Search

The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d'Études Spatiales de la Biosphère (CESBIO) and the Centre National d'Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products. [ABSTRACT FROM AUTHOR]

Atmospheric correction (AC) plays a critical role in the preprocessing of remote sensing images. Although AC is necessary for applications based on remote sensing inversion, it is not always required for those based on remote sensing classification. Recently, remote sensing statistical inference has been proposed for evaluating water quality. However, input data for these models have always been remote sensing reflectance (Rrs), which requires AC. This raises the question of whether AC is necessary for remote sensing statistical inference. We conducted a theoretical analysis and image validations by testing 24 water bodies observed by Landsat-8 and compared their spectral probability distributions (SPDs) calculated from Rrs before and after AC (using the ACOLITE model). Additionally, we tested and found that, if we use remote sensing inference as a tool to quantitatively infer statistical parameters of a specific waterbody, it is better to perform atmospheric correction. However, if the quantitative inference is applied to a large number of water bodies and high inference accuracy is not required, atmospheric correction may not be necessary, and a quick calculation based on the strong correlations between Rrs at the surface and sensor-observed reflectance can be used as a substitute. [ABSTRACT FROM AUTHOR]

Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE <0.32 g cm -2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate. [ABSTRACT FROM AUTHOR]

The net Arctic sea-ice area (SIA) can be estimated from the routine monitoring of sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example, of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty. We here derive this uncertainty by taking spatial and temporal error correlations of the underlying local sea ice concentration products into account. Doing so, we find that the observational uncertainties of both sea-ice area and sea-ice extent (SIE) in 2015 are about 300 000 km2 for daily and weekly estimates and 160 000 km2 for monthly estimates. This is about half of the spread in estimated sea-ice area from different passive microwave SIC products, showing that random SIC errors play at least as much a role in SIA uncertainties as inter-SIC-product biases. We further show that the trend of SIA in September, which is traditionally the month with least Arctic sea ice is 105 000 km2 a-1 ± 9 000 km2 a-1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations. [ABSTRACT FROM AUTHOR]

With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 x 10-05 for Dataset 1 and MSE of 4.0142 x 10-07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 x 10?-07 for Dataset 1, and MSE of 1.0425 x 10-08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable. [ABSTRACT FROM AUTHOR]

The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125° during 1979–2018 over the CONUS using different configurations of CLM, namely CLM5‐biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5‐satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote‐sensed and in situ data sets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC‐simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM‐simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation data sets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially distributed plant phenology and physiology parameters and regional‐specific agricultural management practices (e.g., planting and harvest). Key Points: Parametric and structural updates in Community Land Model version 5 (CLM5) improve its ability in capturing terrestrial biogeochemical dynamicsLow evapotranspiration in CLM5‐biogeochemistry can be attributed to biases in simulating plant phenology rather than soil water limitationsEfforts are needed to improve runoff and incorporate spatially distributed vegetation parameters and agricultural management practices [ABSTRACT FROM AUTHOR]

In the last decade satellite remote sensing has become an effective tool for monitoring geo-hazard-induced ground motions, and has been increasingly used by the scientific community. Direct and indirect costs due to geo-hazards are currently rising, causing serious socio-economics and casualty losses. Therefore, creating a priority list turns out to be essential to highlight the most relevant ground deformations and to better focus risk management practices at regional scale. The Sentinel-1 constellation, thanks to the 6-days repeatability and the free availability of the data, allows to easily update the geo-hazard-induced ground motions, compared to other kind of satellite sensors. In this work, a hot-spot-like method is presented by filtering a large stack of Sentinel-1 images processed by means of the SqueeSAR algorithm. Three periods, with six months repetitiveness, have been analysed in order to evaluate the behaviour and evolution of deformation clusters. The target area is Tuscany Region, located in the central part of Italy and affected by a wide gamma of geohazards, ranging from landslides to large subsidence areas. The final output is a geo-database of ground motions that can be used by regional authorities to prioritize and to effectively plan local risk reduction actions. [ABSTRACT FROM AUTHOR]

Floods are one of the most widespread and severe natural disasters. Then, evaluating the accuracy of multiscale remote sensing for flood monitoring is an essential research topic. In this study, floodwater boundary was obtained for application of the mechanism-based upscaling method and NDWI water extraction method. Floodwater extraction was simulated and its accuracy was analyzed for different spatial extents, spatial resolutions, landscape patterns and water body proportions at eight floodplains in China. The regional accuracy gradually increased with the spatial extent when the spatial resolution was constant and decreased with the spatial resolution when the spatial extent was constant. The water extraction accuracy showed a significant correlation with the water body proportion and water landscape pattern indices. The relative error of the water extraction had a strong positive correlation with the complexity of the patch shape and a strong negative correlation with the patch aggregation and connectivity and the water body proportion. Therefore, it may be concluded that different spatial resolutions of remotely sensed images should be selected according to differences in the water landscape patterns of regions to obtain results with the required water body extraction accuracy for regional flood monitoring in China. [ABSTRACT FROM AUTHOR]

Highlights • A comprehensive optical property database for black carbon (BC) aggregates is developed and evaluated. • The implementation is used to calculate BC optical properties accurately and efficiently based on pre-calculated database. • Examples to retrieve BC refractive index and calculate radiative properties are performed to show the importance of accurate optical property simulations. Abstract Black carbon (BC) aerosol plays an important role in the atmosphere as an absorber of solar radiation and contributes significantly to global warming. The optical properties of BC are fundamental for both direct radiative effect studies and remote sensing of BC properties. However, due to the complex aggregation structures of BC particles, accurate simulations of their optical properties are limited and computationally expensive, while equivalent spherical models or approximate methods could easily introduce relative errors up to 30%. This study generates a comprehensive database to calculate aggregate optical properties efficiently and accurately. The database covers a wide range of BC properties including aggregation structure, refractive index, and particle size (both monomer size and number of monomers). An accurate numerical model, i.e. the multiple-sphere T-matrix method, is used to calculate the single-scattering properties of BC clusters in the form of fractal aggregates. By interpolation, the database can be used to give the scattering properties of aggregates with monomer number from 1 up to 3000, monomer size parameter from 0.05 to 0.5, real part of refractive index from 1.2 to 2.0, and imaginary part from 0.2 to 1.0. The relative errors caused by the interpolation are much less than 1% for the single scattering properties. The pre-calculated database and the implementation used to calculate the required aggregate optical properties are publicly available for users interested in remote sensing of BC aerosols and performing radiative effect calculations. [ABSTRACT FROM AUTHOR]

Recommendations are presented for in situ and remote sensing instruments and capabilities needed to advance the study of convection and turbulence in the atmosphere. These recommendations emerged from a community workshop held on 22–24 May 2017 at the National Center for Atmospheric Research and sponsored by the National Science Foundation. Four areas of research were distinguished at this workshop: i) boundary layer flows, including convective and stable boundary layers over heterogeneous land use and terrain conditions; ii) dynamics and thermodynamics of convection, including deep and shallow convection and continental and maritime convection; iii) turbulence above the boundary layer in clouds and in clear air, terrain driven and elsewhere; and iv) cloud microphysical and chemical processes in convection, including cloud electricity and lightning. The recommendations presented herein address a series of facilities and capabilities, ranging from existing ones that continue to fulfill science needs and thus should be retained and/or incrementally improved, to urgently needed new facilities, to desired capabilities for which no adequate solutions are as yet on the horizon. A common thread among all recommendations is the need for more highly resolved sampling, both in space and in time. Significant progress is anticipated, especially through the improved availability of airborne and ground-based remote sensors to the National Science Foundation (NSF)-supported community. [ABSTRACT FROM AUTHOR]

This review article compiles the characteristics of the gas chlorine nitrate and discusses its role in atmospheric chemistry. Chlorine nitrate is a reservoir of both stratospheric chlorine and nitrogen. It is formed by a termolecular reaction of ClO and NO2. Sink processes include gas-phase chemistry, photo-dissociation, and heterogeneous chemistry on aerosols. The latter sink is particularly important in the context of polar spring stratospheric chlorine activation. ClONO2 has vibrational--rotational bands in the infrared, notably at 779, 809, 1293, and 1735 cm-1, which are used for remote sensing of ClONO2 in the atmosphere. Midinfrared emission and absorption spectroscopy have long been the only concepts for atmospheric ClONO2 measurements. More recently, fluorescence and mass spectroscopic in situ techniques have been developed. Global ClONO2 distributions have a maximum at polar winter latitudes at about 20-30 km altitude, where mixing ratios can exceed 2 ppbv. The annual cycle is most pronounced in the polar stratosphere, where ClONO2 concentrations are an indicator of chlorine activation and de-activation. [ABSTRACT FROM AUTHOR]

Correct estimation of soil and vegetation thermal emissivities is of huge importance in remote sensing studies. It has been shown that the emissivity of leaves retrieved from field observations show subtle spectral features that are related to leaf water content. However, such field measurements provide additional challenges before leaf water content can be successfully obtained, specifically atmospheric correction. The aim of this research was to investigate how information within hyperspectral thermal observations can be used to auto-correct the atmospheric influence. Hyperspectral thermal measurements were taken over a large variety of soil and vegetation types (including vineyard and barley) during ESA’s REFLEX campaign in 2012 using a MIDAC FTIR radiometer. Using MODTRAN simulations, a simple quadratic model was constructed that emulates the atmosphere radiative transfer between the target and the sensor. Afterwards, this model was used to estimate the concentrations of H 2 0 (g) and CO 2 (g) while simultaneously correcting for these gas absorptions. Finally, a temperature-emissivity separation was applied to estimate the emissivities of the different land surface components. The uncertainty of the approach was evaluated by comparing the retrieved gas concentrations against parallel measurements of a LICOR 7500. It was found that most measurements of gas concentrations were successfully retrieved, with uncertainties lower than 25%. However, absolute correction of the absorption features proved more difficult and resulted in overestimations of the correction-terms. This was mainly due to overlapping of spectral features with the observations in the simulations that proved troublesome. [ABSTRACT FROM AUTHOR]

Convective clouds play an essential role for Earth's climate as well as for regional weather events since they have a large influence on the radiation budget and the water cycle. In particular, cloud albedo and the formation of precipitation are influenced by aerosol particles within clouds. In order to improve the understanding of processes from aerosol activation, over cloud droplet growth to changes in cloud radiative properties, remote sensing techniques become more and more important. While passive retrievals for spaceborne observations have become sophisticated and commonplace to infer cloud optical thickness and droplet size from cloud tops, profiles of droplet size have remained largely uncharted territory for passive remote sensing. In principle they could be derived from observations of cloud sides, but faced with with the small-scale structure of cloud sides, classical passive remote sensing techniques are rendered inappropriate. In this work the feasibility is demonstrated to gain new insights into the vertical evolution of cloud droplet effective radius by using reflected solar radiation from cloud sides. Central aspect of this work on its path to a working cloud side retrieval is the analysis of the impact unknown cloud surface geometry has on effective radius retrievals. Using extensive 3D radiative transfer calculations on the basis of realistic droplet size resolving cloud simulations, the sensitivity of reflected solar radiation to cloud droplet size is examined. Sensitivity is enhanced by considering the pixel surrounding to resolve ambiguities caused by illumination and cloud geometry. Based on these findings, a statistical approach is used to provide an effective radius retrieval. An in-depth sensitivity study of the presented approach on the basis of a wide range of radiative transfer test cases demonstrates the feasibility to retrieve cloud particle size profiles from cloud sides. [ABSTRACT FROM AUTHOR]

Clouds are a key factor for the Arctic amplification of global warming, but their actual appearance and distribution are still afflicted by large uncertainty. On the Arctic-wide scale, large discrepancies are found between the various reanalyses and satellite products, respectively. Although ground-based observations by remote sensing are limited to point measurements, they have the advantage of obtaining extended time series of vertically resolved cloud properties. Here, we present a 25-year data record of cloud base height measured by ceilometer at the Ny-Ålesund, Svalbard, Arctic site. We explain the composition of the three sub-periods with different instrumentation contributing to the data set, and show examples of potential application areas. Linked to cyclonic activity, the cloud base height provides essential information for the interpretation of the surface radiation balance and contributes to the understanding of meteorological processes. Furthermore, it is a useful auxiliary component for the analysis of advanced technologies that provide insight into cloud microphysical properties, like the cloud radar. The long-term time series also allows derivation of an annual cycle of the cloud occurrence frequency, revealing the more frequent cloud cover in summer and the lowest cloud cover amount in April. However, as the use of different ceilometer instruments over the years potentially imposed inhomogeneities onto the data record, any long-term trend analysis should be avoided. The Ny-Ålesund cloud base height data from August 1992 to July 2017 are provided in a high temporal resolution of 5 min (1 min) before (after) July 1998, respectively, at the PANGAEA repository (https://doi.org/10.1594/PANGAEA.880300). [ABSTRACT FROM AUTHOR]

Determination of aerosol optical properties with orbital passive remote sensing is a difficult task, as observations often have limited information. Multi-angle instruments, such as the Multi-angle Imaging SpectroRadiometer (MISR) and the POlarization and Directionality of the Earth's Reflectances (POLDER), seek to address this by making information-rich multi-angle observations that can be used to better retrieve aerosol optical properties. The paradigm for such instruments is that each angle view is made from one platform, with, for example, a gimballed sensor or multiple fixed view angle sensors. This restricts the observing geometry to a plane within the scene bidirectional reflectance distribution function (BRDF) observed at the top of the atmosphere (TOA). New technological developments, however, support sensors on small satellites flying in formation, which could be a beneficial alternative. Such sensors may have only one viewing direction each, but the agility of small satellites allows one to control this direction and change it over time. When such agile satellites are flown in formation and their sensors pointed to the same location at approximately the same time, they could sample a distributed set of geometries within the scene BRDF. In other words, observations from multiple satellites can take a variety of view zenith and azimuth angles and are not restricted to one azimuth plane as is the case with a single multi-angle instrument. It is not known, however, whether this is as potentially capable as a multi-angle platform for the purposes of aerosol remote sensing. Using a systems engineering tool coupled with an information content analysis technique, we investigate the feasibility of such an approach for the remote sensing of aerosols. These tools test the mean results of all geometries encountered in an orbit. We find that small satellites in formation are equally capable as multi-angle platforms for aerosol remote sensing, as long as their calibration accuracies and measurement uncertainties are equivalent. As long as the viewing geometries are dispersed throughout the BRDF, it appears the quantity of view angles determines the information content of the observations, not the specific observation geometry. Given the smoothly varying nature of BRDF's observed at the TOA, this is reasonable and supports the viability of aerosol remote sensing with small satellites flying in formation. The incremental improvement in information content that we found with number of view angles also supports the concept of a resilient mission comprised of multiple satellites that are continuously replaced as they age or fail. [ABSTRACT FROM AUTHOR]

In support of infrared remote sensing of Titan, we present temperature dependent cross-sections of propene (C 3 H 6 ; CH 2 CH CH 3 ; propylene) measured in the laboratory. A total of 27 high-resolution (up to 0.0022 cm − 1 ) spectra of pure C 3 H 6 and mixtures of C 3 H 6 with N 2 were obtained at 150–299 K in the 650–1534 cm −1 (6.5–15.4 µm) region. A custom-designed cold cell was used, which was configured with an active temperature control and could be integrated to a Fourier-transform spectrometer (Bruker 125HR) at the Jet Propulsion Laboratory. The observed C 3 H 6 spectral features include its strong v 19 band bearing the most prominent Q-branch peak at 912 cm −1 and three other strong bands: v 18 , v 16 and v 7 centered near 991, 1443, and 1459 cm −1 , respectively. In addition, we have generated empirical pseudoline lists (PLLs) in HITRAN-format, in the two separate spectral regions, which are Region I: 800–1100 and Region II: 1340–1524 cm −1 . The PLLs of C 3 H 6 consists of spectroscopic line parameters (including line intensities and effective lower state energies) for all the individual pseudoline positions. The pseudoline parameters were determined by fitting 27 laboratory spectra of pure and N 2 -broadened propene simultaneously in the selected regions. A newly derived partition function was adopted in the line-by-line radiative transfer calculations. Based on the pseudoline intensities, the total integrated intensities in the 800–1120 and 1320–1524cm −1 regions were measured to be 8.79(47)× 10 −18 and 3.06(21)×10 −18 cm −1 /(molecule cm −2 ) at 296 K, respectively. These values are found to be in a good agreement with the recent measurement made at KAUST at room temperature, but they are significantly lower than those reported by one of the most extensive previous studies at the Pacific Northwest National Laboratory (PNNL). Finally, the two PLLs are submitted as electronic supplements and are also available from the website, https://mark4sun.jpl.nasa.gov/pseudo.html. [ABSTRACT FROM AUTHOR]

Artificially triggered lightning events from May 26, 2017 to July 16, 2017 in Guangzhou Field Experiment Site for Lightning Research and Test (GFESL) were intentionally remotely sensed with a ground-based microwave radiometer for the first time in order to obtain the features of lightning heating effect. The microwave radiometer antenna was adjusted to point at a certain elevation angle towards the expected artificially triggered lightning discharging path. Eight of the 16 successfully artificially triggered lightning events were captured and the brightness temperature data at four frequencies in K and V bands were obtained. The results from data time series analysis show that artificially triggered lightning can make the radiometer generate brightness temperature pulses, and the amplitudes of these pulses are in the range of 2.0 K to 73.8 K. The brightness temperature pulses associated with 7 events can be used to estimate the duration of lightning heating effect through accounting the number of the pulses in the continuous pulse sequence and the sampling interval between four frequencies. The maximum duration of the lightning heating effect is 1.13 s, the minimum is 0.172 s, and the average is 0.63 s. [ABSTRACT FROM AUTHOR]

This paper presents results of coordinated ultraviolet and visible wavelength reflectance measurements, X-ray diffraction analyses of mineral components, and micro X-ray fluorescence analyses of Ti concentrations of 13 lunar soil samples (<210 µm) that span a range of maturity and TiO 2 contents. Results of these analyses are used to determine the effects of soil maturity, TiO 2 concentration, and specific mineralogical makeup, especially ilmenite content, on the ultraviolet/visible (UV/VIS) ratio for application to remote sensing observations of the Moon and other airless bodies. We find that measured ilmenite weight percent correlates highly with measured TiO 2 concentrations. Thus, the ilmenite content is a good predictor of TiO 2 concentration. Ilmenite is the main contributor of TiO 2 for soils with greater than about 2 wt.% TiO 2 , so we take the effects of TiO 2 on reflectance spectra to be essentially those of ilmenite. Constraining the data set to eight mature Apollo soils, we find that among the UV/VIS ratios from laboratory-measured spectra, the 321/415 nm ratio shows the best correlation with TiO 2 and ilmenite. Moreover, for soils with similar maturity in the submature to mature range, those with higher TiO 2 have higher 321/415 UV/VIS ratios. Finally, the correlation between TiO 2 content and 321/415 ratio in samples measured in the lab appears weaker than for the same relationship using the Lunar Reconnaissance Orbiter (LRO) Wide Angle Camera (WAC) spectral data for the 321/415 ratio of Apollo ground-truth sites. The correlation between lab-derived 321/415 ratios and TiO 2 content for measured samples improves when low-maturity samples are excluded from the dataset, implying that the LROC WAC spectra at 400 m/pix spatial resolution senses mostly mature soil. [ABSTRACT FROM AUTHOR]

A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR). [ABSTRACT FROM AUTHOR]

In studies involving the extraction of surface physical parameters using optical remote sensing satellite imagery, sun-sensor geometry must be known, especially for sensor viewing angles. However, while pixel-by-pixel acquisitions of sensor viewing angles are of critical importance to many studies, currently available algorithms for calculating sensor-viewing angles focus only on the center-point pixel or are complicated and are not well known. Thus, this study aims to provide a simple and general method to estimate the sensor viewing angles pixel by pixel. The Rational Function Model (RFM) is already widely used in high-resolution satellite imagery, and, thus, a method is proposed for calculating the sensor viewing angles based on the space-vector information for the observed light implied in the RFM. This method can calculate independently the sensor-viewing angles in a pixel-by-pixel fashion, regardless of the specific form of the geometric model, even for geometrically corrected imageries. The experiments reveal that the calculated values differ by approximately 10-40 for the Gaofen-1 (GF-1)Wide-Field-View-1 (WFV-1) sensor, and by ~10-70 for the Ziyuan-3 (ZY3-02) panchromatic nadir (NAD) sensor when compared to the values that are calculated using the Rigorous Sensor Model (RSM), and the discrepancy is analyzed. Generally, the viewing angles for each pixel in imagery are calculated accurately with the proposed method. [ABSTRACT FROM AUTHOR]

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984-2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York,Melbourne, andMunich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984-2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy tomaximize information gain fromlimited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984-2016 is 89% for Beijing, 94% for New York, 93% forMelbourne, and 89% forMunich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data. [ABSTRACT FROM AUTHOR]

Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10× and 2.5× for airplane and ship, and a detection speedup of maximal 7.2×, 4.1× and 2.5× on three test sets, respectively. [ABSTRACT FROM AUTHOR]

Land use is of great importance for urban planning, environmental monitoring, and transportation management. Several methods have been proposed to obtain land use maps of urban areas, and these can be classified into two categories: remote sensing methods and social sensing methods. However, remote sensing and social sensing approaches have specific disadvantages regarding the description of social and physical features, respectively. Therefore, an appropriate fusion strategy is vital for large-area land use mapping. To address this issue, we propose an efficient land use mapping method that combines remote sensing imagery (RSI) and mobile phone positioning data (MPPD) for large areas. We implemented this method in two steps. First, a support vector machine was adopted to classify the RSI and MPPD. Then, the two classification results were fused using a decision fusion strategy to generate the land use map. The proposed method was applied to a case study of the central area of Beijing. The experimental results show that the proposed method improved classification accuracy compared with that achieved using MPPD alone, validating the efficacy of this new approach for identifying land use. Based on the land use map and MPPD data, activity density in key zones during daytime and nighttime was analyzed to illustrate the volume and variation of people working and living across different regions. [ABSTRACT FROM AUTHOR]

With the large number of high-resolution images now being acquired, high spatial resolution (HSR) remote sensing imagery scene classification has drawn great attention but is still a challenging task due to the complex arrangements of the ground objects in HSR imagery, which leads to the semantic gap between low-level features and high-level semantic concepts. As a feature representation method for automatically learning essential features from image data, convolutional neural networks (CNNs) have been introduced for HSR remote sensing image scene classification due to their excellent performance in natural image classification. However, some scene classes of remote sensing images are object-centered, i.e., the scene class of an image is decided by the objects it contains. Although previous methods based on CNNs have achieved comparatively high classification accuracies compared with the traditional methods with handcrafted features, they do not consider the scale variation of the objects in the scenes. This makes it difficult to directly utilize CNNs on those remote sensing images belonging to object-centered classes to extract features that are robust to scale variation, leading to wrongly classified scene images. To solve this problem, scene classification based on a deep random-scale stretched convolutional neural network (SRSCNN) for HSR remote sensing imagery is proposed in this paper. In the proposed method, patches with a random scale are cropped from the image and stretched to the specified scale as the input to train the CNN. This forces the CNN to extract features that are robust to the scale variation. Furthermore, to further improve the performance of the CNN, a robust scene classification strategy is adopted, i.e., multi-perspective fusion. The experimental results obtained using three datasets--the UC Merced dataset, the Google dataset of SIRI-WHU, and the Wuhan IKONOS dataset--confirm that the proposed method performs better than the traditional scene classification methods. [ABSTRACT FROM AUTHOR]

Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data. [ABSTRACT FROM AUTHOR]

Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud,wave, island,wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regionswhich allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts. [ABSTRACT FROM AUTHOR]

Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal. [ABSTRACT FROM AUTHOR]

The Colville is an Arctic river in the Alaska North Slope. The residents of Nuiqsut rely heavily on the Colville for their subsistence needs. Increased erosion has been reported on the Colville, especially along bluffs, which shaped the goals of this study: to use remote sensing techniques to map and quantify erosion rates and the volume of land loss at selected bluff sites along the main channel of the Colville, and to assess the suitability of automated methods of regional erosion monitoring. We used orthomosaics from high resolution aerial photos acquired in 1955 and 1979/1982, as well as high resolutionWorldView-2 images from 2015 to quantify long-term erosion rates and the cubic volume of erosion. We found that, at the selected sites, erosion rates averaged 1 to 3.5 m per year. The erosion rate remained the same at one site and increased from 1955 to 2015 at two of the four sites. We estimated the volume of land loss to be in the magnitude of 166,000 m3 to 2.5 million m3 at our largest site. We also found that estimates of erosion were comparable for manual hand-digitized and automated methods, suggesting our automated method was effective and can be extended to monitor erosion at other sites along river systems that are bordered by bluffs. [ABSTRACT FROM AUTHOR]

Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called "hyperclouds", i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements. [ABSTRACT FROM AUTHOR]

Estimates of temperate mangrove forest cover are required for management of estuarine ecosystems, particularly in areas experiencing rapid change in mangrove distribution. However, it remains challenging to obtain accurate estimates of temperate mangrove cover using remote sensing because of the unique physical features and environmental conditions of temperate mangroves. The objectives of this study were (1) to develop an improved image analysis approach for estimating temperate mangrove forest cover using remote sensing and (2) to test the new approach by comparing its accuracy and uncertainty with those of traditional image analysis. The study area (around 1500 ha) is located in the southern part of the Waitemata Harbour, Auckland, New Zealand. Landsat images and field surveys were used for mapping, and uncertainty was quantified using a Monte Carlo approach. This study showed that, using a traditional approach of mapping, misclassification was the highest source of uncertainty (up to 19% for dwarf mangroves and 16% for tall mangroves), followed by water column effects (up to 7% for dwarf mangroves and 5% for tall mangroves) and positional errors (up to 4% for dwarf mangroves and 5% for tall mangroves). Improved image analysis enhanced accuracy from 72% to 95% for tall mangroves and from 69% to 90% for dwarf mangroves. The improved approach minimized the overall uncertainty by up to 68% for tall mangroves and 57% for dwarf mangroves. Adopting this innovative approach to image analysis can improve accuracy of estimates of long-term trends in temperate mangrove forest cover. [ABSTRACT FROM PUBLISHER]

Landscape dynamics is the result of interactions between social systems and the environment, these systems evolving significantly over time. climatic conditions and biophysical phenomena are the main factors of landscape dynamics. Also, currently man is responsible for most changes affecting natural ecosystems. The objective of this work is to study the dynamics of a typical landscape of western Algeria in time and space, and to map the distribution of vegetation groups constitute the vegetation cover of this ecosystem. as well as using a method of monitoring the state of a fragile ecosystem by remote sensing to understand the processes of changes in this area. The steppe constitutes a large arid area, with little relief, covered with low and sparse vegetation. it lies between the annual isohyets of 100 to 400 mm, subjected to a very old human exploitation with an activity of extensive breeding of sheep, goats, and camels. Landsat satellite data were used to mapping vegetation groups in the Mecheria Steppe at a scale of 1: 300,000. Then, a comparison was made between the two maps obtained by a classification of Landsat-8 sensor Operational Land Imager (OLI) acquired on March 18, 2014, and Landsat-5 sensor Thematic Mapper (TM) acquired on April 25, 1987. The results obtained show the main changes affecting the natural distribution of steppe species, a strong change in land occupied by the Stipa tenacissima steppe with 65% of change, this steppe is replaced by Thymelaea microphylla, Salsola vermiculata, lygeum spartum and Peganum harmala steppe. an absence from the steppe Artemisia herba-alba that has also been replaced by the same previous steppes species. The groups with Quercus ilex and Juniperus phoenicea are characterized by a strong regression that was lost 60% of its global surface and transformed by steppe to stipa tenacissima and bare soil. [ABSTRACT FROM AUTHOR]

Less than 0.25% of the 250 000 glaciers inventoried in the Randolph Glacier Inventory (RGI V.5) are currently monitored with in situ measurements of surface mass balance. Increasing this archive is very challenging, especially using time-consuming methods based on in situ measurements, and complementary methods are required to quantify the surface mass balance of unmonitored glaciers. The current study relies on the so-called albedo method, based on the analysis of albedo maps retrieved from optical satellite imagery acquired since 2000 by the MODIS sensor, on board the TERRA satellite. Recent studies revealed substantial relationships between summer minimum glacier-wide surface albedo and annual surface mass balance, because this minimum surface albedo is directly related to the accumulation-area ratio and the equilibrium-line altitude. On the basis of 30 glaciers located in the French Alps where annual surface mass balance data are available, our study conducted on the period 2000-2015 confirms the robustness and reliability of the relationship between the summer minimum surface albedo and the annual surface mass balance. For the ablation season, the integrated summer surface albedo is significantly correlated with the summer surface mass balance of the six glaciers seasonally monitored. These results are promising to monitor both annual and summer glacier-wide surface mass balances of individual glaciers at a regional scale using optical satellite images. A sensitivity study on the computed cloud masks revealed a high confidence in the retrieved albedo maps, restricting the number of omission errors. Albedo retrieval artifacts have been detected for topographically incised glaciers, highlighting limitations in the shadow correction algorithm, although interannual comparisons are not affected by systematic errors. [ABSTRACT FROM AUTHOR]

The European Organisation for the Exploitation of Meteorological Satellite (EUMETSAT) established the Satellite Application Facility on Support to Operational Hydrology and Water management (H-SAF) on July 2005. The aim of the H-SAF is to derive parameters which are important for hydrology, for hydrological models. H-SAF derives precipitation, soil moisture, and snow products based on satellite information, and makes hydrological validation. The Hungarian Meteorological Service takes part in the product validation work. In this paper we describe the different products, then the validation activities are shown including some examples. [ABSTRACT FROM AUTHOR]

Calculation errors in ocean-brightness coefficients in the Far Eastern are analyzed for two atmospheric correction algorithms (NIR and MUMM). The daylight measurements in different water types show that the main error component is systematic and has a simple dependence on the magnitudes of the coefficients. The causes of the error behavior are considered. The most probable explanation for the large errors in ocean-color parameters in the Far East is a high concentration of continental aerosol absorbing light. A comparison between satellite and in situ measurements at AERONET stations in the United States and South Korea has been made. It is shown the errors in these two regions differ by up to 10 times upon close water turbidity and relatively high aerosol optical-depth computation precision in the case of using the NIR correction of the atmospheric effect. [ABSTRACT FROM AUTHOR]

Diesel-powered vehicles are intensively used in urban areas for transporting goods and people but can substantially contribute to high emissions of black carbon (BC), organic carbon (OC), and other gaseous pollutants. Strategies aimed at controlling mobile emissions sources thus have the potential to improve air quality and help mitigate the impacts of air pollutants on climate, ecosystems, and human health. However, in developing countries there are limited data on the BC and OC emission characteristics of diesel-powered vehicles, and thus there are large uncertainties in the estimation of the emission contributions from these sources. We measured BC, OC, and other inorganic components of fine particulate matter (PM), as well as carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), ethane, acetylene, benzene, toluene, and C2-benzenes under real-world driving conditions for 20 diesel-powered vehicles encompassing multiple emission level technologies in Mexico City with the chasing technique using the Aerodyne mobile laboratory. Average BC emission factors ranged from 0.41- 2.48 g kg-1 of fuel depending on vehicle type. The vehicles were also simultaneously measured using the cross-road remote sensing technique to obtain the emission factors of nitrogen oxide (NO), CO, total hydrocarbons, and fine PM, thus allowing for the intercomparison of the results from the two techniques. There is overall good agreement between the two techniques and both can identify high and low emitters, but substantial differences were found in some of the vehicles, probably due to the ability of the chasing technique to capture a larger diversity of driving conditions in comparison to the remote sensing technique. A comparison of the results with the US EPA MOVES2014b model showed that the model underestimates CO, OC, and selected VOC species, whereas there is better agreement for NOx and BC. Larger OC/BC ratios were found in comparison to ratios measured in California using the same technique, further demonstrating the need for using locally obtained diesel-powered vehicle emission factor database in developing countries in order to reduce the uncertainty in the emissions estimates and to improve the evaluation of the effectiveness of emissions reduction measures. [ABSTRACT FROM AUTHOR]

Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore large (60 m/pixel) and small scale (individual building level) temperature distribution patterns from thermal remote sensing data and to conclude what kind of information could be extracted from thermal remote sensing on regular basis. Landsat program provides frequent large scale thermal images useful for analysis of city temperature patterns. During the study correlation between temperature patterns and vegetation content based on NDVI and building coverage based on OpenStreetMap data was studied. Landsat based temperature patterns were independent from the season, negatively correlated with vegetation content and positively correlated with building coverage. Small scale analysis included spatial and raster descriptor analysis for polygons corresponding to roofs of individual buildings for evaluating insulation of roofs. Remote sensing and spatial descriptors are poorly related to heat consumption data, however, thermal aerial data median and entropy can help to identify poorly insulated roofs. Automated quantitative roof analysis has high potential for acquiring city wide information about roof insulation, but quality is limited by reference data quality and information on building types, and roof materials would be crucial for further studies. [ABSTRACT FROM AUTHOR]

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup. [ABSTRACT FROM AUTHOR]

Landslides are frequent events that may cause human casualties and injuries as well as damage to urban and man-made structures, with extensive loss of economic resources. For this reason, landslide mapping is a primary tool for hazard and risk assessment. Italian Ministry of Environment, thanks to great availability and functionality of Synthetic Aperture Radar (SAR) data promoted the Not-ordinary Plan of Environmental Remote Sensing (Piano Straordinario di Telerilevamento Ambientale, PST-A in Italian) in 2008, as to constitute a national database of active or potential instability phenomena affecting the Italian territory, based on the exploitation of interferometric products (ERS and ENVISAT). In this paper, the PST-A-3 is described. A procedure based on the integration of engineering-geological approaches and SAR interferometry data belonging to COSMO-SkyMed constellation (100 frames 40 × 40 km) has been here implemented over 7,400 km 2 of the Italian territory. First, landslides have been mapped by field geologists, defining type and state of activity. Simultaneously to field surveys, remote sensing data have been analyzed as to detect areas with considerable displacement registered by the satellite. Both products have been overlaid, also quantifying the coincidence between the events reported according to the two detection methodologies and subtracting those landslide not recordable by the satellite, finally obtaining an updated landslide inventory map with 4,522 newly detected phenomena. Therefore, PST-A-3 proves to be a valuable system for local authorities, in order to provide a contribution to risk management but also for the forecasting of landslide events, as testified by two case studies selected. Thanks to the PST-A experience, the use of such strategy to other countries could represent a valid contribution to land management at worldwide scale. [ABSTRACT FROM AUTHOR]

OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017. [ABSTRACT FROM AUTHOR]

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors. [ABSTRACT FROM AUTHOR]

The systematic underestimation observed in debris flow early warning thresholds has been associated with the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris-flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine-resolution radar data for ~100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1-20km grid size) on the estimation of debris-flow-triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show 8-21% variation in the parameters whereas 10-25% systematic variation results from the use of rain gauge networks, even for densities as high as 1/10km-2. [ABSTRACT FROM AUTHOR]

Many remote sensing based spring phenology products have been developed to monitor and study vegetation phenology at regional and global scales. It is important to understand how these products perform relative to each other and to ground observations. In this study, we extracted spring green-up onset dates (GUD) over the contiguous United States (CONUS) from six major land surface phenology (LSP) products: (1) Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Dynamics Phenology (MCD12Q2); (2) Vegetation Index and Phenology Multi-sensor Phenology (VIPPHENEVI2); (3) Global Long-Term Climate Modeling Grid Land Surface Phenology (CMGLSP); (4 and 5) North American Carbon Program (NACP) Phenology (MOD09Q1PEVI and MOD15PHN); and (6) USGS/EROS advanced very high resolution radiometer (AVHRR) phenology (AVHRRP). We characterized and compared the GUD data in these LSP products, and evaluated their accuracy using ground-based phenology observations [i.e., human observations of first leaf and sensor readings of gross primary productivity (GPP)] from the USA National Phenology Network (USA-NPN) and AmeriFlux. The results revealed the consistencies and discrepancies of GUD estimates among LSP products. Intercomparison of the six products indicated that the root mean square error (RMSE) of these products range from 17.8 days to 31.5 days, whereas AVHRRP GUD has the lowest correlation and largest RMSE (∼30 days) relative to other products. When compared to ground observations, GUD estimates in six LSP products generally have RMSE values of ∼20 days and significant correlations ( p < 0.001). For the products (MCD12Q2, AVHRRP, MOD09Q1PEVI, and MOD15PHN) available for comparisons in the short-term period (from 2001–2007), AVHRRP GUD presented relatively weaker correlations and a lower index of agreement (IOA), however, MCD12Q2 GUD showed overall slightly better consistencies with ground observations. In the two long-term products (CMGLSP and VIPPHENEVI2 from 1982–2013), CMGLSP exhibited stronger correlations, lower RMSE, and higher IOA with ground observations of the first leaf dates than VIPPHENEVI2 did. To our knowledge, our study provides the first comprehensive evaluation of phenology products using two independent ground-based datasets. [ABSTRACT FROM AUTHOR]

In this paper, the retrieving significant wave height from X-band marine radar images based on shadow statistics is investigated, since the retrieving accuracy can not be seriously affected by environmental factors and the method has the advantage of without any external reference to calibrate. However, the accuracy of the significant wave height estimated from the radar image acquired at the near-shore area is not ideal. To solve this problem, the effect of water depth is considered in the theoretical derivation of estimated wave height based on the sea surface slope. And then, an improved retrieving algorithm which is suitable for both in deep water area and shallow water area is developed. In addition, the radar data are sparsely processed in advance in order to achieve high quality edge image for the requirement of shadow statistic algorithm, since the high resolution radar images will lead to angle-blurred for the image edge detection and time-consuming in the estimation of sea surface slope. The data acquired from Pingtan Test Base in Fujian Province were used to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the improved method which takes into account the water depth is more efficient and effective and has better performance for retrieving significant wave height in the shallow water area, compared to the in situ buoy data as the ground truth and that of the existing shadow statistic method. [ABSTRACT FROM AUTHOR]

This paper examines how scientists deal with tensions emerging from their role as providers of objective knowledge and as citizens concerned with how their research influences policy and politics in Brazil. This is accomplished through an ethnographic account of scientists using remote sensing technology, of their knowledge-making activities and of the broader socio-political controversies that permeate the detection of deforestation in the Amazon rainforest. Strategies for mitigating uncertainty are central aspects of the knowledge practices analyzed, bringing controversies ‘external’ to the laboratory ‘into’ the lab, making these boundaries conceptually problematic. In particular, the anticipation of alternative interpretations of rainforest cover is a crucial way that scientists bring the world into the lab, helping to shed light on how scientists, usually seen and analyzed as isolated, are in fact often in constant dialogue with the broader political controversies related to their work. These insights help question the idea that the monitoring of deforestation through remote sensing is a form of secluded research, drawing a more complex picture of the dual role of scientists as knowledge producers and concerned citizens. [ABSTRACT FROM AUTHOR]

Large discrepancies have been observed between satellite-derived sea-ice concentrations(IC) from passive microwave remote sensing and those derived from optical images at several locations in the East Antarctic, between February and April 2014. These artefacts, that resemble polynyas in the IC maps, appear in areas where optical satellite data show that there is landfast sea ice. The IC datasets and the corresponding retrieval algorithms are investigated together with microwave brightness temperature, air temperature, snowfall and bathymetry to understand the failure of the IC retrieval. The artefacts are the result of the application of weather filters in retrieval algorithms. These filters use the 37 and 19 GHz channels to correct for atmospheric effects on the retrieval. These channels show significant departures from typical ranges when the artefacts occur. A melt–refreeze cycle with associated snow metamorphism is proposed as the most likely cause. Together, the areas of the artefacts account for up to 0.5% of the Antarctic sea-ice area and thus cause a bias in sea-IC time series. In addition, erroneous sea ICs can adversely affect shipping operations. [ABSTRACT FROM AUTHOR]

In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O2-B and the deep O2-A absorption bands present a high sensitivity to detect SIF, these regions are also largely influenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., first an AC and second a Spectral Fitting technique to disentangle SIF from reflected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reflectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reflectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reflectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefficient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reflectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation. [ABSTRACT FROM AUTHOR]

Microwave vegetation index (MVI) is a vegetation index defined in microwave bands. It has been developed based on observations from AMSR-E and widely used to monitor global vegetation. Recently, our study found that MVI was influenced by the atmosphere, although it was calculated from microwave bands. Ignoring the atmospheric influence might bring obvious uncertainty to the study of global vegetation. In this study, an atmospheric effect sensitivity analysis for MVI was carried out, and an atmospheric correction algorithm was developed to reduce the influence of the atmosphere. The sensitivity analysis showed that water vapor, clouds and precipitation were main parameters that had an influence on MVI. The result of the atmospheric correction on MVI was validated at both temporal and spatial scales. The validation showed that the atmospheric correction algorithm developed in this study could obviously improve the underestimation of MVI on most land surfaces. Seasonal patterns in the uncorrected MVI were obviously related to atmospheric water content besides vegetation changes. In addition, global maps of MVI showed significant differences before and after atmospheric correction in the northern hemisphere in the northern summer. The atmospheric correction will make the MVI more reliable and improve its performance in calculating vegetation biomass. [ABSTRACT FROM AUTHOR]

The arid valley is a unique type of ecological fragile landscape in the Hengduan Mountain Area, China. The boundary of the arid valley is one of the response indicators to mountainous climate change. Based on the meteorological data from 1999 to 2013 and the SPOT remote sensing images in 1999 and 2013 this study explored the response characteristics of the arid valley boundary to regional climate change in the upper reaches of the Min River in the Hengduan Mountains. The results are as follows: (1) During 1999-2013 the temperature, precipitation and evaporation increased and the sunshine duration and relative humidity showed decreasing trends at the rates of 0.008 °C/a, 2.25 mm/a, 5.51 mm/a, -8.72 h/a and -0.19%/a, respectively. Meanwhile the climate showed the warm-dry tendency in the southern region and the warm-humid tendency in the central and northern areas. (2) On the whole the arid valley boundary mainly distributed between 1601-3200 m and moved downward to 2428 m at the speed of -0.76 u 0.26 m/a along with global warming. The descent speeds in different regions showed the same decreasing order as the regional distributions of precipitation and sunshine duration. (3) The arid valley boundary's displacement in the whole basin had significant negative correlations with current climate change (ρ < 0.05), as well as with variations of moisture factors. Additionally, with the enhancements of the drought degree and humidity tendency, the variations of temperature, evaporation and relative humidity, respectively, became the main factors that had significant correlations with the arid valley boundary's displacement. Therefore, climate change during 1999-2013 shows beneficial effects on the improvement of the arid valley habitat in the upper reaches of the Min River. The study provides a new method and gives basic data for research on climate change. [ABSTRACT FROM AUTHOR]