Vitality loss of trees caused by extreme weather conditions, drought stress or insect infestations, are expected to increase with ongoing climate change. The detection of vitality loss at an early stage is thus of vital importance for forestry and forest management to minimize ecological and economical damage. Remote sensing instruments are able to detect changes over large areas down to the level

Estimating methane (CH4) emission rates using quantitative CH4 retrievals from the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) requires the use of wind speeds. Model wind speeds have limited temporal and spatial resolution, meteorological station wind data are of variable quality and are often not available near observed plumes, and the use of ultrasonic anemometers co-located

Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations

Over the past decades, solar panels have been widely used to harvest solar energy owing to the decreased cost of silicon-based photovoltaic (PV) modules, and therefore it is essential to remotely map and monitor the presence of solar PV modules. Many studies have explored on PV module detection based on color aerial photography and manual photo interpretation. Imaging spectroscopy data are capable

The Monitoring Nitrous Oxide Sources (MIN2OS) satellite project aims at monitoring global-scale nitrous oxide (N2O) sources by retrieving N2O surface fluxes from the inversion of space-borne N2O measurements that are sensitive to the lowermost atmospheric layers under favorable conditions. MIN2OS will provide emission estimates of N2O at a horizontal resolution of 1° × 1° on the global scale and 10 × 10 km2

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is unique in its ability to perform profiling measurements of aerosol and cloud layers globally. Detecting the layer boundaries of aerosols and clouds is a crucial step in CALIPSO data retrieval. The CALIPSO team uses the selective iterated boundary location (SIBYL) algorithm based on threshold arrays to find aerosol and

The accuracy of earthquake catalogs limits the reliability of earthquake hazard assessment and the comprehensive understanding of earthquake mechanisms. Current seismic catalogs are based on the inversion of seismic wave data. The accuracy of their source parameters, which reflect the quantity and layout of seismic stations, and the crustal velocity model used, are often highly uncertain. The open

A method for rectifying thermograms in perspective captured at human height is proposed. It applies to the image pixel-by-pixel corrections that account for the surface emissivity, the reflected longwave infrared radiation flux, and the radiation emitted and attenuated by the atmosphere. The results are validated in two steps: first by comparison with contact temperature devices and then by comparison

Annual global land cover maps (GLC) are being provided by several operational monitoring efforts. However, map validation is lagging, in the sense that the annual land cover maps are often not validated. Concurrently, users such as the climate and land management community require information on the temporal consistency of multi-date GLC maps and stability in their accuracy. In this study, we propose

Research has demonstrated the utility of digital aerial photogrammetry (DAP) for area-based predictions of forest inventory attributes. To date, studies have used DAP data acquired with a range of spatial resolutions and image overlaps. The systematic benchmarking of DAP acquisition parameters remains an outstanding research and operational gap for forest applications. While the impact of along-track

In light of the ongoing global biodiversity crisis, the urge to monitor and map terrestrial plant biodiversity at large spatial extents has spurred research on adequate quantitative methods. The use of spectral diversity metrics from different remote sensing platforms has emerged as a promising tool for such biodiversity assessments. Satellite remote sensing presents the next frontier for implementation

The wind direction dependence of the fully polarimetric ocean emissivity at L-band is studied using a semi-theoretical approach. The model, which is tuned and validated against Aquarius and Soil Moisture Active-Passive (SMAP) mission observations, incorporates the two-scale polarimetric ocean emission model, an improved directional wave spectrum, the Anguelova and Gaiser, 2013 (AG13) foam emissivity

Plant water use is difficult to monitor and predict in complex terrain. NASA's ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides new data for understanding hydrologic cycling in these forested mountain areas. Evaluation of ECOSTRESS evapotranspiration (ET) has relied primarily on eddy flux towers, but most flux towers are located in flat terrain, sampling relatively

The prescription of surface emissivity (ε) strongly controls satellite-derived estimates of land surface temperature (LST). This is particularly important for studying surface urban heat islands (SUHI) since built-up and natural landscapes are known to have distinct ε values. Given the small signal associated with the SUHI compared to LST, accurately prescribing urban and rural ε would improve our

Wildfires have become one of the biggest natural hazards for environments worldwide. The effects of wildfires are heterogeneous, meaning that the magnitude of their effects depends on many factors such as geographical region, climate and land cover/vegetation type. Yet, which areas are more affected by these events remains unclear. Here we present a novel application of the Generalized Synthetic Control

Monitoring the spatiotemporal dynamics of surface water from remote sensing imagery is essential for understanding water's impact on the global ecosystem and climate change. There is often a tradeoff between the spatial and temporal resolutions of imagery acquired from current satellite sensors and as such various spatiotemporal image fusion methods have been explored to circumvent the challenges this

Soil moisture (SM) retrieval in agricultural regions during the growing seasons is particularly challenging due to high spatial variability and dynamic vegetation conditions. The retrievals have been problematic even when the passive signatures at different spatial scales match well since they depend upon the accuracy of vegetation information such as the vegetation water content (VWC). The VWC used

Landslides are common geohazards associated with natural drivers such as precipitation, land degradation, toe erosion by rivers and wave attack, and ground shaking. On the other hand, human alterations such as inundation by water impoundment or rapid drawdown may also destabilize the surrounding slopes. The Guobu slope is an ancient rockslide on the banks of the Laxiwa hydropower station reservoir

Indonesia recently implemented a novel, technology-driven approach for conducting agricultural production surveys, which involves monthly observations at many thousands of strategic locations and automated data logging via a cellular phone application. Data from these comprehensive field surveys offer immense value for advancing remote sensing technology to map crop production across Indonesia, particularly

The detection and repairment of methane leaks from fossil fuel production activities have been identified as a key climate change mitigation strategy. Several types of optical satellite sensors have recently shown potential to support this task. Spaceborne imaging spectrometers measuring in the 400–2500 nm spectral range belong to this group. These instruments measure the solar radiation reflected

A reliable accuracy is essential for the application of land surface temperature (LST) products. Current satellite retrieved LSTs are mainly validated over a few homogeneous sites. However, most of the existing ground sites are located in inhomogeneous areas: thus, their spatial representativeness on satellite pixel scales is unknown. In this situation, how to evaluate the spatial representativeness

Sun-induced chlorophyll fluorescence (SIF) from spaceborne sensors is a promising tool for global carbon cycle monitoring, but its application is constrained by insufficient understanding of the drivers underlying diurnal SIF dynamics. SIF measurements from ground-based towers can reveal diurnal SIF dynamics across biomes and environmental conditions; however, meaningful interpretation of diurnal variations

Full waveform (FW) LiDAR systems have proven their effectiveness to map forest biophysical variables in the last two decades, owing to their ability of measuring, with high accuracy, forest vertical structures. The Global Ecosystem Dynamics Investigation (GEDI) system on board the International Space Station (ISS) is the latest FW spaceborne LiDAR instrument for the continuous observation of Earth's

Medium resolution satellite-derived fractional cover estimates of bare soil (fBS), photosynthetic vegetation (fPV), and non-photosynthetic vegetation (fNPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of fPV and fNPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’

Current estimates of forest degradation are associated with large uncertainties. However, recent advancements in the availability of remote sensing data (e.g., the free data policies of the Landsat and Sentinel Programs) and cloud computing platforms (e.g., Google Earth Engine (GEE)) provide new opportunities for monitoring forest degradation. Several recent studies focus on monitoring forest degradation

Although remote sensing (RS) of solar-induced chlorophyll fluorescence (SIF) is increasingly used as a valuable source of information about vegetation photosynthetic activity, the RS SIF observations are significantly influenced by canopy-specific structural features (i.e., canopy architecture including leaf area index and presence of woody components), atmospheric conditions during their acquisition

Despite the importance of diffraction of irregular ocean waves, there is a lack of reported measurements over scales larger than a few tens of wavelengths. In the coastal zone, Satellite Altimetry is hindered because the backscatter characteristics of waveforms deviate from Brown's theoretical model, specially up to 20 km from the coastline. Here, we combine a novel set of retracked — reprocessed —

Synthetic Aperture Radar (SAR) intensity is used as an input to many flood-mapping algorithms. The appearance of floodwater tends to cause a substantial decrease of backscatter intensity over scarcely vegetated terrain. However, limitations exist in areas where the SAR backscatter is not sufficiently sensitive to surface changes, e.g. shadow areas due to topography or obstacles on the ground, densely

We present the “QAA-RGB”, a semianalytical algorithm that retrieves total absorption, particle backscattering, as well as the diffuse attenuation coefficient and the Secchi disk depth, from eighteen metre and decametre satellite sensors, including present and heritage Landsat's data, Sentinel 2 at 10 m and an array of commercial satellites such as PlanetScope, Pléiades and Worldview. The QAA-RGB is

A semi-analytical model (CDOM-KD2) based on the light vertical attenuation coefficient (Kd(λ)) has been developed for estimating the absorption by colored dissolved organic matter, acdom(443), from ocean color remote sensing at global scale. The performance of this new inversion model together with that of former models by Shanmugam (2011) (S2011), Chen et al. (2017) (C2017) and Aurin et al. (2018)

This study aimed at estimating total forest above-ground net change (ΔAGB; Gg) over five years (2014–2019) based on model-assisted estimation utilizing freely available satellite imagery. The study was conducted for a boreal forest area (approx. 1.4 Mha) in Norway where bi-temporal national forest inventory (NFI), Sentinel-2, and Landsat data were available. Biomass change was modelled based on a direct

Hyperspectral airborne imagery can provide rich information on plant physiological and structural properties at a scale intermediate to that of proximal and satellite remote sensing and has broad applications in assessing ecosystem function and biodiversity. A key processing step of airborne hyperspectral data is the atmospheric correction that compensates for path radiance, aerosol effects and gas

Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanitarian assistance and disaster response. For building

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region

Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the objects of interest. For VLS of forests, the way trees are constructed

The Surface Water and Ocean Topography (SWOT) altimeter mission can measure high-resolution wide-swath sea surface heights (SSHs) that may greatly improve the current accuracy and spatial resolution of marine gravity from nadir-looking altimeters. To investigate the potential of SWOT in recovering high-quality marine gravity and how SWOT observation errors should be treated to optimize the accuracy

High resolution imaging spectrometers are prerequisite to address significant data gaps in inland optical water quality monitoring. In this work, we provide a data-driven alignment of chlorophyll-a and turbidity derived from the Sentinel-2 MultiSpectral Imager (MSI) with corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products. For chlorophyll-a retrieval, empirical ‘ocean colour’

Water-level monitoring is important for climate analysis, water resource planning, and monitoring of disasters, such as floods, droughts, and storm surges. The technology of global navigation satellite system-reflectometry (GNSS-R) has been demonstrated previously and can be used to retrieve data on various water levels, such as sea, lake, river, and reservoir water levels. In this paper, we focus

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photographs

Drought can have pervasive and wide-spread impacts to forest health, as evidenced in several severe events occurring over the recent decades. Extensive forest die-off due to drought can impair the ecological functioning of forests, impacting habitat, water yield and quality from forested lands, and altering forest fire dynamics and intensity. Satellite remote sensing provides an effective means for

The Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) is the most recent U.S. geostationary satellite and has improved resolution and potential for land surface monitoring over previous GOES sensors. The ABI reflectance has diurnal variation due to surface reflectance anisotropy that can be significant, particularly when ABI observations are sensed under hot-spot

In this study, an automatic change detection method for near real-time (NRT) forest monitoring in tropical regions is described. L-band ALOS-2/PALSAR-2 ScanSAR HH, HV, and HH/HV ratio were used to detect various deforestation stages based on their different radar scattering characteristics. The three main stages considered in this approach were as follows: (1) forest was cut, and felled trees were

Carbon uptake and tree growth are important factors for assessing productivity and long-term carbon storage. Measurements of radial stem growth are mainly performed at the individual tree scale and can be used to infer ecosystem net primary productivity (NPP). However, these measurements are spatially limited, and remote sensing provides a promising tool to track vegetation function and productivity

The diurnal and annual cycles of Earth drive the land surface temperature (LST) variations in space and time but their combined effects are conveyed differently from place to place as a function of climate, local weather conditions, and surface characteristics. Fitting a model of the diurnal or annual LST cycle (DTC and ATC, respectively) to satellite data yields several parameters that summarize the

As an extension of wave period inversion models based on altimetry data by an artificial neural network approach, a Deep Neural Network approach (DNN) mean wave period model is developed by introducing new parameters as DNN inputs. In addition to conventional altimeter-derived parameters such as significant wave height (SWH) and the normalized radar cross-section (NRCS) sigma0, the spatial (along-track)

The most prevalent global surface models are derived from space-based technologies. The spatial and temporal coverage of radar and imaging systems have significant advantage over other sensors and platforms. However, these systems are often are challenged in certain environmental conditions to produce accurate elevations as compared to what might achieved with laser altimetry. Radar derived DEM (Digital

Surface downward shortwave radiation (DSR) is a fundamental variable in determining the Earth's radiation balance and is essential in many applications. Considerable efforts have been devoted to algorithm development, product generation, and validation. However, few studies have focused on comparing retrieval approaches, examining their strengths and weaknesses, and identifying the most suitable scenarios

Rapid and accurate crop type mapping is of great significance for agricultural management and sustainable development. Time-series multi-polarization synthetic aperture radar (SAR) data is suitable for obtaining the large-scale distribution of crop types and continuously monitoring crops. At present, the classification method based on the time-series alignment of time-varying feature curves has been

Nighttime images taken with DSLR cameras from the International Space Station (ISS) can provide valuable information on the spatial and temporal variation of artificial nighttime lighting on Earth. In particular, this is the only source of historical and current visible multispectral data across the world (DMSP/OLS and SNPP/VIIRS-DNB data are panchromatic and multispectral in the infrared but not at

In addition to providing water for nearly 2 billion people, snow drives resource selection by wildlife and influences the behavior and demography of many species. Because snow cover is highly spatially and temporally variable, mapping its extent using currently available satellite data remains a challenge. At present, there are no sensors acquiring daily data of Earth's entire surface at fine spatial

Among the many calcifying marine organisms, coccolithophores are the major producer of particulate inorganic carbon (PIC). Calcium carbonate plates covering coccolithophores, called coccoliths and released during blooms, are responsible for a large increase of the water reflectance. Aiming at investigating the spectral features of the remote sensing reflectance RRS(λ) of marine waters during coccolithophore

Identification of materials based on spectral reflectance is confounded by variations in reflectance magnitude that are independent of the spectral shape. Local variations such as viewing/illumination angles, multiscale soil surface roughness that causes shadows and redistributes light, and soil moisture content, all drive changes in magnitude that are distinct from the spectral variations, and complicate

Urban areas, where more than 55% of the global population gathers, contribute more than 70% of anthropogenic fossil fuel carbon dioxide (CO2ff) emissions. Accurate quantification of CO2ff emissions from urban areas is of great importance for formulating global warming mitigation policies to achieve carbon neutrality by 2050. Satellite-based inversion techniques are unique among “top-down” approaches

Most polar-orbit-satellite based passive microwave radiometers, e.g. AMSR-2 and the FY-3B Microwave Radiation Imager (FY3B-MWRI), have the revisit cycles of ~2 days in low latitudes. This indicates that requirement of complete global coverage on the daily basis has not been met. On the other hand, using synergistic observations from different radiometers seems promising to enhance the temporal resolution

The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods have been developed, they have not effectively addressed the issues associated with large gaps in the time series over cloudy and rainy regions, due to the insufficient utilization of the spatial, temporal and periodical