The phenological dynamics of crops reflect the response and feedback of agricultural systems to climate and environmental constraints, and have significant controls on carbon and nutrient cycling across the globe. Remote monitoring of crop phenological dynamics in a consistent and systematic manner is vitally crucial for optimizing the farm management activities and evaluating the agricultural resilience

Surface soil moisture is a vital variable in the process of energy exchange between the land and atmosphere. Monitoring the surface soil moisture at the local and global scales has become feasible due to the development of microwave remote sensing. With the new development of potential satellite missions, it is important to evaluate existing soil moisture retrieval algorithms, which will greatly contribute

Precipitable water vapor (PWV) is among the key variables in the water and energy cycles, whereas current PWV products are limited by spatiotemporal discontinuity, low accuracy, and/or coarse spatial resolution. Based on two widely used global PWV products, i.e., satellite-based MODIS and reanalysis-based ERA5 products, here we propose a data fusion approach to generate PWV maps of spatiotemporal continuity

Soil Moisture (SM) is a direct indicator of dryness of the land surface, and the amount of precipitation (P), vegetation status, and Land Surface Temperature (LST) are directly related to SM; thus, these factors indirectly characterize the dryness of the land surface. However, there are limitations and shortcomings of using a single factor to assess dryness because of the interactions among factors

Topography is one of the key factors that impact remotely sensed data and their interpretation. Indeed, combined with the viewing geometry and neighbour effects, it strongly affects the direct, diffuse and multi-scattered scene irradiance, which in turn impacts the radiative budget and remote sensing signals of the landscapes. The increased availability of digital elevation models (DEM) and the advancement

Accurate crop mapping provides important and timely information for decision support on the estimation of crop production at large scale. Most existing crop-specific cover products based on remote sensing data and machine learning algorithms cannot serve large agriculture production areas as a result of poor model transfer capabilities. Developing a generalizable crop classification model for spatial

Water clarity is a well-established first-order indicator of water quality and has been used globally by water regulators in their monitoring and management programs. Assessments of water clarity in lakes over large temporal and spatial scales, however, are rare, limiting our understanding of its variability and the driven forces. In this study, we developed and validated a robust Secchi disk depth

Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce

The Sustainable Development Goal (SDG) 6.3.2 of the United Nations (UN) focuses on ambient water quality, while water clarity simplistically and visually reflect water quality and can potentially support SDG 6.3.2 reporting. In this study, based on extensive field data and Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery, a random forest regression (RFR) Secchi depth (Zsd) model suitable for

Sentinel-1 Synthetic Aperture Radar (SAR) data have provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol (VV-VH) Sentinel-1 SAR data are being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which are providing Sentinel derived information to support crop monitoring networks

In this paper, an effective schematic is developed for estimating soil moisture (SM) from CYclone Global Navigation Satellite System (CYGNSS) data. Here, a three-layer model of air, vegetation cover, and soil is considered. In practice, the surface reflectivity (Γ) along with its statistics derived from the CYGNSS data and the ancillary vegetation opacity (τ) data from Soil Moisture Active Passive

The impacts of climate change and extreme weather events (e.g. frost-, heat-, drought-, and heavy rainfall events) on the continuous phenological development over the entire seasonal cycle remained poorly understood. Previous studies mainly focused on modeling key phenological transition dates (e.g. discrete timing of spring bud-break and fall senescence) based on aggregated climate variables (e.g

A method to differentiate open water from sea ice using quad-polarization C-band synthetic aperture radar (SAR) imagery is presented, based on the roughness properties of the sea surface scattering facets and the polarization ratio (PR) between VV and HH polarization backscatter from the SAR images. In quad-polarization SAR images, the normalized radar cross section (NRCS), which is linked to sea surface

Fire severity is a function of dynamic interactions between vegetation and burning conditions. To understand the factors that control it, accurate methods for estimating prefire vegetation structure and composition as well as fire propagation conditions are required. Here we analyzed the spatial variability of fire severity in a mixed-severity fire (3217 ha) that occurred in southeast Spain (Yeste

The rampant encroachment of Spartina alterniflora into coastal wetlands of China over the past decades has adversely affected both coastal ecosystems and socio-economic systems. However, there are no annual or multi-year epoch maps of Spartina saltmarsh in China, which hinders our understanding and management of Spartina invasion. In this study, we selected Chongming island, China, where Spartina saltmarsh

Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research

With a growing number of Earth observation (EO) products available through operational programmes such as the European Union’s Copernicus, there is increasing emphasis on product accuracy and uncertainty, necessitating evaluation against in situ reference measurements. Whilst existing reference datasets have proven a valuable resource, they incorporate little data with which products from recent EO

Lichens dominate a significant part of the Earth's land surface, and are valuable bioindicators of various environmental changes. In the northern hemisphere, the largest lichen biomass is in the woodlands and heathlands of the boreal zone and in tundra. Despite the global coverage of lichens, there has been only limited research on their spectral properties in the context of remote sensing of the environment

The Greater Maasai Mara Ecosystem (GMME) in Kenya is an iconic savanna ecosystem of high importance as natural and cultural heritage, notably by including the largest remaining seasonal migration of African ungulates and the semi-nomadic pastoralist Maasai culture. Comprehensive mapping of vegetation distribution and dynamics in GMME is important for understanding ecosystem changes across time and

Satellite-observed night-time light in urban areas has been widely used as an indicator for socioeconomic development and light pollution. Up to present, the diurnal dynamics of city light during the night, which are important to understand the nature of human activity and the underlying variables explaining night-time brightness, have hardly been investigated by remote sensing techniques due to limitation

The interferometric synthetic aperture radar (InSAR) small baseline subset (SBAS) technique can be applied to land with varying deformation magnitudes ranging from mm/yr to tens of cm/yr. SBAS defines a network of interferograms that is limited by temporal and spatial baseline thresholds that are often applied arbitrarily, or in apparently subjective ways in the literature. We use simulated SAR data

Reliable information on salt marsh extent and condition is crucial to promote effective management strategies towards their maintenance and recovery. Most of previous studies on salt marsh extent assessment used Landsat-derived Normalized Difference Vegetation Index (NDVI), being limited the current knowledge about the performance of other Vegetation Indices (VI). Based on Landsat imagery this study

Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest

Shadows are prevalent in urban environments, introducing high uncertainties to fine-scale urban land-cover mapping. In this study, we developed a Recurrent Shadow Attention Model (RSAM), capitalizing on state-of-the-art deep learning architectures, to retrieve fine-scale land-cover classes within cast and self shadows along the urban-rural gradient. The RSAM differs from the other existing shadow removal

The NASA L-Band Soil Moisture Active Passive (SMAP) satellite mission launched in 2015 has produced soil moisture and freeze thaw (FT) products at a global scale. While the use of L-band (1.41 GHz) passive microwave radiometry (P-MW) has proven useful in detecting changes in the surface FT state, these classifications have not been comprehensively assessed against similar existing FT products, such

Photon recollision probability p is a spectrally invariant structural parameter and a powerful tool to link canopy optical properties at any wavelengths to model reflectance, transmittance, or absorption of vegetation canopies. The concepts of the p-theory have been reported and examined at the shoot and canopy scales, but not yet for the crown level. Currently, the p-value is estimated indirectly

Mediterranean-climate oak woodlands are prized for their biodiversity, aesthetics, and ecosystem services. Conservation and maintenance of these landscapes requires accurate observations of both present and historic conditions capable of spanning millions of hectares. Decameter optical satellite image time series have the observational coverage to meet this need, with almost 40 years of intercalibrated

River ice monitoring is important for hydrological research and water resource management of the Tibetan Plateau but limited by the serious shortage of field observations, and remote sensing can be used as an effective supplementary means for monitoring river ice. However, remote sensing high-altitude river ice is scarce and a basin-scale understanding of river ice is lacking on the Tibetan Plateau

The freeze/thaw state of permafrost landscapes is an essential variable for monitoring ecological, hydrological and climate processes. Ground surface state can be obtained from satellite data through time series analysis of C-band backscatter from scatterometer and Synthetic Aperture Radar (SAR) observations. Scatterometer data has been used in a variety of studies concerning freeze/thaw retrieval

The changing climate is affecting the frozen soil at an unprecedented rate across the Northern Hemisphere. However, due to sparse ground measurements, the changes of frozen soil and the environmental controls over the vast cryosphere are still unclear, such as in the Tibetan Plateau (TP). In this study, a process-based model solely driven by satellite remote sensing data is employed to investigate

With less than a decade left to attain the Sustainable Development Goals (SDGs), this communication aims to improve understanding of the enabling environment that is essential for Earth observations (EO) to be fully adopted within the institutional settings that drive the implementation of the SDGs and the Global Indicator Framework, an effective review mechanism for tracking progress at global to

Plant functional diversity, defined as the range of plant chemical, physiological and structural properties within plants, is a key component of biodiversity which controls the ecosystem functioning and stability. Monitoring its variations across space and over time is critical in ecological studies. So far, several reflectance-based metrics have been tested to achieve this objective, yielding different

Ground-based evaporative fraction (EF) observations have been used widely for validation purposes in previous remote sensing-based EF models. Few studies have investigated whether such measurements can be utilized for calibration use. In this paper, an observation-driven optimization method is proposed to quantify EF over a large heterogeneous area within the surface temperature-vegetation index framework

There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature (LST) because of its strong relationships with many surface processes. However, the frequent occurrence of cloud cover induces a large blank area in current thermal infrared-based LST products. To effectively fill this blank area, a new method for reconstructing the

A new aerosol component approach that infers aerosol species fractions from satellite observations was developed and incorporated by Li et al. (2019) into Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm. In the current work we employ this aerosol component approach for derivation and analysis of temporal and spatial characteristics of black and brown carbon (BC and BrC) concentrations

Rivers are essential to the Earth's ecosystem, but the current understanding of river width variability is limited, owing to the sparse distribution of gauging stations. Remote sensing data enable the surveying and analysis of river geomorphology by providing multi-temporal Earth observation data from satellites at fine spatial and temporal resolutions. We proposed an optimized RivWidth method to automatically

Indicators of landscape condition should be sensitive and specific to environmental change and provide early warning detection of ncipient changes. We assessed a suite of five spectral metrics derived from Landsat 5 TM imagery spanning a decade (1996–2007) to quantify ecosystem condition in the ridge-slough mosaic of the Everglades (South Florida, USA). These included the normalized difference vegetation

The characteristics of urban land surfaces contribute to the urban heat island, and, in turn, can exacerbate the severity of heat wave impacts. However, the mechanisms and complex interactions in urban areas underlying land surface temperature are still being understood. Understanding these mechanisms is necessary to design strategies that mitigate land temperatures in our cities. Using the recently

Radiative transfer models (RTMs) of vegetation canopies can be applied for the retrieval of numerical values of vegetation properties from satellite data. For such retrieval, it is necessary first to apply atmospheric correction to translate the top-of-atmosphere (TOA) satellite data into top-of-canopy (TOC) values. This atmospheric correction typically assumes a Lambertian surface reflection, which

Melt ponds on sea ice play an important role in the seasonal evolution of the summer ice cover. In this study we present two machine learning algorithms, one (multi-layer neural network) for the retrieval of melt pond binary classification and another (multinomial logistic regression) for melt pond fraction using moderate resolution visible satellite imagery from the Moderate Resolution Imaging Spectroradiometer

Timely and accurate delineation of the cropland extent over large area is crucial for operational agriculture monitoring and is also beneficial to address food security issues. Existing global datasets associated with cropland are limited by insufficient spatial resolution to properly represent areas with small parcel size distributions, and their less-than-ideal accuracies hamper application at regional

This article proposes a simple and intuitive classification system by which to define full spectral remote sensing reflectance (Rrs(λ)) data with a quantitative output that enables a more manageable handling of spectral information for aquatic science applications. The weighted harmonic mean of the Rrs(λ) wavelengths outputs an Apparent Visible Wavelength (in units of nanometers), representing a one-dimensional

Assessing the oceanic surface layer's optical properties through CALIOP has been one of the reasons of the extension of the CALIOP mission for 3 more years (2018–2020). This is the first work evaluating the potential use of CALIOP for ocean applications at regional scale in mid-latitude regions (i.e. Mediterranean, MED, and Black Sea, BS) and investigating the added information on ocean particles given

The Soil Moisture Active Mission (SMAP) baseline V-pol single channel algorithm (SCA-V) retrieves soil moisture (SM) based on the 2000–2010 Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) climatology. This parameterization scheme might affect SM retrievals in the context of vegetation disturbances, e.g., as a result of drought. By referencing the

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive

The fine-mode aerosols (FMA), generally generated from anthropogenic sources, play an essential role in global radiation balance, climate change, atmospheric environment, and human health. However, the FMA retrieval remains a challenge. An improved high resolution FMA retrieval algorithm based on the Spectral Neutrality of Surface Polarized Reflectance (SNOSPR) was developed using polarized satellite

Reliable urban land-use maps are essential for urban analysis because the spatial distribution of land use reflects the complex environment of cities under the combined effects of nature and socio-economics. In recent years, very high resolution (VHR) remote sensing imagery interpretation has resolved the “semantic gap” between the low-level data and the high-level semantic scenes, and has been used

Historical wetland hydrology data are instrumental to support the design of wetland management and restoration strategies but are rarely available. In this study, we tested the capabilities and limitations of a simple methodological framework based on publicly available MODIS Land Reflectance Products to estimate wetland soil surface saturation and inundation spatiotemporal dynamics. Using supervised

One of the challenging tasks in modern aquatic remote sensing is the retrieval of near-surface concentrations of Total Suspended Solids (TSS). This study aims to present a Statistical, inherent Optical property (IOP) -based, and muLti-conditional Inversion proceDure (SOLID) for enhanced retrievals of satellite-derived TSS under a wide range of in-water bio-optical conditions in rivers, lakes, estuaries

Providing accurate information on fire effects is critical to understanding post-fire ecological processes and to design appropriate land management strategies. Multispectral imagery from optical passive sensors is commonly used to estimate fire damage, yet this type of data is only sensitive to the effects in the upper canopy. This paper evaluates the sensitivity of full waveform LiDAR data to estimate

Synthetic aperture radar (SAR) remote sensing is a potential technique for long-term monitoring of landslide-prone areas. Pixel offset tracking methods work well for fast-moving landslides (more than tens of cm/yr). However, existing methods may present some limitations, such as (i) a high dependence of estimation window design on experience, (ii) a tradeoff between the accuracy of single points and

Atmospheric water vapor plays a key role in the global water and energy cycles. Accurate estimation of water vapor and consistent representation of its spatial-temporal variation are critical to climate analysis and model validation. This study used ground observational data from global radiosonde and sunphotometer networks to evaluate MODIS (MODerate-resolution Imaging Spectroradiometer) precipitable

Glacier velocity is one of the important parameters to know about glacier dynamics, its mass and health. Understanding glacier velocity also assists in the analysis of ice thickness, ice flux and mass balance. Furthermore, basin/sub-basin wise studies are helpful to understand the climate change over that region instead of monitoring individual glaciers. In the current study, the spatial distribution

Cropland abandonment is a widespread land-use change, but it is difficult to monitor with remote sensing because it is often spatially dispersed, easily confused with spectrally similar land-use classes such as grasslands and fallow fields, and because post-agricultural succession can take different forms in different biomes. Due to these difficulties, prior assessments of cropland abandonment have

Despite much progress has been made in recent years, the estimation of terrestrial gross primary productivity (GPP) remains highly uncertain. Recent studies have demonstrated the usefulness of sun-induced chlorophyll fluorescence (SIF) from plants for estimating GPP. In this study, the relationships of GPP to SIF and Photochemical Reflectance Index (PRI) under different environmental conditions are

Landslides can result in loss of lives, cause damage to property, infrastructure, utilities, and residential structures and can block transportation routes. Landslide inventory maps can provide spatial-temporal information about past and recent landslides and are used for analysis to create models that can characterize susceptibility. As such, these maps are considered an essential source for risk

The eutrophication problems in lakes on the Yangtze Plain of China have attracted global concern. However, a comprehensive assessment of the eutrophication status and its evolution is still lacking for these regional lakes, mostly because of technical difficulties and/or insufficient data to cover the large region. Our study attempts to fill this knowledge gap by using the entire archive of remote

With the promise of transformative changes for the management of rural and urban forests, the discrimination of tree species from satellite imagery has been a long-standing goal of remote sensing. For the species-rich urban setting of Washington, D.C. USA, we evaluate current prospects toward this goal by combining a Random Forest (RF) object-based tree species classification method with two large

In tropical forests, leaf phenology—particularly the pronounced dry-season green-up—strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a