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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]

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]

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]

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]

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]

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]

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]

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]

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]

Many seabird species forage at night and potentially interact with nocturnal fishing activities. Jigging fisheries use powerful lights to attract squid, and such high intensity lights can be recorded using global location-sensing loggers (geolocators) attached to seabirds. We use this potential source of information as evidence for interaction of southern giant petrels Macronectes giganteus with night fisheries during the non-breeding season. We compared the number of light spikes at night between sexes and evaluated whether the intensity of the light on those geolocator records matched periods of water immersion (wet-dry) of geolocators, as a measure of foraging activity. Females had more night light spikes than males, and although the activity on water was higher during nights with light spikes than nights without light spikes for both sexes, females had a higher probability to be resting on the water when peaks of light were higher. Females moved further north than males and used areas of higher squid fishery activities within Patagonian waters. This type of information is useful to record potential interactions with night fisheries and proposes that future studies should relate the accurate distribution of individuals (from GPS loggers) with light information (geolocators data) to highlight this undocumented interaction. Southern giant petrels are recognized as interacting intensively with fisheries off Patagonia waters with consequences for population dynamics (e.g. mortality through bycatch events). [ABSTRACT FROM AUTHOR]

The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρω) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. ρω relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in ρω retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries' monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions. [ABSTRACT FROM AUTHOR]

Two post-real time precipitation products from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) are systematically evaluated over China with China daily Precipitation Analysis Product (CPAP) as reference. The IMERG products include the gauge-corrected IMERG product (IMERG_Cal) and the version of IMERG without direct gauge correction (IMERG_Uncal). The post-research TRMM Multisatellite Precipitation Analysis version 7 (TMPA-3B42V7) is also evaluated concurrently with IMERG for better perspective. In order to be consistent with CPAP, the evaluation and comparison of selected products are performed at 0.25° and daily resolutions from 12 March 2014 through 28 February 2015. The results show that: Both IMERG and 3B42V7 show similar performances. Compared to IMERG_Uncal, IMERG_Cal shows significant improvement in overall and conditional bias and in the correlation coefficient. Both IMERG_Cal and IMERG_Uncal perform relatively poor in winter and over-detect slight precipitation events in northwestern China. As an early validation of the GPM-era IMERG products that inherit the TRMM-era global satellite precipitation products, these findings will provide useful feedbacks and insights for algorithm developers and data users over China and beyond. [ABSTRACT FROM AUTHOR]

Analyzing temporal series of satellite data for regional scale studies demand high accuracy in calibration and precise geo-rectification at higher spatial resolution. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) series of satellites provide daily observations for the last 30 years at a nominal resolution of 1.1 km at nadir. However, complexities due to on-board malfunctions and orbital drifts with the earlier missions hinder the usage of these images at their original resolution. In this study, we developed a new method using multiple open source tools which can read level 1B radiances, apply solar and thermal calibration to the channels, remove bow-tie effects on wider zenith angles, correct for clock drifts on earlier images and perform precise geo-rectification by automated generation and filtering of ground control points using a feature matching technique. The entire workflow is reproducible and extendable to any other geographical location. We developed a time series of brightness temperature maps from AVHRR local area coverage images covering the sub alpine lakes of Northern Italy at 1 km resolution (1986-2014; 28 years). For the validation of derived brightness temperatures, we extracted Lake Surface Water Temperature (LSWT) for Lake Garda in Northern Italy and performed inter-platform (NOAA-x vs. NOAA-y) and cross-platform (NOAA-x vs. MODIS/ATSR/AATSR) comparisons. The MAE calculated over available same day observations between the pairs--NOAA-12/14, NOAA-17/18 and NOAA-18/19 are 1.18 K, 0.67 K, 0.35 K, respectively. Similarly, for cross-platform pairs, the MAE varied between 0.5 to 1.5 K. The validation of LSWT from various NOAA instruments with in-situ data shows high accuracy with mean R² and RMSE of 0.97 and 0.91 K respectively. [ABSTRACT FROM AUTHOR]

This study evaluates a remotely sensed and two ground-based potential evapotranspiration ( PET) products for hydrologic application in the Upper Colorado River Basin ( UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product ( MODIS- PET) is a continuous, daily time series with 250 m resolution derived using the Priestley-Taylor (P-T) equation. The MODIS- PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System ( NLDAS) and a Hargreaves PET derived from DAYMET variables ( DAYMET- PET; 1 km, daily). Compared to point-scale PET computed using regional flux tower data, the MODIS- PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET- PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS- PET values, while bias in NLDAS temperature, downward shortwave ( SW↓), and downward longwave ( LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS- PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite-based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB. [ABSTRACT FROM AUTHOR]

The optimal spectral sampling (OSS) method provides a fast and accurate way to model radiometric observations and their Jacobians (required for inversion problems) as a linear combination of monochromatic quantities. The method is flexible and versatile with respect to the treatment of variable constituents, and the method's fidelity to reference line-by-line (LBL) calculations is tunable. The focus of this paper is on the modeling of radiances from hyperspectral infrared sounders in both clear and cloudy (scattering) atmospheres for application to retrieval and data assimilation. In earlier articles, the authors presented an approach that performed spectral sampling for each channel sequentially. This approach is particularly robust in terms of preserving fidelity to LBL models and yields ratios of monochromatic calculations per channel of approximately 1:1 for such hyperspectral sensors as the Infrared Atmospheric Sounding Interferometer (IASI) or the Atmospheric Infrared Sounder (AIRS) (when tuned for nominal 0.05-K accuracy). This paper describes the generalization of the OSS concept to minimize the total number of monochromatic points required to model a set of channels across individual spectral bands or across the entire domain of the measurements. Its application to principal components of radiance measurements is addressed. It is found that the optimal solution produced by the OSS method offers computational advantages over existing models based on principal components, but, more importantly, it has superior error characteristics. [ABSTRACT FROM AUTHOR]

By using field in situ observations, data from the NOMAD database, and remotely sensed data from MODIS-Aqua, an improved algorithm has been developed for estimating the surface concentrations of dissolved organic carbon (DOC) in the No rthern So uth C hina Sea S helf-sea (NoSoCS) and adjacent waters through a combination of the remotely sensed sea-surface temperature (SST) and the absorption coefficient of colored dissolved organic matter (CDOM). Unlike previously reported algorithms, which were based on only one of these two parameters, this algorithm does not require seasonal tuning and is applicable to both coastal and pelagic waters. The uncertainty in the remotely sensed concentrations of DOC is about ±4–10 µM. The climatological distributions of the satellite-derived surface DOC in the NoSoCS and adjacent waters between 2002 and 2012 followed a general trend of increasing concentrations landward, from about 58 and 73 μM in the open South China Sea (SCS) in January and July, respectively, to >100 μM at the mouth of the Pearl River. The intra-annual changes in the monthly average concentration of DOC in the NoSoCS follow a distinct seasonal cycle, with the lowest and highest concentrations, 56 μM and 75 μM, in February and July, respectively. The seasonal cycle of DOC was in phase with that of SST but out of phase with that of primary production, suggesting that biological activity was not likely its primary control. Instead, it was consistent with the entrainment of the cold and less DOC-rich sub-surface water in the surface water by enhanced vertical mixing during winter, and the high inflow of organic-rich river water to the NoSoCS in summer. Sub-regionally, lower concentrations of DOC were found in the known upwelling zones in the NoSoCS, where vertical mixing was enhanced. In the inner shelf, the effect of riverine input was indicated by higher concentrations of DOC in the plume of water at the mouth of the Pearl River. This work represents the first attempt to characterize the regional distribution of surface concentration of DOC in the NoSoCS and adjacent waters. [ABSTRACT FROM AUTHOR]

The violent storms of 22-30 November 2014, resulted in flash floods and wadi floods (rivers) in large parts of Southern Morocco, at the foot of the Atlas Mountains. The Guelmim area was the most affected part with at least 32 fatalities and damages due to inundations. The flooding hazard in the Guelmim region initiated this study in order to investigate the use of remote sensing and geographic information system (GIS) for the detection and identification of areas most likely to be flooded in the future again due to their morphologic properties during similar weather conditions. By combining morphometric analysis and visual interpretation based on Landsat 8 satellite data and derived images such as water index (NDWI) images, areas with relatively higher soil moisture and recently deposited sediments were identified. The resulting maps of weighted overlay procedures, aggregating causal, morphometric factors influencing the susceptibility to flooding (lowest height levels, flattest areas), allowed for the distinguishing of areas with higher, medium and lower susceptibility to flooding. Thus, GIS and remote sensing tools contribut to the recognition and mapping of areas and infrastructure prone to flooding in the Guelmim area. [ABSTRACT FROM AUTHOR]

A new procedure is proposed for estimating actual basal crop coefficients from vegetation indices (Kcb VI) considering a density coefficient (Kd) and a crop coefficient for bare soil. Kd is computed using the fraction of ground cover by vegetation (fc VI), which is also estimated from vegetation indices derived from remote sensing. A combined approach for estimating actual crop coefficients from vegetation indices (Kc VI) is also proposed by integrating the Kcb VI with the soil evaporation coefficient (Ke) derived from the soil water balance model SIMDualKc. Results for maize, barley and an olive orchard have shown that the approaches for estimating both fc VI and Kcb VI compared well with results obtained using the SIMDualKc model after calibration with ground observation data. For the crops studied, the correlation coefficients relative to comparing the actual Kcb VI and Kc VI with actual Kcb and Kc obtained with SIMDualKc were larger than 0.73 and 0.71, respectively. The corresponding regression coefficients were close to 1.0. The methodology herein presented and discussed allowed for obtaining information for the whole crop season, including periods when vegetation cover is incomplete, as the initial and development stages. Results show that the proposed methods are adequate for supporting irrigation management. [ABSTRACT FROM AUTHOR]

Strong evidence for a pan-Arctic expansion of upright shrubs comes from analysis of satellite imagery, historical photographs, vegetation plots, and growth rings. However, there are still uncertainties related to local-scale patterns of shrub growth, resulting interactions among vegetation functional groups, and the relative roles of disturbance and climate as drivers of observed change. Here, we present evidence that widespread and rapid shrub expansion and lichen declines over a 15,000 km area of the western Canadian Arctic have been driven by regional increases in temperature. Using 30 m resolution Landsat satellite imagery and high resolution repeat color-infrared aerial photographs, we show that 85% of the land surface has a positive 1985-2011 trend ( P < 0.05) in NDVI, making this one of the most intensely greening regions in the Arctic. Strong positive trends (>0.03 NDVI/decade) occurred consistently across all landscape positions and most vegetation types. Comparison of 208, 1:2,000 scale vertical air photo pairs from 1980 and 2013 clearly shows that this greening was driven by increased canopy cover of erect dwarf and tall shrubs, with declines in terricolous lichen cover. Disturbances caused by wildfires, exploratory gas wells, and drained lakes all produced strong, yet localized increases in NDVI due to shrub growth. Our analysis also shows that a 4°C winter temperature increase over the past 30 years, leading to warmer soils and enhanced nutrient mineralization provides the best explanation for observed vegetation change. These observations thus provide early corroboration for modeling studies predicting large-scale vegetation shifts in low-Arctic ecosystems from climate change. [ABSTRACT FROM AUTHOR]

Increasing frequency of extreme climate events is likely to impose increased stress on ecosystems and to jeopardize the services that ecosystems provide. Therefore, it is of major importance to assess the effects of extreme climate events on the temporal stability (i.e., the resistance, the resilience, and the variance) of ecosystem properties. Most time series of ecosystem properties are, however, affected by varying data characteristics, uncertainties, and noise, which complicate the comparison of ecosystem stability metrics ( ESMs) between locations. Therefore, there is a strong need for a more comprehensive understanding regarding the reliability of stability metrics and how they can be used to compare ecosystem stability globally. The objective of this study was to evaluate the performance of temporal ESMs based on time series of the Moderate Resolution Imaging Spectroradiometer derived Normalized Difference Vegetation Index of 15 global land-cover types. We provide a framework (i) to assess the reliability of ESMs in function of data characteristics, uncertainties and noise and (ii) to integrate reliability estimates in future global ecosystem stability studies against climate disturbances. The performance of our framework was tested through (i) a global ecosystem comparison and (ii) an comparison of ecosystem stability in response to the 2003 drought. The results show the influence of data quality on the accuracy of ecosystem stability. White noise, biased noise, and trends have a stronger effect on the accuracy of stability metrics than the length of the time series, temporal resolution, or amount of missing values. Moreover, we demonstrate the importance of integrating reliability estimates to interpret stability metrics within confidence limits. Based on these confidence limits, other studies dealing with specific ecosystem types or locations can be put into context, and a more reliable assessment of ecosystem stability against environmental disturbances can be obtained. [ABSTRACT FROM AUTHOR]

Glaciers in the Qilian mountains, located in the northeastern part of the Tibetan Plateau, constitute an important freshwater resource for downstream populations and natural systems. To enhance our understanding of the variability of the glaciers, temporally and spatially comprehensive information on them is needed. In this study, the glacier outlines of ~1990, ~2000 and ~2010 for the whole area were delineated in a semi-automated manner using band TM3/TM5 ratio images of Landsat ETM+ or TM scenes with the help of a merged ASTER GDEM/SRTM v4.1 digital elevation model. Combining our own results with those of previously published studies that span the period back to 1956, we found that the glacier area shrank by 30±8% from 1956 to 2010 and the shrinkage accelerated remarkably in the past two decades. The linear trends of annual air temperature and precipitation measured at weather stations within the glacierized areas were 0.03-0.058C a-1 (significant only after 2000) and 0.37-1.58mm a-1 (not significant) respectively from 1961 to 2010. Glaciers shrank mainly due to the increasing temperature. Glaciers in the Qilian mountains are very unlikely to have experienced positive mass balance over the past decade. Moreover, given the trend toward higher temperatures, the glaciers in this region will continue to shrink. [ABSTRACT FROM AUTHOR]

ABSTRACT The monitoring of volcanic ash in the atmosphere by satellite-borne instruments is highly important for generation of warnings of potential ash hazards to aviation, and to constraining model predictions of an ash cloud's anticipated evolution. The high economic cost of flight restrictions creates a demand for precise monitoring and forecasting; however, no scientific product can be considered precise unless presented with a robust estimate of its associated uncertainty. Data from infrared sensors are focused on, as these monitor the atmosphere both day and night. Most methods for the detection of ash, and the retrieval of its properties, rely on forward modelling to estimate the ash signal at the satellite. This requires assumptions to be made about the ash composition in order to constrain its optical properties as represented in a radiative transfer model. Ash composition may change through the course of an eruption, and is often unknown for new eruptions. Even in cases where the composition of the ash can be sampled, it is unlikely that it is homogeneous enough to match the composition of any of the available optical property datasets exactly (which properties are required for radiative transfer modelling). This often necessary assumption can affect the observed ash signal by an amount that varies with cloud altitude, thickness, and concentration from a few percent to 17.7% for the highest ash concentration examined in this study. This has implications for methods that rely on forward modelling of ash observations, and for the interpretation of real ash observations when ash composition is unknown. [ABSTRACT FROM AUTHOR]

The primary research problem presented in the article is verification of the thesis on the influence of relief and land cover type on the spatial variability of the land surface temperature (LST) distribution in the area including the river catchment area of upper Parsęta. The paper presents the use of thermal channels from two Landsat ETM+ scenes pictures, Corine Land Cover database from 2000 as well as the DTED-2 digital elevation model. Two ETM+ thermal bands processing algorithms were used for calculation of the land surface temperature: Qin et al. (2001) and Jiménez-Muňoz et al. (2003). Conducted statistical tests show significant differences of the land surface temperature values between particular land cover forms as well as types of relief. LST maps can be applied in topoclimatology eg. to detail and verify the in situ measurements. [ABSTRACT FROM AUTHOR]

Characterizing the specific inherent optical properties (SIOPs) of water constituents is fundamental to remote sensing applications. Therefore, this paper presents the absorption properties of phytoplankton, gelbstoff and tripton for three small, optically-diverse South African inland waters. The three reservoirs, Hartbeespoort, Loskop and Theewaterskloof, are challenging for remote sensing, due to differences in phytoplankton assemblage and the considerable range of constituent concentrations. Relationships between the absorption properties and biogeophysical parameters, chlorophyll-α (chl-α), TChl (chl-α plus phaeopigments), seston, minerals and tripton, are established. The value determined for the mass-specific tripton absorption coefficient at 442 nm, a* tr(442), ranges from 0.024 to 0.263 m²·g-1. The value of the TChl-specific phytoplankton absorption coefficient (a*Φ) was strongly influenced by phytoplankton species, size, accessory pigmentation and biomass. a*Φ(440) ranged from 0.056 to 0.018 m²·mg-1 in oligotrophic to hypertrophic waters. The positive relationship between cell size and trophic state observed in open ocean waters was violated by significant small cyanobacterial populations. The phycocyanin-specific phytoplankton absorption at 620 nm, a* pc(620), was determined as 0.007 m²·g-1 in a M. aeruginosa bloom. Chl-α was a better indicator of phytoplankton biomass than phycocyanin (PC) in surface scums, due to reduced accessory pigment production. Absorption budgets demonstrate that monospecific blooms of M. aeruginosa and C. hirundinella may be treated as "cultures", removing some complexities for remote sensing applications. These results contribute toward a better understanding of IOPs and remote sensing applications in hypertrophic inland waters. However, the majority of the water is optically complex, requiring the usage of all the SIOPs derived here for remote sensing applications. The SIOPs may be used for developing remote sensing algorithms for the detection of biogeophysical parameters, including chl-α, suspended matter, tripton and gelbstoff, and in advanced remote sensing studies for phytoplankton type detection. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach to landslide research based on remote sensing and sensor networks. This approach is composed of three important parts: (i) landslide susceptibility mapping using remote-sensing techniques for susceptible determination of landslide spots; (ii) scaled-down landslide simulation experiments for validation of sensor network for landslide monitoring, and (iii) in situ sensor network deployment for intensified landslide monitoring. The study site is the Taziping landslide located in Hongkou Town (Sichuan, China). The landslide features generated by landslides triggered by the 2008 Wenchuan Earthquake were first extracted by means of object-oriented methods from the remote-sensing images before and after the landslides events. On the basis of correlations derived between spatial distribution of landslides and control factors, the landslide susceptibility mapping was carried out using the Artificial Neural Network (ANN) technique. Then the Taziping landslide, located in the above mentioned study area, was taken as an example to design and implement a scaled-down landslide simulation platform in Tongji University (Shanghai, China). The landslide monitoring sensors were carefully investigated and deployed for rainfall induced landslide simulation experiments. Finally, outcomes from the simulation experiments were adopted and employed to design the future in situ sensor network in Taziping landslide site where the sensor deployment is being implemented. [ABSTRACT FROM AUTHOR]

Atmospheric correction is a fundamental process of ocean color remote sensing to remove the atmospheric effect from the top-of-atmosphere. Generally, Near Infrared (NIR) based algorithms perform well for clear waters, while Ultraviolet (UV) based algorithms can obtain good results for turbid waters. However, the latter tends to produce noisy patterns for clear waters. An ideal and practical solution to deal with such a dilemma is to apply NIR- and UV-based algorithms for clear and turbid waters, respectively. We propose a novel atmospheric correction method that integrates the advantages of UV- and NIR-based atmospheric correction (AC) algorithms for coastal ocean color remote sensing. The new approach is called UV-NIR combined AC algorithm. The performance of the new algorithm is evaluated based on match-ups between GOCI images and the AERONET-OC dataset. The results show that the values of retrieved Rrs (Remote Sensing Reflectance) at visible bands agreed well with the in-situ observations. Compared with the SeaDAS (SeaWiFS Data Analysis System) standard NIR algorithm, the new AC algorithm can achieve better precision and provide more available data. [ABSTRACT FROM AUTHOR]

Optical and thermal remote sensing data were acquired at ground level over several turfgrass species under different soil and irrigation treatments in northern Colorado, USA. Three vegetation indices (VIs), estimated based on surface spectral reflectance, were sensitive to the effect of reduced water application on turfgrass quality. The temperature-based Grass Water Stress Index (GWSI) was also estimated by developing non-transpiring and non-water-stressed baselines. The VIs and the GWSI were all consistent in (i) having a non-linear relationship with the water application depth; and, (ii) revealing that the sensitivity of studied species to water availability increased in order from warm season mix to Poa pratensis L. and then Festuca spp.. Implemented soil preparation treatments had no significant effect on turfgrass quality and water stress. The differences between GWSI-based estimates of water use and the results of a complex surface energy balance model (METRIC) were not statistically significant, suggesting that the empirical GWSI method could provide similar results if the baselines are accurately developed under the local conditions of the study area [ABSTRACT FROM AUTHOR]

Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved quantitatively through models or statistical methods. However, producing global products requires a complex computing system and massive volumes of multi-sensor and multi-temporal remotely sensed data. This manuscript describes the ground Global LAnd Surface Satellite (GLASS) product generation system that can be used to generate long-sequence time series of global land surface data products based on various remotely sensed data. To ensure stabilization and efficiency in running the system, we used the methods of task management, parallelization, and multi I/O channels. An array of GLASS remote sensing products related to global land surface parameters are currently being produced and distributed by the Center for Global Change Data Processing and Analysis at Beijing Normal University in Beijing, China. These products include Leaf Area Index (LAI), land surface albedo, and broadband emissivity (BBE) from the years 1981 to 2010, downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) from the years 2008 to 2010 [ABSTRACT FROM AUTHOR]

The objective of this study was to analyze land use/land cover (LULC) changes in the landscape of Munessa-Shashemene area of the Ethiopian highlands over a period of 39 years (1973-2012). Satellite images of Landsat MSS (1973), TM (1986), ETM+ (2000), and RapidEye (2012) were used. All images were classified using object-based image classification technique. Accuracy assessments were conducted for each reference year. Change analysis was carried out using post classification comparison in GIS. Nine LULCs were successfully captured with overall accuracies ranging from 85.7% to 93.2% and Kappa statistic of 0.822 to 0.924. The classification result revealed that grasslands (42.3%), natural forests (21%), and woodlands (11.4%) were dominant LULC types in 1973. In 2012, croplands (48.5%) were the major LULC types followed by others. The change result shows that a rapid reduction in woodland cover of 81.8%, 52.3%, and 36.1% occurred between the first (1973-1986), second (1986-2000), and third (2000-2012) study periods, respectively. Similarly, natural forests cover decreased by 26.1% during the first, 21.1% during the second, and 24.4% during the third periods. Grasslands also declined by 11.9, 17.5, and 21.1% during the three periods, respectively. On the contrary, croplands increased in all three periods by 131, 31.5, and 22.7%, respectively. Analysis of the 39-year change matrix revealed that about 60% of the land showed changes in LULC. Changes were also common along the slope gradient and agro-ecological zones with varying proportions. Further study is suggested to investigate detailed drivers and consequences of changes [ABSTRACT FROM AUTHOR]

This study aims to assess the localization and size distribution of landslides using automatic remote sensing techniques in (semi-) arid, non-vegetated, mountainous environments. The study area is located in the Kurdistan region (NE Iraq), within the Zagros orogenic belt, which is characterized by the High Folded Zone (HFZ), the Imbricated Zone and the Zagros Suture Zone (ZSZ). The available reference inventory includes 3,190 landslides mapped from sixty QuickBird scenes using manual delineation. The landslide types involve rock falls, translational slides and slumps, which occurred in different lithological units. Two hundred and ninety of these landslides lie within the ZSZ, representing a cumulated surface of 32 km2. The HFZ implicates 2,900 landslides with an overall coverage of about 26 km2. We first analyzed cumulative landslide number-size distributions using the inventory map. We then proposed a very simple and robust algorithm for automatic landslide extraction using specific band ratios selected upon the spectral signatures of bare surfaces as well as posteriori slope and the normalized difference vegetation index (NDVI) thresholds. The index is based on the contrast between landslides and their background, whereas the landslides have high reflections in the green and red bands. We applied the slope threshold map to remove low slope areas, which have high reflectance in red and green bands. The algorithm was able to detect ∼96% of the recent landslides known from the reference inventory on a test site. The cumulative landslide number-size distribution of automatically extracted landslide is very similar to the one based on visual mapping. The automatic extraction is therefore adapted for the quantitative analysis of landslides and thus can contribute to the assessment of hazards in similar regions [ABSTRACT FROM AUTHOR]

The main finding by Po-Chedley and Fu was that the University of Alabama in Huntsville (UAH) microwave sounding unit (MSU) product has a bias in its NOAA-9 midtropospheric channel (TMT) warm target factor, which leads to a cold bias in the TMT trend. This reply demonstrates that the central arguments by Christy and Spencer to challenge Po-Chedley and Fu do not stand. This reply establishes that 1) Christy and Spencer found a similar, but insignificant, bias in the UAH target factor because their radiosonde data lack adequate sampling and measurement errors were considered twice; 2) the UAH individual satellite TMT difference between NOAA-9 and NOAA-6 reveals a bias of 0.082 ± 0.011 in the UAH NOAA-9 target factor; 3) comparing the periods before and after NOAA-9 is not an adequate method to draw conclusions about NOAA-9 because of the influence of other satellites; 4) using the Christy and Spencer trend sensitivity value, UAH TMT has a cold bias of 0.035 K decade−1 given a target factor bias of 0.082; 5) similar trends from UAH and Remote Sensing Systems (RSS) for the lower tropospheric temperature product (TLT) do not indicate that the UAH TMT and TLT NOAA-9 target factor is unbiased; and 6) the NOAA-9 warm target temperature signal in UAH TMT indicates a problem with the UAH empirical algorithm to derive the target factor. [ABSTRACT FROM AUTHOR]

We analyzed the 10-year record (1999-2009) of SeaWinds Ku-band microwave backscatter from humid tropical forest regions in South America, Africa, and Indonesia/Malaysia. While backscatter was relatively stable across much of the region, it declined by 1-2 dB in areas of known large-scale deforestation, and increased by up to 1-2 dB in areas of secondary forest or plantation forest growth and in major metropolitan areas. The reduction in backscatter over 142 18.5 km × 18.5 km blocks of tropical forest was correlated with gross forest cover loss (as determined from Landsat data analysis) (R = -0.78); this correlation improved when restricted to humid tropical forest blocks in South America with high initial forest cover (R = -0.93, n = 22). This study shows that scatterometer-based analyses can provide an important geophysical data record leading to robust identification of the spatial patterns and timing of large-scale change in tropical forests. The coarse spatial resolution of SeaWinds ( ~ 10 km) makes it unsuitable for mapping deforestation at the scale of land-use activity. However, due to a combination of instrument stability, sensitivity to canopy change and insensitivity to atmospheric effects, and straight-forward data processing, Ku-band scatterometery can provide a fully independent assessment of large-scale tropical forest canopy dynamics which may complement the interpretation of higher resolution optical remote sensing. [ABSTRACT FROM AUTHOR]

Land surface temperature and emissivity spectra are essential variables for improving models of the earth surface-atmosphere interaction or retrievals of atmospheric variables such as thermodynamic profiles, chemical composition, cloud and aerosol characteristics, and so on. In most cases, emissivity spectral variations are not correctly taken into account in climate models, leading to potentially significant errors in the estimation of surface energy fluxes and temperature. Satellite infrared observations offer the dual opportunity of accurately estimating these properties of land surfaces as well as allowing a global coverage in space and time. Here, high-spectral-resolution observations from the Infrared Atmospheric Sounder Interferometer (IASI) over the tropics (30°N-30°S), covering the period July 2007-March 2011, are interpreted in terms of 1° × 1° monthly mean surface skin temperature and emissivity spectra from 3.7 to 14 μm at a resolution of 0.05 μm. The standard deviation estimated for the surface temperature is about 1.3 K. For the surface emissivity, it varies from about 1%-1.5% for the 10.5-14 - and 5.5-8- μm windows to about 4% around 4 μm. Results from comparisons with products such as Moderate Resolution Imaging Spectroradiometer (MODIS) low-resolution emissivity and surface temperature or ECMWF forecast data (temperature only) are presented and discussed. Comparisons with emissivity derived from the Airborne Research Interferometer Evaluation System (ARIES) radiances collected during an aircraft campaign over Oman and made at the scale of the IASI field of view offer valuable data for the validation of the IASI retrievals. [ABSTRACT FROM AUTHOR]

The possibility of using remote sensing retrievals to estimate apparent water vapor sinks and heat sources is explored. The apparent water vapor sinks and heat sources are estimated from a combination of remote sensing, specific humidity, and temperature from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and wind fields from the National Aeronautics and Space Administration (NASA)'s Goddard Space Flight Center (GSFC)'s Modern Era Retrospective-Analysis for Research and Applications (MERRA). The intraseasonal oscillation (ISO) of the Indian summer monsoon is used as a test bed to evaluate the apparent water vapor sink and heat source. The ISO-related northward movement of the column-integrated apparent water vapor sink matches that of precipitation observed by the Tropical Rainfall Measuring Mission (TRMM) minus the MERRA surface evaporation, although the amplitude of the variation is underestimated by 50%%. The diagnosed water vapor and heat budgets associated with convective events during various phases of the ISO agree with the moisture-convection feedback mechanism. The apparent heat source moves northward coherently with the apparent water vapor sink associated with the deep convective activity, which is consistent with the northward migration of the precipitation anomaly. The horizontal advection of water vapor and dynamical warming are strong north of the convective area, causing the northward movement of the convection by the destabilization of the atmosphere. The spatial distribution of the apparent heat source anomalies associated with different phases of the ISO is consistent with that of the diabatic heating anomalies from the trained heating (TRAIN Q1) dataset. Further diagnostics of the TRAIN Q1 heating anomalies indicate that the ISO in the apparent heat source is dominated by a variation in latent heating associated with the precipitation. [ABSTRACT FROM AUTHOR]

Abstract: The southwestern area of Spain, by its geographical and climatological conditions, is a key location for the characterization of atmospheric aerosol properties. The present study is aimed at evaluating the reliability of satellite-based aerosol climatologies, as inferred from level 2 standard aerosol products such as the Terra-MODIS (Moderate Resolution Imaging Radiometer) MOD04 aerosol product, with an application over this region during the period 2000–2008. This evaluation is carried out by means of comparison with ground-based data from the AERONET station of El Arenosillo (Spain, 37.1N, 6.7W), which has been providing continuous data since 2000. The focus of this paper is the climatology of two aerosol optical parameters: the aerosol optical depth (AOD) and the Ångström exponent. AERONET ground-based measurements give an annual mean value of 0.16±0.12 and a median of 0.12 for the AOD, and a mean value of 1.20±0.47 for the Ångström exponent. The seasonal pattern is characterized by two maxima, the most important maximum occurs in summer months, and the other one in late-winter/early-spring. Lowest values appear in fall and winter, however, a local minimum is observed in July which is only detected with the long-term data series. The mean climatological AOD based on AERONET exhibits complex seasonal patterns (i.e. with multiple local extrema), which are not always captured by MODIS-based climatology. MODIS only reproduces low values of the AOD in winter and high values in summer, as well as the local minimum of July which is sharper when using over-land retrievals. The time series of the AOD retrieved from MODIS both over land and ocean are in relatively good agreement with the ground-based measurements, with a monthly overestimation of about 30% on average, and higher differences in spring. Seasonal patterns from MODIS are better reproduced over land than over ocean. The agreement between daily AERONET and MODIS, as assessed by linear regression, gives correlation coefficients above 80% and an intercept bias below 0.03. [Copyright &y& Elsevier]

Abstract: Aircraft emissions contribute to the increased atmospheric burden of particulate matter (PM) that plays an important role in air quality, human health, visibility, contrail formation and climate change. Sampling and measurement of modern aircraft emissions at the engine exhaust plane (EEP) for engine and fuel certification remains challenging, as no agency-certified method is available. In this paper we summarize the results of three recent field studies devoted to investigate the consistency and applicability of “extractive” and “optical remote-sensing” (ORS) technologies in the sampling and measurement of gaseous and PM emitted by a number of military aircraft engines. Three classes of military engines were investigated; these include T56, TF33, and T700 & T701C types of engines, which consume 70–80% of the military aviation fuel each year. JP-8 and Fischer–Tropsch (FT)-derived paraffinic fuels were used to study the effect of fuels. It was found that non-volatile particles in the engine emissions were in the 20nm range for the low power condition of new helicopter engines to 80nm for the high power condition of legacy engines. Elemental analysis indicated little metals were present on particles, while most of the materials on the exhaust particles were carbon and sulfate based. Alkanes, carbon monoxide, carbon dioxide, nitrogen oxides, sulfur dioxide, formaldehyde, ethylene, acetylene and propylene were detected. The last five species were most noticeable only under low engine power. The emission indices calculated based on the ORS data deviate significantly from those based on the extractive data. Nevertheless, the ORS techniques were useful in the sense that it provided non-intrusive real-time detection of species in the exhaust plume, which warrants further development. The results obtained in this program help validate sampling methodology and measurement techniques used for non-volatile PM aircraft emissions as described in the . [Copyright &y& Elsevier]

The article presents a study which calculates the multiply scattered lidar signals of cloud water droplets using the Monte Carlo method. It states that the researchers modified the gamma size distribution of liquid water content (LWC) to simulate multiple scattering in water clouds such as fog and clouds. It concludes that the extinction coefficient and the degree of polarization for water cloud conditions have a one-to-one correspondence.

Abstract: Surface energy balance and 24-h evapotranspiration were mapped on an agricultural landscape with SRF willows using a remote sensing-based model to provide information on the water use by different land cover types. The results demonstrate the influence of land cover and vegetation type on surface energy balance and water losses. The evapotranspiration from forests was much higher than for other land cover types, corresponding to the significant cooling of the land surface. The SRF willow plantings were on average 3.6°C cooler than the surrounding grassy areas; the 24-hour evapotranspiration from willows was 1.6 times higher than the evapotranspiration from surrounding grass, but lower than evapotranspiration from the forests. The possibility of changes in evapotranspiration patterns, local climate, and regional water balance with increasing acreage in SRF crop are discussed. [Copyright &y& Elsevier]

Surface upwelling longwave radiation (LWUP) is an important component in the surface radiation budget. Existing satellite-derived LWUP data sets are too coarse to support high-resolution numerical models, and their accuracy needs to be improved. In this paper, we evaluate three methods for estimating clear-sky land LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1-km spatial resolution. The three methods are as follows: 1) the temperature-emissivity method; 2) the linear model method; and 3) the artificial neural network (ANN) model method. Methods 2 and 3 are new methods based on extensive radiative transfer simulations and statistical analysis. We explicitly considered surface emissivity effects by incorporating the University of California Santa Barbara emissivity library in the radiative transfer simulation. The three methods were evaluated using ground-measured LWUP from six SURFRAD sites. Although methods 2 and 3 were developed using MODIS Terra atmospheric profiles, they were applied to both Terra and Aqua data because the designs of the two sensors are similar. The root mean squared errors (rmses) of the ANN model method are smaller than that of the other two methods at all sites. The averaged rmses of the ANN model method are 15.89 W/m² (Terra) and 14.57 W/m² (Aqua); the averaged biases are -8.67 W/m² (Terra) and -7.21 W/m² (Aqua). The biases and rmses for Aqua are ∼1.3 W/m² smaller than that of Terra. The biases and rmses of the ANN model method are ∼5 W/m² smaller than that of the temperature-emissivity method and ∼2.5 W/m² smaller than that of the linear model method. [ABSTRACT FROM AUTHOR]

Remotely sensed images often suffer from the common problems of stripe noise and random dead pixels. The techniques to recover a good image from the contaminated one are called image destriping (for stripes) and image inpainting (for dead pixels). This paper presents a maximum a posteriori (MAP)-based algorithm for both destriping and inpainting problems. The main advantage of this algorithm is that it can constrain the solution space according to a priori knowledge during the destriping and inpainting processes. In the MAP framework, the likelihood probability density function (PDF) is constructed based on a linear image observation model, and a robust Huber-Markov model is used as the prior PDF. The gradient descent optimization method is employed to produce the desired image. The proposed algorithm has been tested using moderate resolution imaging spectrometer images for destriping and China—Brazil Earth Resource Satellite and QuickBird images for simulated inpainting. The experiment results and quantitative analyses verify the efficacy of this algorithm. [ABSTRACT FROM AUTHOR]

The North Water Polynya (NOW) is an area of ocean between Greenland and Ellesmere Island that does not freeze completely during the winter months. The mechanism that maintains the polynya during the Arctic winter and early spring is not precisely known, but the presence of open water is a critical factor in allowing oceanic heat to escape into the atmosphere. The northerly location of the NOW permitted the collection of an Advanced Very High Resolution Radiometer (AVHRR) image every 101 minutes for seven consecutive orbits. The unique, short time span imagery allowed thermal features of the NOW to be mapped over a tidal cycle. The combination of AVHRR imagery, Acoustic Doppler Current Profiler data, and the Composite Arctic Sea Surface Temperature Algorithm show the dynamic nature of the NOW over a tidal cycle. Both the amount and configuration of open water can change dramatically over a 12-hour period in response to tidal fluctuations. The evidence suggests that the amount of open water in the NOW during March and April is related to the velocity of the current, which in turn is influenced by the tidal cycle. The open water caused by the tide-induced movement of ice then allows oceanic heat to escape into the environment. During March and April, the considerable temperature difference between the ocean and the atmosphere at their interface results in a high incidence of ice fog near leads and open water in the NOW. The amount of ice fog observed on the satellite imagery fluctuates with the tidal cycle, suggesting that open water within the NOW is influenced by the tide in the short term. [ABSTRACT FROM AUTHOR]

A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components (PCs) from the hyperspectral data and building several morphological profiles (MPs). These profiles can be used all together in one extended MP. A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixelwise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality, the final classification is achieved by using a support vector machine classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of MPs based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set. [ABSTRACT FROM AUTHOR]

A new type of remote-sensing radiometer based upon the Fabry-Perot (FP) interferometric technique has been developed at NASA's Goddard Space Flight Center and tested from both ground and aircraft platforms. The sensor uses direct or reflected sunlight and has channels for measuring the column concentration of carbon dioxide at 1570 nm, oxygen lines sensitive to pressure and temperature at 762 and 768 nm, and water vapor (940 nm). A solid FP etalon is used as a tunable narrow bandpass filter to restrict the measurement to the gas of interest's absorption bands. By adjusting the temperature of the etalon, which changes the index of refraction of its material, the transmission fringes can be brought into nearly exact correspondence with the absorption lines of the particular species. With this alignment between absorption lines and fringes, changes in the amount of a species in the atmosphere strongly affect the amount of light transmitted by the etalon and can be related to gas concentration. The technique is applicable to different chemical species. We have performed simulations and instrument design studies for CH4, 13CO2 isotope, and CO detection. [ABSTRACT FROM AUTHOR]

In this paper, a new and improved L-band version of the correlated noise calibration standard (CNCS) has been developed to aid in the characterization of polarimetric microwave radiometers. The CNCS generates a series of known polarimetric test signals using a two-channel commercial arbitrary waveform generator (AWG) and a pair of frequency-upconversion modules. When it is inserted in place of the antenna used by a radiometer-under-test (RUT), it can fully characterize the polarimetric response of the receiver portion of the RUT. Both hardware and software improvements have been made over a previous version of the CNCS. The frequency upconverters now include integral warm and cold calibration-reference targets to automatically compensate for small drifts in AWG output-signal strength. The procedure used to calibrate the RUT has been generalized to correct for nonideal characteristics of the CNCS itself. Moreover, the effects on the derived polarimetric gain matrix of impedance mismatches between the RUT, and either the CNCS or the antenna have been determined. Details of the CNCS improvements are presented. The results of an experimental demonstration of its use and of a series of validation tests of its performance are also presented. [ABSTRACT FROM AUTHOR]

A refined ray system based on the geometrical theory of diffraction (GTD) for an object embedded in soil for a monostatic transmitter-receiver alignment is presented. Apart from the investigation of the "classical" reflections from the target, creeping waves are also taken into account, and their formalism is presented. The objective of such a ray set is to better understand the different scattering mechanisms, which are presented in the complex scattering framework. In electromagnetic modeling based on GTD, the complex shape of a target is replaced by simpler canonical objects, e.g., facets, cones, wedges, spheres, or cylinders. Here, a cylinder is located in parallel and closely to the plane interface of two dielectric half-spaces. The example of air-soil is taken into account. The numerical results obtained for various directions of incidence are employed to describe the polarimetric characteristics of the diffracted field from grazing to perpendicular incidence to the surface. By representing the diffracted GTD field on the Poincaré sphere, the location on the sphere has a one-to-one relationship to the dielectric properties of the soil. The relation can be employed to extract information as the soil moisture. [ABSTRACT FROM AUTHOR]

Remote sensing of ocean color from space, a problem that consists of retrieving spectral marine reflectance from spectral top-of-atmosphere reflectance, is considered as a collection of similar inverse problems continuously indexed by the angular variables influencing the observation process. A general solution is proposed in the form of a field of non-linear regression models over the set T of permitted values for the angular variables, i.e., as a map from T to some function space. Each value of the field is a regression model that performs a direct mapping from the top-of-atmosphere reflectance to the marine reflectance. Since the spectral components of the field take values in the same variable vector space, the retrievals in individual spectral bands are not independent, i.e., the solution is not just a juxtaposition of independent models for each spectral band. A scheme based on ridge functions is developed to approximate this solution to an arbitrary accuracy, and is applied to the retrieval of marine reflectance in Case 1 waters, for which optical properties are only governed by biogenic content. The statistical models are evaluated on synthetic data as well as actual data originating from the SeaWiFS instrument, taking into account noise in the data. Theoretical performance is good in terms of accuracy, robustness, and generalization capabilities, suggesting that the function field methodology might improve atmospheric correction in the presence of absorbing aerosols and provide more accurate estimates of marine reflectance in productive waters. When applied to SeaWiFS imagery acquired off California, the function field methodology gives generally higher estimates of marine reflectance than the standard SeaDAS algorithm, but the values are more realistic. [Copyright &y& Elsevier]