Land surface temperature (LST) derived from satellite observations and weather modeling has been widely used for investigating Earth surface-atmosphere energy exchange and radiation budget. However, satellite-derived LST has a trade-off between spatial and temporal resolutions and missing observations caused by clouds, while there are limitations such as potential bias and expensive computation in

The fraction of green vegetation is a widely-used indicator of vegetation abundance at regional and/or global scales. The pixel mixture model, especially the dimidiate pixel model (DPM, also referred to as two-endmember model) based on the normalized difference vegetation index (NDVI), plays an important role in the accurate estimation of fractional vegetation cover (FVC) remote sensing. The two components

Satellite altimetry data has become essential for studying the dynamics of water bodies, especially in regions with limited or inaccessible data. Traditional low-resolution mode (LRM) satellites' accuracy cannot be guaranteed when it comes to assessing water levels in small- (< 200 m in width) and medium-sized (200–800 m in width) rivers. Synthetic aperture radar (SAR) altimeters, exemplified by Sentinel-3 A

Volcanic CO is widely acknowledged as an important geochemical precursor for volcanic activity; however, obtaining observations through remote sensing remains a challenge. It is well established that volcanic CO diffusely degases during magma ascent, and the volatiles interact with the ecosystem on the surface through CO fertilization, which can improve vegetation health. A normalized difference vegetation

Although wetlands are widely recognized for thier important role in providing ecosystem services, their abundance, spatial extent, and condition remain poorly constrained and at-risk of decline. Accurate mapping and monitoring are therefore essential for their protection. However, distinguishing swamps from upland forests and shrublands is especially challenging because optical sensors cannot detect

The Surface Water and Ocean Topography (SWOT) mission, originally designed for observing ocean and inland water bodies, can be a valuable tool for mapping the topography of intertidal flats. This study provides the first demonstration of SWOT's ability to measure intertidal topography using simultaneous interferometric acquisitions. Observations acquired between April and July 2023, during the calibration/validation

Precise and timely information about crop types plays a crucial role in various agriculture-related applications. However, crop type mapping methods often face significant challenges in cross-regional and cross-time scenarios with high discrepancies between temporal-spectral characteristics of crops from different regions and years. Unsupervised domain adaptation (UDA) methods have been employed to

Flood emergency mapping is essential for flood management, often requiring near real-time extraction of large-scale flood extents by combining pre- and post-event multi-source remote sensing images. Pre-event optical imagery delineates the normal water extent, providing a benchmark for estimation of post-flood changes. Synthetic aperture radar (SAR) imagery provides rapid and accurate interpretation

Phase unwrapping is the process of retrieving the true phase values from observed wrapped phases by adding the correct multiples of 2π. This process is crucial in synthetic aperture radar (SAR) interferometry, and numerous studies have aimed to enhance its performance. This study explored phase unwrapping using a modified U-Net regression model by optimizing both the network structure and training

Solar-induced chlorophyll fluorescence (SIF) is a subtle but informative probe of plant photosynthesis. Quantifying the three-dimensional (3D) distribution of SIF benefits a better understanding of photosynthesis variations over heterogeneous canopies. Although radiative transfer models (RTMs) provide a solid theoretical basis for simulating the 3D SIF distribution, most RTMs use virtual scenes with

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy

For over four decades, spaceborne multispectral data have played a crucial role in supporting mineral exploration and geologic mapping. The spaceborne multispectral datasets, however, have a restricted number of bands with coarse spectral resolution and, thus are very limited in mineral mapping. The advent of high-quality spaceborne imaging spectroscopic data like the Environmental Mapping and Analysis

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed , a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number

We developed the SCARF (Spatial Mismatch and Systematic Prediction Error Corrected cAscade Random Forests) algorithm for continuous prediction of biomass dynamics using machine learning and Landsat Time Series (LTS). Our approach addresses the challenges posed by the cloudy subtropical forests in southern China, where monitoring biomass dynamics is notoriously difficult. To derive spectral-temporal

Accurate spatio-temporal information on rice cropping patterns is vital for predicting grain production, managing water resource and assessing greenhouse gas emissions. However, current automated mapping of rice cropping patterns at regional scale is heavily constrained by insufficient training samples and frequent cloudy weathers in major rice-producing areas. To tackle this challenge, we proposed

Mesoscale dipoles compose two counter-rotating mesoscale eddies. They are observed in oceans worldwide and significantly impact the biogeochemical cycle, marine ecosystems, and ocean water transport. This study investigates the evolution of asymmetric dipole eddies based on the satellite altimeter data for the period January 1993–December 2020. The coupling and decoupling of eddy dipoles impact various

Synthetic Aperture Radar (SAR) is a unique remote sensing instrument imaging ocean surface waves in two dimensions with high spatial resolution regardless of sunlight and weather conditions. However, due to the nonlinear imaging process, the ocean wave spectra cannot be retrieved directly from SAR data. The emergence of deep learning (DL) techniques provides a new paradigm for addressing this challenge

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations

As one of the most important staple foods globally, rice sustains nearly half of the world's population. Accurate and timely paddy rice mapping is, thus, essential for rice-related policy-making to ensure food security in the context of anthropogenic, environmental and climate changes. However, paddy rice mapping remains a challenging task since it usually has similar spectral characteristics to other

Urbanization has exerted considerable impacts on urban water systems and ecological environments, yet its effects on local meteorological drought remain under-explored. The primary challenge to local-scale drought analysis is the scarcity of meteorological datasets with sufficient spatial and temporal resolution. To address the research gap, we initially proposed a two-step fusion framework, integrating

Changes in the net carbon sink of boreal forests constitute a major source of uncertainty in the future global carbon budget and, hence, climate change projections. The annual net ecosystem exchange of carbon dioxide (CO) controlling the terrestrial carbon stock results from the small difference between respiratory CO release and the photosynthetic CO uptake by vegetation. The boreal forest, and the

Spaceborne lidar (light detection and ranging) instruments such as the Global Ecosystem Dynamics Investigation (GEDI) provide a unique opportunity for global forest inventory by generating broad-scale measurements sensitive to the vertical arrangement of plant matter as a supplement to measurements. Lidar measurables are not directly relatable to most physical attributes of interest, including biomass

Global navigation satellite system reflectometry (GNSS-R) has emerged as a new technique to provide L-band bistatic measurements for ocean wind speed retrieval, in which traditional geophysical model functions (GMFs) or shallow neural networks (NNs) are normally used. However, it is still challenging to identify and consider all relevant parameters in the GMF. Meanwhile, NN models face limitations

Effective monitoring of soil and vegetation properties on a global scale is essential for better understanding climate changes, hydrological dynamics, and ecological processes. Passive microwave remote sensing at C-band radio frequency, with long observation period and relatively high penetration capability, has been widely used to retrieve soil moisture (SM) and vegetation optical depth (C-VOD). The

This paper is the second part of companion papers describing the development of GRASP approach for aerosol and surface retrieval from Sentinel-5P/TROPOMI. Here we focus on the S5P/TROPOMI GRASP aerosol and surface products global validation and systematic intercomparison with other products from independent instruments and algorithms. Specifically, we have validated the S5P/TROPOMI GRASP, Suomi-NPP/VIIRS

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for

A dataset of sub-daily C-band data, acquired with a ground-based synthetic aperture radar, has been used to study soil and vegetation dynamics during a complete growing season in a controlled agricultural test site. The data have been exploited to analyse the rate and sources of decorrelation in the scene, as well as the consequences of the observation conditions of a sub-daily satellite (with either

Accurate measurement of water levels is essential for effectively managing reservoirs to proactively mitigate flooding and drought. Nonetheless, the inaccuracies in measurements derived from gauging station and remote sensing images impose constraints to water resource management. In this study, we developed a novel water level estimation model which utilizes solely the altitude of reliable water boundary

Mapping and monitoring groundwater-dependant vegetation (GDV) and springs is essential for understanding the dynamics of shallow groundwater systems and for developing ecosystem conservation strategies. While large-scale identification of GDV and springs often relies on mapping excess evaporation, it is low resolution, and it can be prone to false detections over some types of soils and in areas of

Atmospheric aerosols have strong impact on climate, environment, and health. To account correctly for such impact, extended aerosol characterization, including spectral Aerosol Optical Depth (AOD), Angstrom Exponent (AE), spectral Single Scattering Albedo (SSA) etc., are required to be derived globally from space-borne observations. Together with the aerosol, the Earth's surfaces are an important component

Transparent, accurate, and affordable monitoring of CO emissions from power plants is crucial for realization of transition away from fossil fuels. Satellite remote sensing has emerged as a recent research focus, enabling emission estimation without the cooperation of emitters. In 2022, China launched the world's first CO monitoring satellite based on laser detection, DQ-1. Equipped with ACDL, it enables

Human-transformed agricultural and aquacultural ponds, collectively referred to as “dike-pond systems”, play a crucial role in ensuring food security but also contribute to the widespread loss of natural wetlands. However, spatially and temporally explicit patterns of dike-pond systems have not been thoroughly documented globally. Here, we map the distribution of dike-pond systems over both global

This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this

P-band (0.3–1 GHz) radiometry shows promise in enhancing the next generation of radiometer missions for global soil moisture and vegetation optical depth (VOD) monitoring, particularly in moderately to densely vegetated areas, which remain challenging for current L-band (1.4 GHz) missions such as Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP). However, the superiority

Land surface temperature (LST) is an essential climate variable (ECV) which can be estimated from appropriate measurements of the surface thermal infrared (TIR) radiance. LST varies on a very short time scale and closely depends on the illumination and scan angles considered. To fully exploit LST products, a method for reconstructing the temporal profile and the angular dependence at the same time

Accurate estimation of sub-surface properties such as moisture content and depth of soil and vegetation layers is crucial for applications spanning sub-surface condition monitoring, precision agriculture, and effective wildfire risk assessment. Soil in nature is often covered by overlaying vegetation and surface organic material, making its characterization challenging. In addition, the estimation

River ice plays a crucial role in hydrology, ecosystems, and socio-economic systems in mid-to-high latitude regions. However, there is a notable gap in the analysis of patterns and trends in river ice phenologies and their influence factors at the river level. In this study, we developed a practical method to identify river ice phenologies by leveraging Landsat observations from 2000 to 2021, and established

Terrestrial water storage anomaly (TWSA) and groundwater storage anomaly (GWSA) data are of great importance for hydrological research and water resource management. However, products derived from the Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On mission (GRACE-FO) are constrained by the satellite design and variation in processing strategies among different institutions

Rock glaciers, unique creeping periglacial landforms, have garnered research interest for their valuable insights into hydrological resources and representing potential geological hazards in the context of climate warming. However, owing to the scarcity of high-spatiotemporal-resolution kinematic observations, the mechanisms driving rock glacier movements remain poorly understood. This study introduces

Managing unregulated dumpsites in urban environments is a pressing issue, but determining their locations presents persistent challenges due to their irregular distribution. Recently, researchers have begun employing aerial view remote sensing imagery coupled with deep learning detection to identify open dumpsites. However, due to interference from scenes with similar visual features in the satellite

Downward shortwave radiation (DSR) is the main energy source for the Earth's system and a dominant component of the radiation budget. Although many algorithms have been proposed for estimating DSR, most models only consider either the radiative effects of terrain or atmosphere, and no research has considered the conditions of clouds on the mountainside. In this paper, a high-resolution Mountain Radiative

The Atmospheric Laser Doppler Instrument (ALADIN) onboard the Aeolus wind mission was the first High Spectral Resolution Lidar operating in the Ultra Violet (UV) region deployed in space. This study explores and documents the feasibility of deriving ocean optical properties using data from ALADIN. A three steps (i.e. data screening, analytical estimation of the total in-water signal contribution, Look

The choice of ice particle model is critical for microwave remote sensing of ice clouds. Using a model-observation closure method, the global applicability of a series of single crystal and aggregate ice particle models is evaluated based on satellite-based microwave measurements from the CloudSat Cloud Profiling Radar (CPR) and the Global Precipitation Mission (GPM) Microwave Imager (GMI) high-frequency

Sea surface temperature (SST) is an important parameter affecting global climate, weather disasters, and marine resources. Acquiring SST data that covers large areas and spans over long periods is one of the most essential tasks for various scientific research. During the past decades, meteorological satellites (e.g., the Himawari 8) have been able to provide large-scale, high-resolution continuous

The maximum carboxylation rate of plant leaves () at 25 °C () is a fundamental parameter in terrestrial biosphere models (TBMs) to estimate carbon assimilation of C3 biomes. It has been reported that ignoring the seasonal variations in induces considerable uncertainties in TBMs. Recently, a model was developed to estimate of C3 biomes mechanistically from climate data based on eco-evolutionary optimality

Information on tree species across various spatial scales, from an individual tree to a forest stand and the broader landscape, contributes to an accurate and thorough understanding of forest conditions either as an individual characteristic or as an input of species-dependent models. However, tree species recognition is one of the most challenging tasks in forest remote sensing studies, due to the

Plant adaxial (upper) and abaxial (lower) leaf sides have different reflectances and transmittances, and this has been explored and modeled in several studies. The emitted chlorophyll fluorescence is also not identical when different leaf sides are illuminated; although this has already been discovered, its effects on canopy-level fluorescence have not been explored, and there is a lack of models for

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model

With the upcoming spaceborne synthetic aperture radar (SAR) missions (BIOMASS, LuTan-1, NISAR, and TanDEM-L), it will become possible to extract vegetation height at a global scale by utilizing spaceborne low-frequency (L- and P-band) polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, in the context of single-baseline parameter retrieval by the random volume over ground

Intertidal areas, which emerge during low tide, form a vital link between terrestrial and marine environments. Seagrasses, a well-studied intertidal habitat, provide a multitude of different ecosystem goods and services. However, owing to their relatively high exposure to anthropogenic impacts, seagrasss meadows and other intertidal habitats have seen extensive declines. Remote sensing methods that

Existing, state-of-the-art vegetation models disagree by a factor of four on the seasonal amplitude of the global, terrestrial carbon cycle. This seasonal amplitude is likely increasing over time due to climate change, and disagreements among vegetation models therefore complicate efforts to quantify how climate change is impacting the carbon cycle. We evaluate the seasonal cycle of terrestrial CO

Satellite-derived vegetation indices (VIs) have been extensively used in monitoring vegetation dynamics at local, regional, and global scales. While numerous studies have explored various factors influencing VIs, a remarkable knowledge gap persists concerning their applicability in mountain areas with complex topographic variations. Here we bridge this gap by conducting a comprehensive evaluation of

In this era of global warming, the regular and accurate mapping of vegetation conditions is essential for monitoring ecosystems, climate sustainability and biodiversity. In this context, this work proposes a physics-guided data-driven methodology to invert radiative transfer models (RTM) for the retrieval of vegetation biophysical variables. A hybrid paradigm is proposed by incorporating the physical

In the context of global urbanization, unplanned urban expansion renders cities particularly susceptible to the impacts of climate change, natural disasters, and extreme heat and humidity. Monitoring the land surface temperature (LST) in urban areas is crucial for assessing the urban thermal environment. Fine-scale (<100 m) LST products are essential for comprehensively understanding urban environments

Large areas of Europe have been repeatedly affected by severe droughts. Stressed trees suffered from direct drought impacts such as water stress or heat and were also more susceptible to other biotic and abiotic stress agents and calamities. Monitoring such vulnerable forests area-wide is crucial to assess the highly dynamic climate change induced impacts not captured by traditional ground-based monitoring

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky

Extracting river networks that are both accurate and topologically connected is important for applications that involve correct routing of material, for example water and sediment, through such networks. We combined water and sediment extraction using radar and multispectral imagery from Sentinel-1 and Sentinel-2 to create both water and sediment masks over a range of study areas. These were then used

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD)

This paper presents the Nighttime Ocean Aerosol Optical Depth (AOD) Retrieval Algorithm (NOARA), an innovative method for deriving the oceanic AOD from the reflected moonlight observation of Day-Night Band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) over ocean surfaces. Building upon the recent development of nighttime AOD retrievals for rural/wild areas over land, the NOARA

Accurately assessing forest structures at a national level is essential for promoting sustainable development, managing forests effectively, and fulfilling commitments under the Paris Agreement to combat climate change. In Japan, the traditional method of estimating forest carbon stock through National Forest Inventories (NFI) has certain limitations. The limited scope of forest survey data samples