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Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam. [ABSTRACT FROM AUTHOR]

NASA's Ice, Cloud, and land Elevation Satellite (ICESat-2), designed for surface altimetry, plays a pivotal role in providing precise ice sheet elevation measurements. While its primary focus is altimetry, ICESat-2 also offers valuable atmospheric data. Current conventional processing methods for producing atmospheric data products encounter challenges, particularly in conditions with low signal or high background noise. The thresholding technique traditionally used for atmospheric feature detection in lidar data uses a threshold value to accept signals while rejecting noise, which may result in signal loss or false detection in the presence of excessive noise. Traditional approaches for improving feature detection, such as averaging, lead to a trade-off between detection resolution and accuracy. In addition, the discrimination of cloud from aerosol in the identified features is difficult given ICESat-2's single wavelength and lack of depolarization measurement capability. To address these challenges, we demonstrate atmospheric feature detection and cloud–aerosol discrimination using deep learning-based semantic segmentation by a convolutional neural network (CNN). The key findings from our research are the effectiveness of a deep learning model for feature detection and cloud–aerosol classification in ICESat-2 atmospheric data and the model's surprising capability to detect complex atmospheric features at a finer resolution than is currently possible with traditional processing techniques. We identify several examples where the traditional feature detection and cloud–aerosol discrimination algorithms struggle, like in scenarios with several layers of vertically stacked clouds, or in the presence of clouds embedded within aerosol, and demonstrate the ability of the CNN model to detect such features, resolving the boundaries between adjacent layers and detecting clouds hidden within aerosol layers at a fine resolution. [ABSTRACT FROM AUTHOR]

Space plasma turbulence plays a relevant role in several plasma environments, such as solar wind and the Earth's magnetosphere–ionosphere system, and is essential for describing their complex coupling. This interaction gives rise to various phenomena, including ionospheric irregularities and the amplification of magnetospheric and ionospheric currents. The structure and dynamics of these currents have relevant implications, for example, in studying ionospheric heating and the nature of electric and magnetic field fluctuations in the auroral and polar environments. In this study, we investigate the nature of small-scale fluctuations characterizing the ionospheric magnetic field in response to different geomagnetic conditions. We use high-resolution (50 Hz) magnetic data from the ESA's Swarm mission, collected during a series of high-latitude crossings, to probe the scaling features of magnetic field fluctuations in auroral and polar cap regions at spatial scales still poorly explored. Our findings reveal that magnetic field fluctuations in field-aligned currents (FACs) and polar cap regions across both hemispheres are characterized by different scaling properties, suggesting a distinct driver of turbulence. Furthermore, we find that geomagnetic activity significantly influences the nature of energy dissipation in FAC regions, leading to more localized filamentary structures toward smaller scales. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) provides users with all-weather, continuous, high-precision positioning, navigation, and timing (PNT) services. In the operation and use of GNSS, the influence of the space environment is a factor that must be considered. For example, during geomagnetic storms, a series of changes in the Earth's magnetosphere, ionosphere, and upper atmosphere affect GNSS's positioning performance. To investigate the positioning performance of global satellite navigation systems during geomagnetic storms, this study selected three geomagnetic storm events that occurred from September to December 2023. Utilizing the global positioning system (GPS)/Beidou navigation satellite system (BDS) dual-system, kinematic precise point positioning (PPP) experiments were conducted, and the raw observational data from 100 stations worldwide was analyzed. The experimental results show that the positioning accuracy of some stations in high-latitude areas decreases significantly when using the conventional Geometry Free (GF) cycle-slip detection threshold during geomagnetic storms, which means that the GF is no longer applicable to high-precision positioning services. Meanwhile, there is no significant change in the satellite signal strengths received at the stations during the period of the decrease in positioning accuracy. Analyzing the cycle-slip rates for stations where abnormal accuracy occurred, it was observed that stations experiencing a significant decline in positioning accuracy exhibited serious cycle-slip misjudgments. To improve the kinematic PPP accuracy during magnetic storms, this paper proposes an adaptive threshold for cycle-slip detection and designs five experimental strategies. After using the GF adaptive threshold, the station positioning accuracy improved significantly. It achieved the accuracy level of the quiet period, while the cycle-slip incidence reached the average level. During magnetic storms, the ionosphere changes rapidly, and the use of the traditional GF constant threshold will cause serious cycle-slip misjudgments, which makes the dynamic accuracy in high latitude areas and some mid-latitude areas uncommon, while the use of the GF adaptive threshold can alleviate this phenomenon and improve the positioning accuracy in the high-latitude regions and some of the affected mid-latitude areas during the magnetic storms. [ABSTRACT FROM AUTHOR]

Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction between Earth's magnetosphere and solar wind near the subsolar point via soft X-ray imaging. As the boundary of Earth's magnetosphere, magnetopause is a significant detection target to mirror solar wind's change for the SMILE mission. In preparation for inverting three-dimensional magnetopause, we proposed an OESA-UNet model to detect the magnetopause position. The model obtains magnetopause with a U-shaped structure, in an end-to-end manner. Inspired by attention mechanisms, these blocks are integrated into ours. OESA-UNet captures low and high-level feature maps by adjusting the receptive field for precise localization. Adaptively pre-processing the image provides a prior for the network. Availability metrics are designed to determine whether it can serve three-dimensional inversion. Lastly, we provided ablation and comparison experiments by qualitative and quantitative analysis. Our recall, precision, and f1 score are 93.8%, 92.1%, and 92.9%, respectively, with an average angle deviation of 0.005 under the availability metrics. Results indicate that OESA-UNet outperforms other methods. It can better serve the purpose of magnetopause tracing from an X-ray image. [ABSTRACT FROM AUTHOR]

We study the sub-ionospheric VLF transmitter signals recorded by the Austrian Graz station in the year 2020. Those radio signals are known to propagate in the Earth-ionosphere waveguide between the ground and lower ionosphere. The Austrian Graz facility (geographic coordinates: 15.46°E, 47.03°N) can receive such sub-ionospheric transmitter signals, particularly those propagating above earthquake (EQ) regions in the southern part of Europe. We consider in this work the transmitter amplitude variations recorded a few weeks before the occurrence of two EQs in Croatia at a distance less than 200 km from Graz VLF facility. The selected EQs happened on 22 March 2020 and 29 December 2020, with magnitudes of Mw5.4 and Mw6.4, respectively, epicenters localized close to Zagreb (16.02°E, 45.87°N; 16.21°E, 45.42°N), and with focuses of depth smaller than 10 km. In our study we emphasize the anomaly fluctuations before/after the sunrise times, sunset times, and the cross-correlation of transmitter signals. We attempt to evaluate and to estimate the latitudinal and the longitudinal expansions of the ionospheric disturbances related to the seismic preparation areas. [ABSTRACT FROM AUTHOR]

Our understanding of the impact of climate change on forests is constrained by a lack of long-term phenological monitoring. It is generally carried out via (1) ground observations, (2) satellite-based remote sensing, and (3) near-surface remote sensing (e.g., PhenoCams, unmanned aerial vehicles, etc.). Ground-based observations are limited by space, time, funds, and human observer bias. Satellite-based phenological monitoring does not carry these limitations; however, it is generally associated with larger uncertainties due to atmospheric noise, land cover mixing, and the modifiable area unit problem. In this context, near-surface remote sensing technologies, e.g., PhenoCam, emerge as a promising alternative complementing ground and satellite-based observations. Ground-based phenological observations generally record the following key parameters: leaves (bud stage, mature, abscission), flowers (bud stage, anthesis, abscission), and fruit (bud stage, maturation, and abscission). This review suggests that most of these nine parameters can be recorded using PhenoCam with >90% accuracy. Currently, Phenocameras are situated in the US, Europe, and East Asia, with a stark paucity over Africa, South America, Central, South-East, and South Asia. There is a need to expand PhenoCam monitoring in underrepresented regions, especially in the tropics, to better understand global forest dynamics as well as the impact of global change on forest ecosystems. Here, we spotlight India and discuss the need for a new PhenoCam network covering the diversity of Indian forests and its possible applications in forest management at a local level. [ABSTRACT FROM AUTHOR]

Tweek signals are a special type of VLF (very low frequency) pulse, originally produced by lightning discharge, which becomes dispersive after repetitive bounces within the waveguide between the Earth's surface and lower ionosphere. As such, tweek signals carry critical information about the region near the reflection height of the VLF waves, namely the D-region ionosphere. Although tweek measurements have been widely utilized in studies of the D-region ionosphere and lightning discharge, few statistical studies have been conducted, mainly due to the difficulty of manually identifying tweek signals from the enormous amount of VLF data with heavy noise. Considering the importance of tweek signals and the lack of a high-precision detection model, in this study, we propose a method to automatically and accurately pick out tweek signals from VLF measurements. This method is explicitly developed based on the you only look once (YOLO) model and a post-tracing process. Using a total of 2495 randomly selected VLF spectrogram images as the testing set, we evaluated the performance of this method. The precision and recall are found to be 92.0% and 89.2% for the first-order mode, and 97.5% and 86.7% for the first-two-order mode tweek, respectively. The time needed to process 10-s VLF measurements with a cadence of 4 μs is only 6.5 s, allowing for identifying the tweek signals from continuous VLF measurements in real time. Therefore, this method represents a reliable means to automatically detect tweek signals and enables the opportunity to statistically investigate the D-region ionosphere and lightning discharge via these signals. [ABSTRACT FROM AUTHOR]

High-quality satellite cloud images are of great significance for weather diagnosis and prediction. However, many of these images are often degraded due to relative motion, atmospheric turbulence, instrument noise, and other factors. In the satellite imaging process, the degradation also cannot be completely corrected. Therefore, it is necessary to further improve the satellite cloud image quality for real applications. In this study, we propose an unsupervised image restoration model with a two-stage network, in which the first stage, named the Prior-Knowledge-based Generative Adversarial Network (PKKernelGAN), aims to learn the blur kernel, and the second stage, named the Zero-Shot Deep Residual Network (ZSResNet), aims to improve the image quality. In PKKernelGAN, we propose a satellite cloud imaging loss function, which is a novel objective function that brings optimization of a generative model into the prior-knowledge domain. In ZSResNet, we build a dataset which contains the original satellite cloud image as high-quality images (HQ) paired with low-quality images (LQ) generated by the blur kernel learning from PKKernelGAN. The above innovations lead to a more efficient local structure in satellite cloud image restoration. The original dataset of our experiment is from the Sunflower 8 satellite provided by the Japan Meteorological Agency. This dataset is divided into training and testing sets to train and test PKKernelGAN. Then, ZSResNet is trained by the "LQ–HQ" image pairs generated by PKKernelGAN. Compared with other supervised and unsupervised deep learning models for image restoration, our model has a better performance. Extensive experiments have demonstrated that our proposed model can achieve better performance on different datasets. [ABSTRACT FROM AUTHOR]

The controlled source extremely low frequency (CSELF) method bears the potential for deep resource exploitation utilizing the skywave. The "Skywave" denotes the electromagnetic wave propagating through the waveguide formed by the Earth and ionosphere. It has a considerable penetration depth into the lithosphere due to its low-frequency band. Previous research on extremely low-frequency electromagnetic fields with the coupled lithosphere, atmosphere, and planar ionosphere models ignored the effect of the Earth's curvature. Thus, we aimed to present the exact formulas for horizontal electric dipoles (HED) in a spherical "Earth-ionosphere" model. These new formulas consider the Earth's curvature as a multilayer medium rather than a homogeneous underground. We introduce three techniques: function combination pairs, addition and subtraction terms, and Padé approximants, to handle slow convergence in numerical calculation. In the spherical waveguide, electromagnetic fields are mutually interfered with and produce oscillations, which is different from the planar model. The influence of Earth's curvature cannot be neglected with the increase in source–receiver distance, though it is negligible within 3000 km. Furthermore, it is worth noting that apparent resistivity ρθφ enters the waveguide area earlier than ρφθ. This method can be used as Green's function to simulate the electromagnetic field of actual antennas and 3-D models. [ABSTRACT FROM AUTHOR]

Due to the long distance propagation characteristics of the HF spectrum, noise and interference are significant and challenging factors that have to be addressed in the context of HF system performance. Both aspects exhibit systematic diurnal, seasonal, and solar cycle variability that result in a number of corresponding measurement campaigns all over the globe, in an effort to characterise HF noise. Noise at HF consists of man-made and naturally occurring HF noise with atmospheric and galactic components. An understanding of HF noise variation in terms of time, azimuth, and filter bandwidth is crucial for a specific location, as it can provide a background knowledge based on which appropriate values of HF system parameters may be selected. This paper deals with the temporal, azimuthal, and bandwidth characteristics of HF noise based on a one-month dataset of electric field strength measurements recorded by a directional antenna in Cyprus. [ABSTRACT FROM AUTHOR]

The group velocity of lightning electromagnetic signals plays an important role in lightning location systems using the time difference of arrival (TDOA) method. Accurate estimation of group velocity is difficult due to the space- and time-varying properties of the Earth-ionosphere waveguide. Besides, the analytical solution of the group velocity is difficult to obtain from the classic mode theory, especially when higher-order modes, anisotropic geomagnetic background, diffuse ionosphere profile, and propagation path segmentation are all taken into consideration. To overcome these challenges, a novel numerical method is proposed in this paper to estimate the group velocity of the lightning signal during ionospheric quiet periods. The well-known Long Wavelength Propagation Capability (LWPC) code is used to model the propagation of VLF/LF radio waves. Since LWPC uses a simplified ionospheric model which is unable to describe the subtle variations of ionospheric parameters over time and space, the IRI-2016 model is incorporated into the numerical modeling process to provide more accurate ionosphere parameters. Experimental results of a VLF/LF lightning location network are demonstrated and analyzed to show the effectiveness of our method. The proposed method is also applicable when there is a sudden ionospheric disturbance as long as the parameters of the ionosphere are obtained in real time by remote sensing methods. [ABSTRACT FROM AUTHOR]

With the rapid development of interferometric synthetic aperture radar (InSAR) measurement technology, its measurement accuracy requirements are increasing. Atmospheric delay errors must be corrected, especially in the case of crustal deformation monitoring, the 20% variation of tropospheric water vapor among InSAR pairs generally produces range from 10 cm to 14 cm deformation errors. Such errors can be of the same magnitude as the annual changes in crustal deformation, or even greater, masking crustal deformation information and seriously affecting the results of crustal deformation monitoring. Therefore, in order to obtain a more accurate InSAR atmospheric delay correction model, this paper calculated and integrated atmospheric delays that were estimated by different sources, including the 37 pressure levels of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF)) numerical weather prediction model, ECMWF Reanalysis v5 (ERA5), and Global Navigation Satellite System (GNSS) measurement data from the crustal movement observation network of China, based on the variance component estimation (VCE) weighting method. The results showed that the integrated model, based on the VCE method, is better than the generic atmospheric correction online service (GACOS) model for InSAR measuring of crustal deformation. The precision in monitoring crustal deformations was improved by approximately 5 mm, the correlation coefficient of atmospheric delay errors and crustal deformations improved from 0.287 to 0.347, and accuracy improved by approximately 25%. However, the improvement in accuracy was limited because of system error decoherence that was induced by atmospheric noise caused by abundant vegetation or snow cover. Therefore, in order to achieve more accurate results, we recommend the adoption of the multi-source integrated atmospheric delay correction model, based on the VCE method, for InSAR high-precision measuring of crustal deformation and seismic activities. [ABSTRACT FROM AUTHOR]

LiDAR is an excellent source of elevation data used in many surveys. The spaceborne handle system, Global Ecosystem Dynamics Investigation (GEDI), provides ground elevation information with high accuracy except for areas with steep slopes. GEDI data have a lot of noise from atmospheric conditions, and therefore filtering procedures are mandatory to select the best dataset. The dataset presents uncertainties of different magnitudes, with values reaching more than 100 m of difference between the reference data and the GEDI data. The challenge is to find a criterion to determine a threshold to filter accurate GEDI samples. This research aims to identify the threshold based on the difference values between the reference data and the GEDI data to select the maximum number of samples with low RMSE values. Therefore, we used the Kolmogorov–Smirnov (KS) non-parametric test to define the best threshold based on a normal distribution. Our results demonstrated a lower RMSE value with a high number of samples when compared with the quality flag parameter threshold, even using sensitivity parameter thresholds. This method is useful for achieving the best possible accuracy from GEDI data worldwide. [ABSTRACT FROM AUTHOR]

Gamma-Flash is an Italian program devoted to the realization of both a ground-based and an airborne gamma-ray and neutron detection system, for in situ measurements of high-energy phenomena correlated to thunderstorm activity, such as Terrestrial Gamma-ray Flashes (TGFs), gamma-ray glows, and associated neutron emissions. The ground-based Gamma-Flash experiment is currently under installation at the Osservatorio Climatico "Ottavio Vittori" (CNR-ISAC) on Mt. Cimone, in Northern-Central Italy (2165 m a.s.l.), and it will be operational starting in Summer 2022. We studied the detectability of TGFs in the surroundings of the ground-based Gamma-Flash experiment, to identify an investigable spatial region around the detectors from which typical TGFs can survive and be revealed onground. We carried out numerical simulations of gamma-ray propagation in the mid-latitude atmosphere, and we developed a qualitative analytical model to integrate the results. This analysis allows one to identify a spatial region extending up to 4 km distance on ground and up to 10 km altitude a.s.l., considering typical TGFs emitting ∼ 10 18 gamma-ray photons at the source. Lightning sferics data acquired by the LINET network demonstrate that such a region is interested by frequent cloud-to-ground and intra-cloud lightning, pointing out the suitability of the location for the purposes of the Gamma-Flash program. [ABSTRACT FROM AUTHOR]

Fully coupled lithosphere, atmosphere, and ionosphere theory has demonstrated that extremely low-frequency electromagnetic (ELF-EM) fields present a broad application prospect in deep resource exploration, but previous studies have ignored the contribution of the Earth's curvature. This study extends the theory of ELF-EM over a stratified Earth to the case where the Earth's curvature must be taken into account, and presents an analytical solution of the ELF-EM field excited by a grounded horizontal antenna in a spherical Earth–ionosphere model, whose theoretical approach and solution method are notably different from the flat Earth–ionosphere model. Additionally, the Earth is treated as a concentric-layered sphere rather than an ideal homogeneous sphere. We aim to investigate the effects of the Earth's curvature on the surface field, so as to broaden the coverage of the ELF wave in resource exploration. The solution is mathematically accurate and physically reasonable, since it reflects the sphericity and radially stratified structure of the Earth. We first verify the correctness and reliability of the proposed method by comparing the results with FDTD in a full-space spherical model. Additionally, we then compared the spherical results with the conventional controlled-source electromagnetic method and flat Earth–ionosphere results. The results show that when the distance between the transmitter and the receiver is comparable to the Earth radius, the spherical model better reflects the resonance of the wave in the cavity, suggesting that the effect of the Earth's curvature is not negligible. Then, the numerical simulations conducted to investigate the properties of the EM fields and their sensitivities to the conductivity at depth in the Earth are discussed. Finally, the EM responses of some simple electrical conductivity structures models are modeled to illustrate their prospects in future resource exploration. [ABSTRACT FROM AUTHOR]

The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference background consisting of sea clutter, ionosphere clutter, Radio Frequency Interference (RFI) and atmospheric noise. Eliminating unwanted echoes and exploring obscured targets contribute to achieving ideal surveillance of sea surface targets. In this paper, a Self-regulating Multi-clutter Suppression Framework (SMSF) has been proposed for small aperture HFSWR. SMSF can remove many types of clutter or RFI; meanwhile, it mines the targets merged into clutter and tracks the travelling path of the ship. In SMSF, a novel Dynamic Threshold Mapping Recognition (DTMR) method is first proposed to reduce the atmospheric noise and recognize each type of unwanted echo; these recognized echoes are fed into the proposed Adaptive Prophase-current Dictionary Learning (APDL) algorithm. To make a comprehensive evaluation, we also designed three novel assessment parameters: Obscured Targets Detection Rate (OTDR), Clutter Purification Rate (CPR) and Erroneous Suppression Rate (ESR). The experiment data collected from a small aperture HFSWR system confirm that SMSF has precise suppression performance over most of the classical algorithms and concurrently reveals the moving targets, and OTDR of SMSF is usually higher than compared methods. [ABSTRACT FROM AUTHOR]

The equivalent propagation method adopts a variable propagation velocity in lightning location, minimizing the location error caused by various factors in the long-range lightning location network. To verify the feasibility of this method, we establish a long-range lightning location network in China. A new method is used to extract the ground wave peak points of the lightning sferics and is combined with the equivalent propagation velocity method for lightning location. By comparing with the lightning data detected by the lightning locating system called advanced direction and time-of-arrival detecting (ADTD) that has been widely used for tens of years in China, the feasibility of this method is initially verified. Additionally, it is found that the relative detection efficiency of our long-range lightning location network can reach 53%, the average location error is 9.17 km, and the detection range can reach more than 3000 km. The equivalent propagation method can improve the average location accuracy by ~1.16 km, compared with the assumed light speed of lightning-radiated sferic from the lightning stroke point to the observation station. The 50th percentile of the equal propagation velocity is 0.998c, which may be used in the long-range lightning location networks. [ABSTRACT FROM AUTHOR]

Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new techniques for these tasks is a vital research area. Here, deep learning has established the current state of the art. However, deploying large-capacity deep models on-board an Earth observation satellite poses additional technological challenges concerned with their memory footprints, energy consumption requirements, and robustness against varying-quality image data, with the last problem being under-researched. In this paper, we tackle this issue, and propose a set of simulation scenarios that reflect a range of atmospheric conditions and noise contamination that may ultimately happen on-board an imaging satellite. We verify their impact on the generalization capabilities of spectral and spectral-spatial convolutional neural networks for hyperspectral image segmentation. Our experimental analysis, coupled with various visualizations, sheds more light on the robustness of the deep models and indicate that specific noise distributions can significantly deteriorate their performance. Additionally, we show that simulating atmospheric conditions is key to obtaining the learners that generalize well over image data acquired in different imaging settings. [ABSTRACT FROM AUTHOR]

The time series (TS) of the normalized difference vegetation index (NDVI) has been widely used to trace the temporal and spatial variability of terrestrial vegetation. However, many factors such as atmospheric noise and radiometric correction residuals conceal the actual variation in the land surface, and thus hamper the TS information extraction. To minimize the negative effects of these noise factors, we propose a new method to produce a synthetic gap-free NDVI TS from the original contaminated observation. First, the key temporal points are identified from the NDVI time profiles based on a generally used rule-based strategy, making the TS segmented into several adjacent segments. Then, the observed data points in each segment are fitted with a weighted double-logistic function. The proposed dynamic weight reassignment process effectively emphasizes cloud-free points and deemphasizes cloud-contaminated points. Finally, the proposed method is evaluated on more than 3,000 test points from three selected Sentinel-2 tiles, and is compared with the generally used Savitzky-Golay (S-G) and harmonic analysis of time series (HANTS) methods from qualitative and quantitative aspects. The results indicate that the proposed method has a higher capability of retaining cloud-free data points and identifying outliers than the others, and can generate a gap-free NDVI time profile derived from a medium-resolution satellite sensor. [ABSTRACT FROM AUTHOR]

Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions. [ABSTRACT FROM AUTHOR]

Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster monitoring, for example forest fires. In order to correct for the dominant source of atmospheric noise, a simple, fast and flexible algorithm has been developed. The algorithm is implemented in Python and freely available in PySpectral which is part of the PyTroll family of open source packages, allowing easy access to powerful real-time image-processing tools. Pre-calculated look-up tables of top of atmosphere reflectance are derived by off-line calculations with RTM DISORT as part of the LibRadtran package. The approach is independent of platform and sensor bands, and allows it to be applied to any band in the visible spectral range. Due to the use of standard atmospheric profiles and standard aerosol loads, it is possible just to reduce the background disturbance. Thus signals from excess aerosols become more discernible. Examples of uncorrected and corrected satellite images demonstrate that this flexible real-time algorithm is a useful tool for atmospheric correction. [ABSTRACT FROM AUTHOR]