is an important indicator of photosynthetic capacity. Sun-induced chlorophyll fluorescence (SIF) is an optical signal emitted from the leaf interior, providing a unique technique for accurately estimating LCC. The far-red to red ratio of chlorophyll fluorescence (Fratio) has been used to empirically estimate LCC in some previous studies. While these studies support the

Aerosol pollution significantly influences the interaction between solar radiation and the earth's atmosphere and seriously threatens human health. Numerous studies have applied machine learning models such as Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) to estimate aerosol-related parameters, including aerosol optical depth and particulate matter concentrations

Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multi-component analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based

Ocean remote sensing data is crucial in understanding the global climate system. Due to satellite orbital coverage gaps and cloud cover, satellite ocean remote sensing products have significant data gaps. This paper introduces a Generalized Data Completion Model (GDCM) based on deep learning to reconstruct gap-free and cloud-free key oceanic variables such as sea surface temperature (SST), wind speed

Maritime transportation is a substantial contributor to anthropogenic NOx emissions and coastal air pollution. Recognizing this, the International Maritime Organization (IMO) has steadily implemented stepwise stricter emission standards for ships in recent years. However, monitoring emissions from sea-bound vessels poses inherent challenges, prompting the exploration of satellite observations as a

Air quality in northern South America faces significant challenges due to insufficient high-resolution emission inventories and sparse atmospheric studies. This study addresses these gaps by developing a novel framework that integrates high-resolution nighttime light data from SDGSAT-1 and multisource remote sensing datasets with deep learning techniques to downscale emission inventories. The refined