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Reliable detection of clouds is a critical pre-processing step in optical satellite based remote sensing. Currently, most methods are based on classifying invidual pixels from their spectral signatures, therefore they do not incorporate the spatial patterns. This often leads to misclassifications of highly reflective surfaces, such as human made structures or snow/ice. Multi-temporal methods can be used to alleviate this problem, but these methods introduce new problems, such as the need of a cloud-free image of the scene. In this paper, we introduce the Remote Sensing Network (RS-Net), a deep learning model for detection of clouds in optical satellite imagery, based on the U-net architecture. The model is trained and evaluated using the Landsat 8 Biome and SPARCS datasets, and it shows state-of-the-art performance, especially over biomes with hardly distinguishable scenery, such as clouds over snowy and icy regions. In particular, the performance of the model that uses only the RGB bands is significantly improved, showing promising results for cloud detection with smaller satellites with limited multi-spectral capabilities. Furthermore, we show how training the RS-Net models on data from an existing cloud masking method, which are treated as noisy data, leads to increased performance compared to the original method. This is validated by using the Fmask algorithm to annotate the Landsat 8 datasets, and then use these annotations as training data for regularized RS-Net models, which then show improved performance compared to the Fmask algorithm. Finally, the classification time of a full Landsat 8 product is 18.0 ± 2.4 s for the largest RS-Net model, thereby making it suitable for production environments. • This algorithm show high performance, even when limited to RGB bands. • The processing time is low due to GPU based calculations. • Cloud detection performance over snow/ice is significantly improved. • This algorithm can be trained on the output of earlier cloud detection algorithms. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45 % of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20 %) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system. Highlights • Presented a modular process chain for plant trait mapping including local effects • High-resolution global maps of leaf traits by fusing measured trait data, LANDSAT and MODIS • Scope for testing and parameterizing trait-enabled Earth System models • Implications for land management and Earth system science applications [ABSTRACT FROM AUTHOR]

The proximity of seagrass meadows to centers of human activity makes them vulnerable to a variety of habitat degrading insults. Physical scarring has long been recognized as an important but difficult-to-quantify source of habitat fragmentation and seagrass loss. We present a pixel-based algorithm to detect seafloor propeller seagrass scars in shallow water that promises to automate the detection and measurement of scars across the submarine landscape. 1 1 A preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Amsterdam, Netherlands, 2014 ( Oguslu et al., 2014 ). We applied the algorithm to multispectral and panchromatic images captured at the Deckle Beach, Florida using the WorldView-2 commercial satellite. The algorithm involves four steps using spectral and spatial information from radiometrically calibrated multispectral and panchromatic images. First, we fused multispectral and panchromatic images using a principal component analysis (PCA)-based pan-sharpening method to obtain multispectral pan-sharpened bands. In the second step, we enhanced the image contrast of the pan-sharpened bands for better scar detection. In the third step, we classified the contrast enhanced image pixels into scar and non-scar categories based on a sparse coding algorithm that produced an initial scar map in which false positive scar pixels were also present. In the fourth step, we applied post-processing techniques including a morphological filter and local orientation to reduce false positives. Our results show that the proposed method may be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters. [ABSTRACT FROM AUTHOR]

In vegetation-abundant ecosystems, accurate fractional cover estimation of multiple targets, including non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV) and bare soil (BS) is an important indicator of carbon storage and climate change, and a contributing factor to soil and water conservation, wildfire, and drought. Satellite remote sensing began with sensors limited to the visible and near infrared (VNIR, 380 nm to 1000 nm), and advancing to sensors spanning the whole solar domain (350 nm to 2500 nm) over the last five decades. However, NPV is still difficult to distinguish from BS in satellite images covering large spatial areas, since their spectral reflectances are always mixed and have subtly different spectral features. Many spectral indices have been proposed to identify NPV, however, most include a short-wave infrared band which is lacking on many current and historical spaceborne sensors. To fully utilize current and historical airborne/satellite images with only VNIR bands, a novel spectral index – the VNIR spectral shape index (SSI) – is proposed in this paper. Using spectra of PV, NPV, and BS acquired from world-recognized spectral libraries, the SSI was devised using only blue, green, red, and NIR bands. When applied to Sentinel-2 MSI images, the SSI was effective in estimating/mapping the fractional cover of PV, NPV, and BS using the triangular space method. The resulting fractional cover was evaluated in three 3 km × 2 km study areas including one grassland, one woodland, and one cropland. The spectral reflectance collected by spectroradiometer in field work and digital image collect by digital camera mounted on unmanned aerial vehicle (UAV) support that the triangular space method is able to estimate the fractional cover of photosynthetic and non-photosynthetic vegetation simultaneously, and that endmember selection relying solely on satellite images is reasonable. Comparing the abundance of each target in UAV images to corresponding MSI pixels, the SSI index and triangular space method proved effective for fractional cover estimation in varied vegetated ecosystems. For fractional cover evaluation in grassland, woodland, and cropland, the R2 reaching to 0.59, 0.62, and 0.70, respectively. These results demonstrate that the SSI is feasible for multiple sensors, enabling potential large temporal and/or spatial scale applications. • Spectral index, SSI, is devised to separate soil and non-photosynthetic vegetation. • SSI employs blue, green, red, and NIR bands to catch spectral shape difference. • Four bands in SSI are equipped on most satellite/airborne sensors since 1980s. • SSI-NDVI combination can estimate fraction of land cover using triangle method. [ABSTRACT FROM AUTHOR]

Mangrove deforestation has rapidly declined by an order of magnitude compared to that reported for the 20th century, but the remaining mangrove ecosystems are still undergoing biodiversity loss and degradation. Labor-intensive ground surveys that are usually used for terrestrial plant biodiversity assessment are difficult to conduct in the mangrove forests. Assessing plant biodiversity from space does offer a novel perspective, but existing solutions are mainly focused on estimating α- or β-diversities (i.e., diversity within communities or diversity among communities) alone. This paper applied a novel holistic biodiversity approach that could partition γ-diversity into α- and β-diversities for plant diversity mapping with operational satellites (WorldView-2, Sentinel-2, and Zhuhai-1) and field plots in the Qinglangang Provincial Nature Reserve, Hainan, China. We compared the resulting outputs of the α-diversity from the holistic method (SD α) to those by the coefficient of variation (CV) and the Rao's Q index, and the contributions of individual spectral features to α- and β-diversity were also measured. Results indicated that α- and β-diversities accounted for ~30% and ~ 70% of the total diversity in the Reserve, respectively. α-diversity derived from the WorldView-2 images showed statistically higher correlations with the observed Shannon's index (R2: 0.20–0.42) compared to that from Sentinel-2 and Zhuhai-1 (R2: 0.03–0.15). β-diversity derived from WorldView-2 images had the highest accuracy (90.00%), followed by that from Sentinel-2 and Zhuhai-1 (83.33% and 73.33%, respectively). Red-edge and near-infrared spectral features were the most informative features for diversity mapping while shortwave infrared (SWIR) features were also valuable for β-diversity mapping. Concurrent mapping of α- and β-diversities of mangrove forests represents the first step toward achieving rapid biodiversity monitoring schemes of mangrove forests over a national or global scale. • This paper used satellite imagery to map the biodiversity of a mangrove forest. • The γ-diversity was partitioned into α- and β-diversities by a novel method. • The contributions of individual spectral features were quantitatively evaluated. • We analyzed the possibilities and challenges for mapping mangrove biodiversity. [ABSTRACT FROM AUTHOR]

We present comparisons of cloud droplet size distributions (DSDs) retrieved from the research scanning polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). The airborne portion of this field experiment was based out of St. John's airport, Newfoundland, Canada with the focus of this paper being on the deployment in May–June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring the polarized and total reflectance in 9 spectral channels. Its uniquely high angular resolution allows for characterization of liquid water droplet sizes using the rainbow structure observed in the polarized reflectance over the scattering angle range from 135° to 165°. The rainbow is dominated by single scattering of light by cloud droplets, so its structure is characteristic specifically of the droplet sizes at cloud top (within unit optical depth into the cloud, equivalent to approximately 50 m). A parametric fitting algorithm applied to the polarized reflectance provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, the Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution itself. The latter is important in the case of clouds with complex microphysical structure, or multiple layers of cloud, which result in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparisons between remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was near the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons generally show good agreement (better than 1 μm for effective radius and in most cases better than 0.02 for effective variance) with deviations explainable by the position of the aircraft within the cloud, or by the presence of additional cloud layers between the cloud being sampled by the in situ instrumentation and the altitude of the remote sensing segment. In the latter case, the multi-modal DSDs retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The results of these comparisons provide a rare validation of polarimetric droplet size retrieval techniques, demonstrating their accuracy and robustness and the potential of satellite data of this kind on a global scale. [ABSTRACT FROM AUTHOR]

Abstract: Since 2009, subsets of declassified satellite images of Arctic sea ice have been released as literal image derived products (LIDPs) with one-meter resolution for scientific use. The collections include imagery acquired over six fixed locations in the Arctic Basin since 1999. Targeted acquisitions over drifting ice parcels, from 2009 on, provide an added dimension to the LIDP archive. This mode of acquisition is especially useful for capturing time-varying processes during the melt season. In this paper, we describe the characteristics of LIDPs, the extent of the current archive, and highlight their uses for Arctic sea ice science. At this writing, the archive of LIDPs is still quite limited, and thus this paper illustrates the potential utility of the imagery. We provide an overview of the retrieval of five sea ice parameters that would benefit significantly from the surface details afforded by the higher resolution LIDPs. They include: melt pond coverage, open water fraction, ridge height, floe size, and, openings and closings. Two other uses are suggested: measurement of lateral melt and the interpretation of radar backscatter. The intent is to motivate geophysical uses of the LIDP and future acquisitions. Results here suggest that the effective observations of sea ice parameters and especially their changes require more focused sampling strategies to address specific spatial and temporal sampling needs. Presently, acquisitions of this type of imagery at fixed locations and drifting sites are being continued, and LIDPs are added to the archive as they are released. [Copyright &y& Elsevier]

Soil moisture is one of the essential climate variables for the Global Climate Observing System (GCOS) that has been prioritized by the ESA's Climate Change Initiative to construct its homogeneous long-term climate record. This requires a consistent characterization of the error structures in the individual data sets, which vary due to changes in instrument configuration and calibration, and retrieval algorithm design. In this paper, the random error and systematic differences in nine passive and active microwave satellite soil moisture products over Australia (time coverage: 1978–present) are estimated in a same manner for SM components at subseasonal and seasonal-to-interannual timescales separately. The multi-scale error structures are found to be non-trivial and vary between the products, giving cause for conducting multi-scale merging with awareness of these differences. Noticeable similarities between the error structures of the satellite products derived from same retrieval algorithm and same measuring frequency however suggest transferability of error parameters between them. Using partial rank correlation analysis, the error maps are linked to statistics on vegetation index, digital elevation, soil moisture and soil temperature, and land cover fractions and mixing in order to explain the observed variability and the similarities between the products. [ABSTRACT FROM AUTHOR]

Soil salinization is widely recognized to be a major threat to worldwide agriculture. Despite decades of research in soil mapping, no reliable and up-to-date salinity maps are available for large geographical regions, especially for the salinity ranges that are most relevant to agricultural productivity (i.e., salinities less than 20 dS m − 1 , when measured as the electrical conductivity of the soil saturation extract). This paper explores the potentials and limitations of assessing and mapping soil salinity via linear modeling of remote sensing vegetation indices. A case study is presented for western San Joaquin Valley, California, USA using multi-year Landsat 7 ETM + canopy reflectance and the Canopy Response Salinity Index (CRSI). Highly detailed salinity maps for 22 fields comprising 542 ha were used for ground-truthing. Re-gridded to 30 × 30 m, the ground-truth data totaled over 5000 pixels with salinity values in the range 0 to 35.2 dS m − 1 . Multi-year maximum values of CRSI were used to model soil salinity. Soil type, meteorological data, and crop type were evaluated as covariates. All considered models were evaluated for their fit to the whole data set as well as their performance in a leave-one-field-out spatial cross-validation. The best performing model was a function of CRSI, crop type (i.e., cropped or fallow), rainfall, and average minimum temperature, with R 2 = 0.728 when evaluated against all data and R 2 = 0.611 for the cross-validation predictions. Broken out by salinity classes, the mean absolute errors (MAE) for the cross-validation predictions were (all units dS m − 1 ): 2.94 for the 0–2 interval (non-saline), 2.12 for 2–4 (slightly saline), 2.35 for 4–8 (moderately saline), 3.23 for 8–16 (strongly saline), and 5.64 for > 16 (extremely saline). On a per-field basis, the validation predictions had good agreement with the field average (R 2 = 0.79, MAE = 2.46 dS m − 1 ), minimum (R 2 = 0.76, MAE = 2.25 dS m − 1 ), and maximum (R 2 = 0.76, MAE = 3.09 dS m − 1 ) observed salinity. Overall, reasonably accurate and precise high resolution, regional-scale remote sensing of soil salinity is possible, even over the critical range of 0 to 20 dS m − 1 , where researchers and policy makers must focus to prevent loss of agricultural productivity and ecosystem health. [ABSTRACT FROM AUTHOR]

The Red Sea is a semi-enclosed tropical marine ecosystem that stretches from the Gulf of Suez and Gulf of Aqaba in the north, to the Gulf of Aden in the south. Despite its ecological and economic importance, its biological environment is relatively unexplored. Satellite ocean-colour estimates of chlorophyll concentration (an index of phytoplankton biomass) offer an observational platform to monitor the health of the Red Sea. However, little is known about the optical properties of the region. In this paper, we investigate the optical properties of the Red Sea in the context of satellite ocean-colour estimates of chlorophyll concentration. Making use of a new merged ocean-colour product, from the European Space Agency (ESA) Climate Change Initiative, and in situ data in the region, we test the performance of a series of ocean-colour chlorophyll algorithms. We find that standard algorithms systematically overestimate chlorophyll when compared with the in situ data. To investigate this bias we develop an ocean-colour model for the Red Sea, parameterised to data collected during the Tara Oceans expedition, that estimates remote-sensing reflectance as a function of chlorophyll concentration. We used the Red Sea model to tune the standard chlorophyll algorithms and the overestimation in chlorophyll originally observed was corrected. Results suggest that the overestimation was likely due to an excess of CDOM absorption per unit chlorophyll in the Red Sea when compared with average global conditions. However, we recognise that additional information is required to test the influence of other potential sources of the overestimation, such as aeolian dust, and we discuss uncertainties in the datasets used. We present a series of regional chlorophyll algorithms for the Red Sea, designed for a suite of ocean-colour sensors, that may be used for further testing. [ABSTRACT FROM AUTHOR]

Mountain glaciers are pertinent indicators of climate change and their dynamics, in particular surface velocity change, is an essential climate variable. In order to retrieve the climatic signature from surface velocity, large-scale study of temporal trends spanning multiple decades is required. Satellite image feature-tracking has been successfully used to derive mountain glacier surface velocities, but most studies rely on manually selected pairs of images, which is not adequate for large datasets. In this paper, we propose a processing strategy to exploit complete satellite archives in a semi-automated way in order to derive robust and spatially complete glacier velocities and their uncertainties on a large spatial scale. In this approach, all available pairs within a defined time span are analysed, preprocessed to improve image quality and features are tracked to produce a velocity stack; the final velocity is obtained by selecting measures from the stack with the statistically higher level of confidence. This approach allows to compute statistical uncertainty level associated with each measured image pixel. This strategy is applied to 1536 pairs of Landsat 5 and 7 images covering the 3000 km long Pamir–Karakoram–Himalaya range for the period of 1999–2001 to produce glacier annual velocity fields. We obtain a velocity estimate for 76,000 km 2 or 92% of the glacierized areas of this region. We then discuss the impact of coregistration errors and variability of glacier flow on the final velocity. The median 95% confidence interval ranges from 2.0 m/year on the average in stable areas and 4.4 m/year on the average over glaciers with variability related to data density, surface conditions and strain rate. These performances highlight the benefits of processing of a complete satellite archive to produce glacier velocity fields and to analyse glacier dynamics at regional scales. [ABSTRACT FROM AUTHOR]

Satellite-derived remote-sensing reflectance ( R rs ) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situ R rs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in R rs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies. [ABSTRACT FROM AUTHOR]

Globally, irrigation accounts for more than two thirds of freshwater demand. Despite their importance, the geographic distribution and water use of many important irrigated areas remains uncertain. This paper advances a methodology to map summer cropping irrigated areas experiencing extreme climate and different management practices in the Murray–Darling Basin (Australia). A Random Forest classification model was implemented to austral hemisphere summer irrigated areas for the water-years 2004/05 to 2010/11. The methodology used training samples from Landsat TM/ETM + reflectance data and monthly time-series of remotely-sensed observations from the MODerate resolution Imaging Spectroradiometer (MODIS). The covariates included in the classification model characterised the monthly dynamics and rates of change of: (i) the vegetation phenology via the recurrent and persistent absorbed fractions of photosynthetically active radiation (fPAR rec and fPAR per , respectively); (ii) water use via remotely-sensed estimates of actual evapotranspiration ( ET a ), precipitation ( P ) and the difference between ET a and P . Observed agreement – in terms of the kappa coefficient – for correctly classified pixels in the training sample was 96%. Independent comparisons of yearly irrigated area estimates showed linear relationships with Pearson's correlation coefficients ( r ) generally greater than 0.7 for: (i) reported areas; (ii) areas with available metered water withdrawals; and (iii) estimates of agricultural yields. Sequential covariate optimisation suggested that the most important predictors to identify of irrigation areas included the emergence–senescence period (as determined by the fPAR rec and corresponding rates of change) and the ET a surplus over P during this period. The latter can be important when identifying supplementary irrigation due to periodically unreliable water supply in areas with otherwise high precipitation that are in-phase with summer crop growth. [ABSTRACT FROM AUTHOR]

Abstract: Central Business Districts (CBDs) are an important urban structural type (UST), and an apparent structural feature of many large cities. CBD locations play a decisive role in the spatial arrangement of functions and exposures within cities. However, while past research underscores the importance of their spatial detection, delineation and cartographic representation, the definitions used are mostly functional and qualitative. Objective pre-defined methods/thresholds for the semi-automated spatial classification of CBDs, based on a quantitative approach, do not yet exist. This paper presents a conceptual framework to define the CBD using physical and morphological parameters, and tests the approach using 3-D city models of three European test sites (Canary Wharf in London, La Defense in Paris, and Levent in Istanbul). From these case studies, we develop a transferable method to detect and delineate CBDs over larger areas from a combination of Cartosat-1 digital surface models and multispectral Landsat ETM+ imagery. Applying this wide-area method to the entire extents of the three European megacities of London, Paris and Istanbul, we detect CBDs with a user accuracy of 75.7% and spatially delineate them with overall accuracies of 82.9%. Finally, we apply spatial metrics to analyze and compare the location and distribution of CBDs across the three mega cities, finding many similarities between London and Paris, but showing that Istanbul features a more complex urban footprint, and a different spatial CBD pattern. [Copyright &y& Elsevier]

Abstract: The use of satellite-based soil moisture retrievals for hydrologic, meteorological and climatological applications is advancing significantly due to increasing capability and temporal coverage of current and future missions. Characterisation of the relative skill of soil moisture products from different satellite sensors on a common spatial grid is crucial to achieve synergetic applications. This paper therefore evaluates three soil moisture products from AMSR-E (Advanced Microwave Scanning Radiometer — Earth Observing System), ASCAT (Advanced Scatterometer) and SMOS (Soil Moisture and Ocean Salinity) in absolute soil moisture units and on a common grid, against in-situ observations from southeast Australia. Before renormalisation, the three products yield correlations of 0.63–0.71 and a similar root-mean-square difference (RMSD) in the order of 0.1m3 m−3, although showing different levels of error contributions from bias, variance and correlations. The results are compared with land and precipitation data to investigate the sensitivity of their errors to land surface features. Three renormalisation strategies – minimum–maximum matching, mean/standard-deviation (μ–σ) matching and cumulative distribution function (CDF) matching – are considered for correcting systematic differences between ground and satellite data. The renormalised satellite data is found to retain RMSDs of 0.04–0.06m3 m−3 on average. The CDF method produces only marginal further improvements to correlations (0.67–0.75) and RMSDs compared to the μ–σ approach. The renormalisations by μ–σ and CDF methods also bring three products into better agreements with each other, but lead to strong correlations between RMSD and the dynamic range of in-situ soil moisture. [Copyright &y& Elsevier]

Abstract: The number of cloud-free views by Earth-orbiting optical remote sensing instruments must be characterized to verify that the coverage is sufficient to address a mission''s science objectives. This study determines the expected cloud-free coverage for the Hyperspectral Infra-red Imager (HyspIRI) mission concept using the Terra MODIS 5km daytime cloud mask dataset covering the period 2001–2010. The HyspIRI mission concept has two instruments: a Visible Shortwave Infrared (VSWIR) imaging spectrometer and a Thermal Infrared (TIR) multispectral imager. The VSWIR and TIR instruments have equatorial revisit times of 19 and 5days, respectively. Using the MODIS cloud mask data, we determined the percentage of the Earth''s land and shallow water regions that would have been viewable with cloud-free acquisitions for each quarter and year in 2001 through 2010. For the VSWIR, the average cloud-free coverage was 83.0% quarterly and 99.6% annually. Average TIR monthly coverage was also evaluated to be 90.7%. The standard deviations for these statistics were all less than 1.2%, and the average coverages exceed HyspIRI''s quarterly and annual requirements (listed in Table 6) by over twenty standard deviations. By the central limit theorem, it follows that in a typical year, HyspIRI will meet its coverage requirements (listed in Table 6) with extremely low uncertainty, acquiring a sufficient number of scenes to enable both monthly and quarterly assessments of the vast majority of the Earth''s land and shallow water. This paper includes: yearly global statistics for cloud-free MODIS observations at 5km resolution between 2001 and 2010; a one year global map representing percent cloud-free observations between 2001 and 2010; global maps representing percent cloud-free observations between 2001 and 2010 for each quarter; maps of the predicted number of views by each HyspIRI instrument quarterly and over the course of a year; and predicted global coverage quarterly and for one year for each HyspIRI instrument, with additional monthly statistics for TIR. [Copyright &y& Elsevier]

Abstract: A lack of spatially, structurally and thematically accurate habitat data complicates conservation and management planning as well as ecological research within structurally complex littoral enviromnents. The Scanning Hydrographic Operational Airborne Light Detection And Ranging (LiDAR) Survey (SHOALS) has considerable potential to provide such data, by means of its proficiency in generating high-resolution measurements of emerged and immersed elevations and to create thematic maps. Return signals, i.e., waveforms, contain signatures and structural information of salt-marsh and benthic cover. This paper focuses on the capability of the SHOALS to assess the structural complexity of emerged and immersed coastal habitats, and to define the contribution of SHOALS data, both elevation and intensity, in order to accurately and seamlessly map these habitats from supratidal to nearshore levels. The study area was selected based upon the variety of littoral cover types, encompassing kelp habitat, eelgrass meadow, beach, salt-marsh, farm and urban coastal environments. Firstly, the LiDAR-derived green waveform, through an ad hoc decision-tree, satisfactorily assessed the structural complexity of littoral habitats (r=0.75, p<0.01). Secondly, synergistic combination between NDLVI (LiDAR-derived red and near-infrared data) and topographic/bathymetric surfaces (LiDAR-derived near-infrared and green, respectively) enabled the accurate mapping of 19 littoral habitats (overall accuracy and kappa coefficient equaled to 90.09% and 0.8883, respectively). Accordingly, a single multispectral (three bands) LiDAR survey holds significant potential for monitoring small-scale structural complexity, as well as constructing spatially and temporally seamless patterns of coastal habitats. [Copyright &y& Elsevier]

Abstract: The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and clustering of the data space are considered to perform active selection. Experiments on urban and agricultural images show the great potential of the proposed strategy to perform model adaptation. [Copyright &y& Elsevier]

Abstract: Bubonic plague, caused by the bacteria Yersinia pestis, persists as a public health problem in many parts of the world, including central Kazakhstan. Bubonic plague occurs most often in humans through a flea bite, when a questing flea fails to find a rodent host. For many of the plague foci in Kazakhstan the great gerbil is the major host of plague, a social rodent well-adapted to desert environments. Intensive monitoring and prevention of plague in gerbils started in 1947, reducing the number of human cases and mortalities drastically. However, the monitoring is labour-intensive and hence expensive and is now under threat due to financial constraints. Previous research showed that the occupancy rate of the burrow systems of the great gerbil is a strong indicator for the probability of a plague outbreak. The burrow systems are around 30m in diameter with a bare surface. This paper aims to demonstrate the automatic classification of burrow systems in satellite images using object-oriented analysis. We performed field campaigns in September 2007 and May and September 2008 and acquired corresponding QuickBird images of the first two periods. User''s and producer''s accuracy values of the classification reached 60 and 86%, respectively, providing proof of concept that automatic mapping of burrow systems using high-resolution satellite images is possible. Such maps, by better defining great gerbil foci, locating new or expanding foci and measuring the density of great gerbil burrow systems could play a major role in a renewed monitoring system by better directing surveillance and control efforts. Furthermore, if similar analyses can separate occupied burrow systems from empty ones, then very-high-resolution images stand to play a crucial role in plague surveillance throughout central Asia. [Copyright &y& Elsevier]

Abstract: This paper presents a technique for mapping erythemally-weighted solar ultraviolet (EUV) radiation from satellite data in a tropical environment. A satellite-based EUV radiation model was formulated for calculating the EUV daily dose from satellite-derived earth-atmospheric albedo, total column ozone and other ground-based ancillary data. The earth-atmospheric albedo was obtained from a geostationary satellite (GMS5) while the total column ozone was retrieved from a polar orbiting satellite (EP/TOMS). The model was validated against the monthly average EUV daily dose from the measurements at four solar radiation monitoring stations located in the tropical environment of Thailand. The monthly average EUV daily dose calculated from the model was in reasonable agreement with that obtained from the measurement, with root mean square difference (RMSD) and mean bias difference (MBD) of 12.3% and 0.7%, respectively. After the validation, the model was used to calculate the monthly average EUV daily dose over Thailand employing an 8-year period of data from GMS5, EP/TOMS and other ancillary surface data. Values of the monthly average of EUV daily dose were presented as monthly and yearly maps. These maps reveal that the tropical monsoons have a strong influence on the EUV in this region. [Copyright &y& Elsevier]

Rossby waves are difficult to detect with in situ methods. However, as we show in this paper, they can be clearly identified in multi-parameters in multi-mission satellite observations of sea surface height (SSH), sea surface temperature (SST) and ocean color observations of chlorophyll-a (chl-a), as well as 1/12° global HYbrid Coordinate Ocean Model (HYCOM) simulations of SSH, SST and sea surface salinity (SSS) in the Indian Ocean. While the surface structure of Rossby waves can be elucidated from comparisons of the signal in different sea surface parameters, models are needed to gain direct information about how these waves affect the ocean at depth. The first three baroclinic modes of the Rossby waves are inferred from the Fast Fourier Transform (FFT), and two-dimensional Radon Transform (2D RT). At many latitudes the first and second baroclinic mode Rossby wave phase speeds from satellite observations and model parameters are identified. Wavelet transforms of these multi-parameters from satellite observations and model simulations help to discriminate between the annual and semi-annual signal of these Rossby waves. This comprehensive study reveals that the surface signature of Rossby waves in SSS anomalies is likely to be between 0.05 and 0.3 psu in the South Indian Ocean. [Copyright &y& Elsevier]

The radiation budget at the earth surface is an essential climate variable for climate monitoring and analysis as well as for verification of climate model output and reanalysis data. Accurate solar surface irradiance data is a prerequisite for an accurate estimation of the radiation budget and for an efficient planning and operation of solar energy systems. This paper describes a new approach for the retrieval of the solar surface irradiance from satellite data. The method is based on radiative transfer modelling and enables the use of extended information about the atmospheric state. Accurate analysis of the interaction between the atmosphere, surface albedo, transmission and the top of atmosphere albedo has been the basis for the new method, characterised by a combination of parameterisations and “eigenvector” look-up tables. The method is characterised by a high computing performance combined with a high accuracy. The performed validation shows that the mean absolute deviation is of the same magnitude as the confidence level of the BSRN (Baseline Surface Radiation Measurement) ground based measurements and significant lower as the CM-SAF (Climate Monitoring Satellite Application Facility) target accuracy of 10 W/m2. The mean absolute difference between monthly means of ground measurements and satellite based solar surface irradiance is 5 W/m2 with a mean bias deviation of −1 W/m2 and a RMSD (Root Mean Square Deviation) of 5.4 W/m2 for the investigated European sites. The results for the investigated African sites obtained by comparing instantaneous values are also encouraging. The mean absolute difference is with 2.8% even lower as for the European sites being 3.9%, but the mean bias deviation is with −1.1% slightly higher as for the European sites, being 0.8%. Validation results over the ocean in the Mediterranean Sea using shipboard data complete the validation. The mean bias is −3.6 W/m2 and 2.3% respectively. The slightly higher mean bias deviation over ocean is at least partly resulting from inherent differences due to the movement of the ship (shadowing, allocation of satellite pixel). The validation results demonstrate that the high accuracy of the surface solar irradiance is given in different climate regions. The discussed method has also the potential to improve the treatment of radiation processes in climate and Numerical Weather Prediction (NWP) models, because of the high accuracy combined with a high computing speed. [Copyright &y& Elsevier]

Accurate prediction of crop yield is essential for agricultural trading, market risk management and food security. Although various statistical models and machine learning models have been developed to enhance prediction accuracy, spatial and temporal non-stationarity, an intrinsic attribute of many geographical processes, is still rarely considered in crop yield modeling. From a statistical point of view, this study respectively provided evidence for the existence of spatial non-stationarity and temporal non-stationarity in winter wheat yield prediction based on geographically weighted regression (GWR) and temporally weighted regression (TWR). Then, a geographically and temporally weighted neural network (GTWNN) model was proposed by integrating artificial neural network (ANN) into geographically and temporally weighted regression (GTWR) using publicly available data sources, including satellite imagery and climate data. For a more credible evaluation, the leave-one-year-out strategy was adopted to make out-of-sample prediction resulting in a total of 12 test years from 2008 to 2019. The experiment results showed that the proposed GTWNN outperformed ANN, GTWR and support vector regression (SVR) achieving the average coefficient of determination (R 2) values of 0.766, 0.759 and 0.720 at the three prediction times of end of July, end of June and end of May. Moreover, an extended Moran's I was adopted to assess the degree of spatiotemporal autocorrelation of the prediction errors. The error aggregation of GTWNN was lower than other models, indicating that GTWNN is applicable to addressing spatial non-stationarity in modeling the relationship between predictors and yield response. The methodology proposed in this paper can be extended to handle spatiotemporal non-stationarity in other crop yield predictions and even other environmental phenomena. • The existence of spatial and temporal non-stationarity was statistically tested. • A GTWNN model was developed for winter wheat yield prediction in US. • GTWNN outperformed three widely used models including GTWR, ANN and SVR. • Moran's I values showed GTWNN's ability to address spatiotemporal non-stationarity. [ABSTRACT FROM AUTHOR]

Abstract: Within Australia, the discrimination and mapping of forest communities has traditionally been undertaken at the stand scale using stereo aerial photography. Focusing on mixed species forests in central south-east Queensland, this paper outlines an approach for the generation of tree species maps at the tree crown/cluster level using 1 m spatial resolution Compact Airborne Spectrographic Imager (CASI; 445.8 nm–837.7 nm wavelength) and the use of these to generate stand-level assessments of community composition. Following automated delineation of tree crowns/crown clusters, spectral reflectance from pixels representing maxima or mean-lit averages of channel reflectance or band ratios were extracted for a range of species including Acacia, Angophora, Callitris and Eucalyptus. Based on stepwise discriminant analysis, classification accuracies of dominant species were greatest (87% and 76% for training and testing datasets; n =398) when the mean-lit spectra associated with a ratio of the reflectance (ρ) at 742 nm (ρ 742) and 714 nm (ρ 714) were used. The integration of 2.6 m HyMap (446.1 nm–2477.8 nm) spectra increased the accuracy of classification for some species, largely because of the inclusion of shortwave infrared wavebands. Similar increases in accuracy were achieved when classifications of field spectra resampled to CASI and HyMap wavebands were compared. The discriminant functions were applied subsequently to classify crowns within each image and produce maps of tree species distributions which were equivalent or better than those generated through aerial photograph interpretation. The research provides a new approach to tree species mapping, although some a priori knowledge of the occurrence of broad species groups is required. The tree maps have application to biodiversity assessment in Australian forests. [Copyright &y& Elsevier]

Wild-land fires have become intense and more frequent all over the world. Improving the accuracy of mapping fuel models is essential for fuel management decisions and explicit fire behavior prediction for real-time support of suppression tactics and logistics decisions. The overall aim of this paper is to develop the use of lidar (LIght Detection and Ranging) remote sensing to accurately and effectively assess fuel models in East Texas. More specific goals include: (1) developing lidar derived products and the methodology to use them for assessing fuel models; (2) investigating the use of several techniques for data fusion of lidar and multispectral imagery for assessing fuel models; (3) investigating the gain in fuels mapping accuracy when using lidar as opposed to QuickBird imagery alone; and (4) producing spatially explicit digital fuel maps. Estimates of fuel models were compared with in-situ data collected over 62 plots. We employ a unique approach to classify fuel models using a combination of lidar height bins and multispectral image data. Different image processing approaches for fusing lidar and multispectral data, such as the Minimum Noise Fraction (MNF) and Principle Component Analysis (PCA), were used to improve the overall accuracy of image classification. Supervised image classification methods provided better accuracy (90.10%) with the fusion of airborne lidar data with QuickBird data than with QuickBird imagery alone (76.52%). According to our results, lidar derived data provide accurate estimates of surface fuel parameters efficiently and accurately over extensive areas of forests. This study demonstrates the importance of using accurate maps of fuel models derived using new lidar remote sensing techniques. [Copyright &y& Elsevier]

The potential of multitemporal coarse spatial resolution remotely sensed images for vegetation monitoring is reduced in fragmented landscapes, where most of the pixels are composed of a mixture of different surfaces. Several approaches have been proposed for the estimation of reflectance or NDVI values of the different land-cover classes included in a low resolution mixed pixel. In this paper, we propose a novel approach for the estimation of sub-pixel NDVI values from multitemporal coarse resolution satellite data. Sub-pixel NDVIs for the different land-cover classes are calculated by solving a weighted linear system of equations for each pixel of a coarse resolution image, exploiting information about within-pixel fractional cover derived from a high resolution land-use map. The weights assigned to the different pixels of the image for the estimation of sub-pixel NDVIs of a target pixel i are calculated taking into account both the spatial distance between each pixel and the target and their spectral dissimilarity estimated on medium-resolution remote-sensing images acquired in different periods of the year. The algorithm was applied to daily and 16-day composite MODIS NDVI images, using Landsat-5 TM images for calculation of weights and accuracy evaluation. Results showed that application of the algorithm provided good estimates of sub-pixel NDVIs even for poorly represented land-cover classes (i.e., with a low total cover in the test area). No significant accuracy differences were found between results obtained on daily and composite MODIS images. The main advantage of the proposed technique with respect to others is that the inclusion of the spectral term in weight calculation allows an accurate estimate of sub-pixel NDVI time series even for land-cover classes characterized by large and rapid spatial variations in their spectral properties. [Copyright &y& Elsevier]

Remote sensing represents a powerful tool to derive quantitative and qualitative information about ecosystem biodiversity. In particular, since plant species richness is a fundamental indicator of biodiversity at the community and regional scales, attempts were made to predict species richness (spatial heterogeneity) by means of spectral heterogeneity. The possibility of using spectral variance of satellite images for predicting species richness is known as Spectral Variation Hypothesis. However, when using remotely sensed data, researchers are limited to specific scales of investigation. This paper aims to investigate the effects of scale (both as spatial and spectral resolution) when searching for a relation between spectral and spatial (related to plant species richness) heterogeneity, by using satellite data with different spatial and spectral resolution. Species composition was sampled within square plots of 100 m2 nested in macroplots of 10,000 m2. Spectral heterogeneity of each macroplot was calculated using satellite images with different spatial and spectral resolution: a Quickbird multispectral image (4 bands, spatial resolution of 3 m), an Aster multispectral image (first 9 bands used, spatial resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9), an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band 7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+ image. Quickbird image heterogeneity showed a statistically highly significant correlation with species richness (r =0.69) while coarse resolution images showed contrasting results (r =0.43, r =0.67, and r =0.69 considering the Aster, Landsat ETM+, and the resampled 60 m Landsat ETM+ images respectively). It should be stressed that spectral variability is scene and sensor dependent. Considering coarser spatial resolution images, in such a case even using SWIR Aster bands (i.e. the additional spectral information with respect to Quickbird image) such an image showed a very low power in catching spectral and thus spatial variability with respect to Landsat ETM+ imagery. Obviously coarser resolution data tend to have mixed pixel problems and hence less sensitive to spatial complexity. Thus, one might argue that using a finer pixel dimension should inevitably result in a higher level of detail. On the other hand, the spectral response from different land-cover features (and thus different species) in images with higher spectral resolution should exhibit higher complexity. Spectral Variation Hypothesis could be a basis for improving sampling designs and strategies for species inventory fieldwork. However, researchers must be aware on scale effects when measuring spectral (and thus spatial) heterogeneity and relating it to field data, hence considering the concept of scale not only related to a spatial framework but even to a spectral one. [Copyright &y& Elsevier]

Net ecosystem exchange (NEE) of CO2 between the atmosphere and forest ecosystems is determined by gross primary production (GPP) of vegetation and ecosystem respiration. CO2 flux measurements at individual CO2 eddy flux sites provide valuable information on the seasonal dynamics of GPP. In this paper, we developed and validated the satellite-based Vegetation Photosynthesis Model (VPM), using site-specific CO2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA. The VPM model is built upon the conceptual partitioning of photosynthetically active vegetation and non-photosynthetic vegetation (NPV) within the leaf and canopy. It estimates GPP, using satellite-derived Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), air temperature and photosynthetically active radiation (PAR). Multi-year (1998–2001) data analyses have shown that EVI had a stronger linear relationship with GPP than did the Normalized Difference Vegetation Index (NDVI). Two simulations of the VPM model were conducted, using vegetation indices from the VEGETATION (VGT) sensor onboard the SPOT-4 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra satellite. The predicted GPP values agreed reasonably well with observed GPP of the deciduous broadleaf forest at Harvard Forest, Massachusetts. This study highlighted the biophysical performance of improved vegetation indices in relation to GPP and demonstrated the potential of the VPM model for scaling-up of GPP of deciduous broadleaf forests. [Copyright &y& Elsevier]

Leaf Area Index (LAI) is a fundamental vegetation biophysical variable serving as an essential input to many land surface and atmospheric models. Long-term LAI maps are typically generated with satellite images at moderate spatial resolution (0.25 to 1 km), such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS). While useful for regional-scale land surface modeling, these moderate resolution products often cannot resolve spatial heterogeneity important for many agricultural and hydrological applications. This paper proposes an approach to map LAI at 30-m resolution based on Landsat images for the Contiguous US (CONUS) consistent with the MODIS product, aimed at multi-scale modeling applications. The algorithm was driven by 1.6 million spatially homogeneous samples derived from MODIS LAI and Landsat surface reflectance products from 2006 to 2018. Based on these samples, we trained separate random forest models to estimate LAI from Landsat surface reflectance for eight biomes of the National Land Cover Database (NLCD). A balanced sample design regarding the saturation status of MODIS LAI and a machine-learning-based noise detection technique were introduced to mitigate the trade-off in estimation accuracy between medium LAI (e.g., 3 to 4, unsaturated) and high LAI (e.g., 4–6, saturated). This approach was evaluated using ground measurements from 19 National Ecological Observatory Network (NEON) sites and eight independent sites from other sources. These sites comprise a representative sample of forests, grasslands, shrublands, and croplands across the US. For NEON sites, the LAI estimates show an overall Root Mean Squared Error (RMSE) of 0.8 with r2 of 0.88. For the eight independent sites, the Landsat LAI algorithm achieves RMSE between 0.52 and 0.91. The uncertainty in Landsat estimated LAI varies across biomes and locations. The proposed algorithm was implemented on the Google Earth Engine platform, allowing for the rapid generation of long-term high-resolution LAI records from the 1980s using Landsat images (code is available at https://github.com/yanghuikang/Landsat-LAI). Our findings also highlight the importance of sample balance on regression-based modeling in remote sensing applications. • Map Leaf Area Index from Landsat images for CONUS using machine learning. • Generated 1.6 million samples from MODIS LAI and Landsat surface reflectance data. • Balanced sampling and a novel noise detection technique adopted to reduce bias. • Produce MODIS-consistent LAI estimation with improved spatial resolution. • Enable rapid generation of 30-m LAI back to the 1980s using Google Earth Engine. [ABSTRACT FROM AUTHOR]

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images. • A method to learn transferable deep model for 5-class land-cover (LC) classification. • A labeled dataset consisting of 150 Gaofen-2 images for LC classification. • It improves LC classification performance about 20% using multi-source RS images. • The method shows good transferability on different sensors and geolocations. [ABSTRACT FROM AUTHOR]

Satellite observations of sea ice along marginal ice zones suggest a strong coupling between sea ice transport and the underlying ocean turbulent eddy field. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery spanning over almost two decades of daily observations at a resolution of up to 250 m provides a good resource for deriving long-term ocean kinematics from sea ice dynamics. In this paper, we present a newly developed automatic algorithm to retrieve dynamic measurements of sea ice from these images. We describe the methodology by presenting results acquired along the East Greenland Current (ECG) for 6.5 weeks in the spring of 2017. During this period, our ice floe tracker was used to identify and track ice floes with length scales ranging from 8 to 65 km. By effectively filtering atmospheric conditions from MODIS images, ice floes were tracked for up to ten consecutive days, and a total of 1061 trajectories were retrieved. A southward mean sea ice flow associated with the ECG was observed along with deviations in both direction and magnitude, suggesting the effect of an underlying turbulent eddy field. The absolute position and tracking errors associated with our method are 255 m and 0.65 cm/s, respectively, each derived from a comparison between manually and automatically identified ice floes. Going forward, our methodology will be employed to process longer time sequences to analyze nonlinear interactions between drifting ice floes and the upper ocean turbulent eddy field in the ECG as well as to investigate other prominent regions of the Arctic Ocean. Unlabelled Image • Processing of MODIS imagery by adaptively filtering atmospheric conditions • Sea ice tracking via automatic matching based on surface features • Estimation of sea ice velocity fields calculated from retrieved trajectories [ABSTRACT FROM AUTHOR]