Aerosol pollution is an acute environmental issue in developing countries. Asia has been experiencing rapid changes in anthropogenic aerosols during the past two decades due to fast growth in population and economy. It is still an open question how aerosol loadings, represented by aerosol optical depth (AOD), have evolved in this century, particularly during the past decade when China and India implemented

Increased focus on restoring forest structural variation and spatial pattern in dry conifer forests has led to greater emphasis on forest monitoring strategies that can be summarized across scales. To inform restoration objectives with data sources that can characterize individual trees, groups of trees, and the entire stand, different remote sensing strategies such as aerial and terrestrial light

The ability of a forest to buffer understory temperature extremes depends on the canopy structure, which is often measured from the ground. However, ground measurements provide only point estimates, which cannot be used for spatially explicit microclimate modeling. Canopy structures derived from airborne light detection and ranging (LiDAR) can overcome these limitations, but high point-density LiDAR

This paper aims to quantify the precision of retracked Jason-2 altimeter sea level data (2008–2016) in the 0–5 km Australian coastal zone. A new retracking strategy has been developed to achieve the highest possible precision of coastal sea-level data. Coastal waveforms were classified into four groups according to the adaptability of dedicated coastal retrackers, from which each group was reprocessed

Accurate estimation of aerial net primary production (ANPP) using remotely acquired data is one of the main challenges in both environmental monitoring and precision agriculture. Reflectance-based techniques have been widely used for decades, but detection of fluorescence emission by chlorophyll has emerged as a promising alternative in recent years. Although passive sun-induced fluorescence (SIF)

In the recent decades, satellite monitoring techniques have enhanced the discovery of non-catastrophic slope movements triggered by earthquake shaking involving old paleo-landslides with deep-seated sliding surfaces. Understanding the triggering and attenuation mechanisms of such mass movements is crucial to assessing their hazard. In December 2018, Etna volcano (southern Italy) began a very intense

Random error in remotely sensed (RS) and modeled soil moisture (SM) products is typically assumed to be statistically stationary for the purpose of SM merging applications. In reality, such error is often non-stationary, which may undermine applications based on a stationary assumption. Here, we introduce a dual time-space (2D) SM merging approach that considers a class of inferred error that is non-stationary

Anthropogenic heat (AH) significantly impacts urban climates. Although combining the surface energy balance (SEB) with remote sensing data (RS-SEB) is promising for AH quantification, it has been shown to yield paradoxical low AH values in urban centers. Some speculation on the causes for the underestimation has appeared in the literature; however, none has been verified or thoroughly evaluated, largely

With the launch of the Observing Carbon Observatories (OCO-2 and OCO-3), we have entered a new era of greenhouse gas (GHG) data collection where sub-city scale data can be collected at varying times across the entire globe. An increasing focus on quantifying urban emissions of GHGs from policy makers has begun to spur new research into how best to use this unique and very high resolution data set.

Timely and reliable acquisition of built-up areas (BAs) information in large regions is of great significance for the assessment of the influence of human activities in local, regional and even global environments and promotes the sustainable development of cities. In this study, a deep learning based framework is presented to automatically extract BAs from Gaofen-3 (GF-3) data in China. In the framework

The monitoring of forested ecosystems relies on an accurate description of forest structure. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2), launched in September 2018, carries the Advanced Topographic Laser Altimeter System (ATLAS), a Light Detection and Ranging (LiDAR) instrument capable of detecting individual photons reflected back from vegetation canopy. ICESat-2 data is delivering global

Large-scale high-resolution satellite observations of plant functional diversity patterns will greatly benefit our ability to study ecosystem functioning. Here, we demonstrate a potentially scalable approach that uses aggregate plant traits estimated from radiative transfer model (RTM) inversion of Sentinel-2 satellite images to calculate community patterns of plant functional diversity. Trait retrieval

Measurements made by the MOPITT (“Measurements of Pollution in the Troposphere”) instrument on the NASA Terra polar-orbiting platform enable the retrieval of tropospheric carbon monoxide (CO) concentrations. As determined by the Terra orbit and MOPITT swath width, the frequency of MOPITT observations at a specific location, or measurement sampling frequency, is typically about once every three to four

Secondary succession (SS) is one of the main consequences of the abandonment of agricultural and forestry practices in rural areas, causing -among other processes- woody encroachment on former pastures and croplands. In this study we model and monitor the spatial evolution of SS over semi-natural grassland communities in the mountain range of the Pyrenees in Spain, during the last 36 years (1984-2019)

The Landsat satellite data applications have been broadened by the improvement in data consistency, e.g., advances in calibration and atmospheric correction algorithms. However, there are still inter-sensor inconsistencies of surface reflectance caused by differences in sensor spectral response and residuals in calibration and atmospheric correction. Such inconsistencies tend to entangle with the land

Accurate prediction of crop yield is essential for agricultural trading, market risk management and food security. Although various statistical models and machine learning models have been developed to enhance prediction accuracy, spatial and temporal non-stationarity, an intrinsic attribute of many geographical processes, is still rarely considered in crop yield modeling. From a statistical point

Daily continuous evapotranspiration (ET) estimates of 1 km spatial resolution can benefit agricultural water resources management at regional scales. Thermal infrared remote sensing-derived land surface temperature (LST) is a critical variable for ET estimation using energy balance-based models. However, missing LST information under cloudy conditions remains a long-standing barrier for spatiotemporally

Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics

Disturbances have increased in Central Europe's forests, but whether changes in disturbance regimes are driven by natural or human causes remains unclear. Satellite-based remote sensing provides an important data source for quantifying forest disturbance change. Separating causes of forest disturbance is challenging, however, particularly in areas such as Central Europe where disturbance patches are

The PRISMA satellite mission launched on March 22nd, 2019 is one of the latest spaceborne imaging spectroscopy mission for Earth Observation. The PRISMA satellite comprises a high-spectral resolution VNIR-SWIR imaging spectrometer and a panchromatic camera. In summer 2019, first operations during the commissioning phase were mainly devoted to acquisitions in specific areas for evaluating instrument

Plant nitrogen (N) use is an essential component of the N cycle in Arctic terrestrial ecosystems, and important processes include plant N uptake and reallocation during the growing season. While the availability of N to deciduous tundra plants in part relies on their internal reallocation of N from leaves to stems and roots during autumn senescence, the species-specific importance of reallocation timing

This study applied a Spike-Filter (SF) method to identify and remove rapidly moving clouds over the coastal ocean in GOES-16 satellite sea surface temperature fields (SST). These images were then gap-filled to capture upwelling in the Mid Atlantic Bight (MAB), a critical process that impacts ecosystems and atmospheric processes in the region and is important to coastal regions around the world. During

Sun-induced fluorescence (SIF) retrieved from satellite measurements has been widely used as proxy for chlorophyll-a concentration and as indicator of phytoplankton physiological status in oceans. The practical use of this naturally occurring light signal in environmental research is, however, under-exploited, particularly in research focusing on optically complex waters such as inland and coastal

Strong ground vibration triggered by seismic waves is one of the most significant factors that causes building damage during a seismic event. The interferometric synthetic aperture radar (InSAR) technique can efficiently and quickly provide seismic deformation information. There is no clear relationship model between seismic deformation and the degree of building damage. Considering the earthquake

We develop a Bayesian Land Surface Phenology (LSP) model and examine its performance using Enhanced Vegetation Index (EVI) observations derived from the Harmonized Landsat Sentinel-2 (HLS) dataset. Building on previous work, we propose a double logistic function that, once couched within a Bayesian model, yields posterior distributions for all LSP parameters. We assess the efficacy of the Normal, Truncated

The terrestrial component of the Earth system has witnessed considerable changes in the past decades due to anthropogenic action. Throughout this period, the NASA Terra mission has been constantly monitoring the surface with the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. When combined with the MODIS instrument on-board of the Aqua platform, we obtain a hyper-frequent revisit

Cloud detection is not only a challenging task, but it also plays a major role in image processing. Due to the diversity of the cloud and the complexity of underlying surfaces, most of the current cloud detection methods still face great challenges, especially in detecting thin cloud. Therefore, we propose a method to detect cloud pixels in the GaoFen-1 WFV images. In our method, a deep network is

Arctic glaciers and ice caps (GIC) are losing mass rapidly, and this process is expected to continue during the 21st century owing to polar amplification of climate warming. Here, we use seven years of CryoSat-2 swath interferometric altimetry to track changes in the volume of Arctic GIC. From these data, we produce a pan-Arctic assessment of GIC mass imbalance, and we partition their losses into signals

After reflecting on the past decomposition studies using the polarimetric synthetic aperture radar (PolSAR) technique and data in Earth observation (EO) studies, three primary issues are identified. Elements C12 and C32 of a covariance matrix, [C], are essential in the decomposition and cannot be ignored. Existing algorithms cannot adequately distinguish urban targets with large azimuth orientation

Three-dimensional (3D) vegetation canopy structure plays an important role in the way radiation interacts with the land surface. Accurately representing this process in Earth System models (ESMs) is crucial for the modeling of the global carbon, energy, and water cycles and hence future climate projections. Despite the importance of accounting for 3D canopy structure, the inability to represent such

Estimates of surface reflectance from observed radiance are fundamentally tied to the accuracy of the radiative transfer models used to simulate the interaction of light with the atmosphere and surface. These radiative transfer models are parameterized by a wide range of quantities, ranging from observation and solar geometries to assumptions about atmospheric conditions, including vertical distributions

Surface soil moisture (SM) retrieval over agricultural areas from polarimetric synthetic aperture radar (PolSAR) has long been restricted by vegetation attenuation, simplified polarimetric scattering modelling, and limited SAR measurements. This study proposes a modified polarimetric decomposition framework to retrieve SM from multi-incidence and multitemporal PolSAR observations. The framework is

Urban functional zones (UFZs) are essential for characterizing both urban spatial configurations and socio-economic properties and monitoring urbanization process, thus UFZs are fundamental to urban planning, management and renewal. Although many efforts in remote sensing field have been made to map UFZs, large-scale and fine-grained UFZ maps required by a broad range of urban applications are still

Long-term changes in the marine ecosystems of the Eastern Boundary Upwelling Systems (EBUS) are predicted due to anthropogenic climate change. In particular, global ocean acidification is having a profound effect on the coastal waters of the EBUS, affecting the entire trophic chain, net primary production (NPP) and related economic activities such as fisheries. Another predicted change related to human

Wildfires are increasing in intensity and frequency across the globe due to climate change and rising global temperature. Development of novel approach to Monitor wildfire progressions in near real-time is therefore of critical importance for emergency responses. The objective of this research is to investigate continuous learning with U-Net by exploiting both Sentinel-1 SAR and Sentinel-2 MSI time

Land surface phenology (LSP) is a consistent and sensitive indicator of climate change effects on Earth's vegetation. Existing methods of estimating LSP require time series densities that, until recently, have only been available from coarse spatial resolution imagery such as MODIS (500 m) and AVHRR (1 km). LSP products from these datasets have improved our understanding of phenological change at the

Information on forest disturbance is crucial for tropical forest management and global carbon cycle analysis. The long-term collection of data from the Landsat missions provides some of the most valuable information for understanding the processes of global tropical forest disturbance. However, there are substantial uncertainties in the estimation of non-mechanized, small-scale (i.e., small area) clearings

With a large part of the world's population residing in coastal areas, and largely depending on the coastal environment, monitoring natural and human-induced coastal changes are paramount to understand the dynamic and vulnerability of these coastal systems/communities. To understand changes in coastal areas, e.g. environmental and social resilience to environmental change, local measurements are inadequate

Although the statistical significances for the trends of integrated water vapor (IWV) are essential for a correct interpretation of climate change signals, obtaining accurate IWV trend estimates with realistic uncertainties remains a challenge. This study evaluates the feasibility of the IWV trends derived from the newly released fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF)

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of

A comprehensive in situ dataset of chlorophyll a (Chl a; N = 18,001), net primary production (NPP; N = 165) and net community production (NCP; N = 95), were used to evaluate the performance of Moderate Resolution Imaging Spectroradiometer on Aqua (MODIS-A) algorithms for these parameters, in the South Atlantic Ocean, to facilitate the accurate generation of satellite NCP time series. For Chl a, five

This paper presents a method for joint retrieval of the ocean surface wind and current vectors using the backscatter and the Doppler frequency shift measured by spaceborne single-beam single-polarization synthetic aperture radar (SAR). The retrieval method is based on the Bayesian approach with the a priori information provided by atmospheric and oceanic models for surface wind and currents, respectively

Vegetation phenology obtained from time series of remote sensing data is relevant for a range of ecological applications. The freely available Sentinel-2 imagery at a 10 m spatial resolution with a ~ 5-day repeat cycle provides an opportunity to map vegetation phenology at an unprecedented fine spatial scale. To facilitate the production of a Europe-wide Copernicus Land Monitoring Sentinel-2 based

Accurate knowledge of the distribution, breadth and change in agricultural activity is important to food security and the related trade and policy mechanisms. Routine observations afforded by spaceborne Synthetic Aperture Radar (SAR) allows for high-fidelity monitoring of agricultural parameters at the field scale. Here we evaluate the approach to be used for generating NASA's upcoming NASA ISRO SAR

Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical

In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned area (BA) mapping combining radar and optical datasets acquired by Sentinel-1 and Sentinel-2 on-board sensors, respectively. Combining active and passive datasets into a seamless wall-to-wall cloud cover independent

Remote imaging spectroscopy, also known as hyperspectral imaging, uses Radiative Transfer Models (RTMs) to predict the measured radiance spectrum for a specific surface and atmospheric state. Discrepancies between RTM assumptions and physical reality can cause systematic errors in surface property estimates. We present a statistical method to quantify these model errors without invoking ground reference

McArthur's foliage height diversity (FHD) has been the gold standard in the determination of structural complexity of forests characterized by LiDAR vertical height profiles. It is based on Shannon's entropy index, which was originally designed to describe evenness in abundances among qualitative typologies, and thus the calculation of FHD involves subjective layering steps which are essentially unnatural

Satellite remote sensing can detect and predict subsurface temperature and salinity structure within the ocean over large scales. In the era of big ocean data, making full use of multisource satellite observations to accurately detect and predict global subsurface thermohaline structure and advance our understanding of the ocean interior processes is extremely challenging. This study proposed a new

Tropical Montane Cloud Forests (TMCFs) form biodiverse communities that are characterized by frequent occurrence of low-level clouds from which they capture a substantial proportion of their precipitation — here referred to as occult precipitation. TMCFs provide important ecosystem services, in particular the supply of water to their wider surroundings. Throughout the tropics (here 23.5∘ S to 23.5∘

The timing of leaf emergence in temperate and boreal forests is changing, which has profound implications for a wide array of ecosystem processes and services. Spring phenology models, which have been widely used to predict the timing of leaf emergence, generally assume that a combination of photoperiod and thermal forcing control when leaves emerge. However, the exact nature and magnitude of how photoperiod

The bulk surface properties, including canopy height (h), zero-plane displacement height (d), roughness length (z0), and aerodynamic resistance (rb and rd), are crucial biophysical parameters that influence momentum, energy and mass exchanges at the land-atmosphere interface. The variabilities of these parameters are important for understanding possible impacts of the ecosystem on climate change, yet

Submerged aquatic vegetation (SAV) is one of the main producers in inland lakes. Tracking the temporal and spatial changes in SAV is crucial for the identification of state changes in lacustrine ecosystems, such as changes in light, nutrients, and temperature. However, the available SAV classification algorithms based on remote sensing are highly dependent on field survey data and/or human interventions

This paper demonstrates the capability and performance of sea surface wind speed retrieval in coastal regions (within 200 km away from the coastline) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data from NASA's Cyclone GNSS (CYGNSS) mission. The wind speed retrieval is based on the Artificial Neural Network (ANN). A feedforward neural network is trained with the collocated

Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issue for heterogeneous landscapes, such as tree-grass ecosystems (TGE). In these areas, while appearing

We present a joint exploitation of space-borne and ground-based Synthetic Aperture Radar Interferometry (InSAR) and Multi Temporal (MT) InSAR measurements for investigating the Stromboli volcano (Italy) deformation phenomena. In particular, we focus our analysis on three periods: a) the time interval following the 2014 flank eruption, b) the July–August 2019 eruption and c) the following post-eruptive

Generation of consistent multi-sensor datasets is critical to the assessment of long-term global changes using satellite-borne instruments. Recent research suggests, however, that a fundamental assumption in satellite ocean color data processing concerning the calibration of the long near infrared band (i.e., 865 nm for MODIS) may introduce sensor-specific biases in space and/or time, which may also