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  • 1. Brown, Laura C.
    et al.
    Howell, Stephen E. L.
    Mortin, Jonas
    Stockholm University, Faculty of Science, Department of Meteorology .
    Derksen, Chris
    Evaluation of the Interactive Multisensor Snow and Ice Mapping System (IMS) for monitoring sea ice phenology2014In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 147, p. 65-78Article in journal (Refereed)
    Abstract [en]

    We present an evaluation of the Interactive Multisensor Snow and Ice Mapping System (IMS) for monitoring northern hemisphere sea ice phenology. Analysts utilize a variety of datasets to manually derive the daily extent of snow, ice, water and land, available at both 24 and 4 km. The 4 km IMS product was assessed for 2004-2008 against several previously established melt/freeze algorithms using Scatterometer Image Reconstruction (SIR) SeaWinds/QuikSCAT (QuikSCAT) backscatter (sigma degrees), Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) brightness temperature (T-B) measurements, data from the Special Sensor Microwave/Image data (SSM/I) and sea ice concentrations derived from DMSP Special Sensor Microwave/Imager-Special Sensor Microwave Imager Sounder (SSMI-SSMIS) data (NASATeam dataset). The resolution possible with the 4 km IMS product allows for better spatial representation of sea ice along the coastlines, the ice edges and in the narrow channels of the Canadian Arctic Archipelago as compared to the microwave products. IMS detects open water earlier and freeze onset later than the automated microwave products, and also allows for the detection of opening, and the subsequent closing, of leads that the other datasets are unable to detect. Using RADARSAT-1 imagery for evaluation, IMS is shown to outperform the other datasets for the timing and extent of the first open water detection. IMS identified between 17 and 53% greater open water coverage than the other datasets in the narrow channels of the Northwest Passage (Barrow Strait). In order to further the use of IMS for sea ice applications, we derived two new spatial datasets using the full record of IMS data (4 km: 2004-present 24 km: 1997-present): melt duration to open water (duration from melt onset detected with SSM/I passive microwave until open water detected by IMS) and first year ice cover duration (duration from freeze onset until open water, both detected by IMS). Crown Copyright (C) 2014 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).

  • 2. Gålfalk, Magnus
    et al.
    Olofsson, Göran
    Stockholm University, Faculty of Science, Department of Astronomy.
    Bastviken, David
    Approaches for hyperspectral remote flux quantification and visualization of GHGs in the environment2017In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 191, p. 81-94Article in journal (Refereed)
    Abstract [en]

    Methane (CH4) and nitrous oxide (N2O) are two very potent greenhouse gases, with highly heterogeneous distributions in both space and time. Mapping hot-spots and source areas, and measuring fluxes in different environments has so far not been possible on a local scale using direct measurements. We have developed a method for simultaneous mapping of methane (CH4) and nitrous oxide (N2O), also including water vapor (H2O), using ground-based remote sensing on a landscape-sized scale by utilizing Imaging Fourier Transform Spectrometers (IFTS) with high spectral resolution and imaging rates. The approach uses calculated libraries of transmission spectra at the spectroscopic resolutions of the IFTS, based on the HITRAN database of spectroscopic lines and our own line-by-line radiative transfer model (LBLRTM). For each species, 1024 spectra have been made, resulting in 10243 combinations of column densities. Using an adaptive grid, solutions are found for each line of sight at a spectral resolution of up to 0.25 cm(-1) using the full spectral region of the detector. The modeling is multi-layered, calculating temperatures of the background, air, and any additional gas layers, also accounting for reflected cold sky. Background distances can be mapped from the amount of water vapor in each line of sight. The described approach can be used to identify sources, quantify gas distributions, and to calculate fluxes. Visualizations can produce gas distribution images, as well as air motion videos, which are used to map fluxes using the same data set, without the need for additional instruments for wind measurements.

  • 3.
    Harvey, E. Therese
    et al.
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Kratzer, Susanne
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Philipson, Petra
    Satellite-based water quality monitoring for improved spatial and temporal retrieval of chlorophyll-a in coastal waters2015In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 158, p. 417-430Article in journal (Refereed)
    Abstract [en]

    The coastal zones are the most inhabited areas of the world and are therefore strongly affected by humans, leading to undesirable environmental changes that may alter the ecosystems, such as eutrophication. In order to evaluate changes in the environment an effective water quality monitoring system for the coastal zones must be in place. The chlorophyll-a concentration is commonly used as a proxy for phytoplankton biomass and as indicator for eutrophication and it can be retrieved from ocean colour remote sensing data. Several operational monitoring systems based on remote sensing are in place to monitor the open sea and, to some extent, the coastal zones. However, evaluations of coastal monitoring systems based on satellite data are scarce. This paper compares the chlorophyll-a concentrations retrieved from an operational satellite system based on MERIS (Medium Resolution Imaging Spectrophotometer) data with ship-based monitoring for the productive seasons in 2008 and 2010, in a coastal area in the Baltic Sea. The comparisons showed that the satellite-based monitoring system is reliable and that the estimations of chlorophyll-a concentration are comparable to in situ measurements in terms of accuracy and quantitative retrieval. A very strong correlation was found between measurements from satellite-derived chlorophyll-a compared to in situ measurements taken close in time (0-3 days), with RMSE of 64% and a MNB of 17%. The comparison of the monthly means showed improved RMSE and a MNB of only 8%. Furthermore, this study shows that MERIS is better at capturing spatial dynamics and the extent of phytoplankton blooms than ship-based monitoring, since it has a synoptic view and higher temporal resolution. Satellite-based monitoring also increases the frequency of chlorophyll-a observations considerably, where the degree of improvement is dependent on the sampling frequency of the respective monitoring programme. Our results show that ocean colour remote sensing can, when combined with field sampling, provide an improved basis for more effective monitoring and management of the coastal zone. These results are important for eutrophication assessment and status classifications of water basins and can be applied to a larger extent within national and international agreements considering the coastal zones, e.g. the European Commission's Water Framework Directive.

  • 4.
    Kratzer, Susanne
    et al.
    Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology (INK). Stockholm University, Faculty of Science, Department of Systems Ecology.
    Brockmann, Carsten
    Moore, Gerald
    Using MERIS full resolution data to monitor coastal waters: A case study from Himmerfjärden, a fjord-like bay in the northwestern Baltic Sea2008In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 112, no 5, p. 2284-2300Article in journal (Refereed)
    Abstract [en]

    In this paper we investigate if MERIS full resolution (FR) data (300 m) is sufficient to monitor changes in optical constituents in Himmerfjärden, a fjord-like, north– south facing bay of about 30 km length and 4 km width. The MERIS FR products were derived using a coastal processor (FUB Case-2 Plug-In). We also compared the performance between FUB and standard processor (MEGS 7.4), using reduced resolution (RR) data (1 km resolution) from the open Baltic Sea, and compared the products to sea-truthing data. The optical variables measured for seatruthing

    were chlorophyll, suspended particulate matter (SPM), as well as coloured dissolved organic matter (CDOM, also termed yellow substances), and the spectral diffuse attenuation coefficient, K d (490). The comparison of the RR data to the sea-truthing data showed that, in the open Baltic Sea, the MERIS standard processor overestimated chlorophyll by about 59%, and SPM by about 28%, and underestimated yellow substance by about 81%, whereas the FUB processor underestimated SPM by about 60%, CDOM by about 78%, and chlorophyll a by about 56%.

    The FUB processor showed a relatively high precision for all optical components (standard deviation: 6– 18%), whereas the precision for the MEGS 7.4 was rather low (standard deviation: 43– 73%), except for CDOM (standard deviation: 13%). The analysis of the FR data showed that all FR level 2 water products derived from MERIS followed a polynomial decline in concentration when moving off-shore. The distribution of chlorophyll and SPM was best described by a 2nd order polynomial, and the distribution of CDOM by a 3rd order polynomial, verifying the

    diffusional model described in Kratzer and Tett [Kratzer, S. and Tett, P. (in press). Using bio-optics to investigate the extent of coastal waters— a Swedish case study. Hydrobiologia.]. A new K d (490) and Secchi depth algorithm based on MERIS channel 3 (490 nm) and channel 6 (620 nm) each was derived from radiometric sea-truthing data (TACCS, Satlantic). Applying the K d (490) algorithm to the MERIS FR data over Himmerfjärden, and comparing to sea-truthing data the results showed a strong correlation (r =0.94). When comparing the FR data to the seatruthing

    data CDOM and K d (490) showed a low accuracy, but a high precision with a rather constant off-set. In summary, one may state that the precision of MERIS data improves by applying the FUB Case-2 processor and the accuracy improves with improved spatial resolution for chlorophyll and SPM. Furthermore, the FUB processor can be used off-the-shelf for open Baltic Sea monitoring, provided one corrects for the respective off-set from sea-truthing data which is most likely caused by an inaccuracy in the atmospheric correction. Additionally, the FR data can

    be used to derive CDOM, K d (490) and Secchi depth in Himmmerfjärden if one corrects for the respective off-set. We will need to perform more comparisons between sea-truthing and MERIS FR data before the new K d (490) algorithm can be made operational, including also scenes from other times of year. In order to provide a level 2 product that can be used reliably by the Baltic Sea user community, our recommendation to ESA is to include the spectral attenuation coefficient as a MERIS standard product.

  • 5.
    Krishnaswamy, Jagdish
    et al.
    Stockholm University, Stockholm Resilience Centre.
    Bawa, Kamaijit S.
    Ganeshaiah, K. N.
    Kiran, M. C.
    Quantifying and mapping biodiversity and ecosystem services: Utility of a multi-season NDVI based Mahalanobis distance surrogate2009In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 113, no 4, p. 857-867Article in journal (Refereed)
    Abstract [en]

    There is an urgent need for techniques to rapidly and periodically measure biodiversity and ecosystem services over large landscapes. Conventional vegetation classification and mapping approaches are based on discrete arbitrary classes which do not capture gradual changes in forest type (and corresponding biodiversity and ecosystem services values) from site to site. We developed a simple multi-date NDVI based Mahalanobis distance measure (called eco-climatic distance) that quantifies forest type variability across a moisture gradient for complex tropical forested landscapes on a single ecologically interpretable, continuous scale. This Mahalanobis distance, unlike other distance measures takes into account the variability in the reference class and shared information amongst bands as it is based on the covariance matrix, and therefore is most useful to summarize ecological distance of a pixel to a reference class in multi-band remotely sensed space In this study we successfully apply this measure as a surrogate for tree biodiversity and ecosystem services at two nested scales for the Western Chats Bio-diversity hotspot. Data from over 500 tree-plots and forest type maps was used to test the ability of this remotely sensed distance to be a surrogate for abundance based tree-species compositional turn-over and as a continuous measure of forest type and ecosystem services. Our results suggest a strong but scale dependant relationship between the remotely-sensed distance measure and floristic distance between plots. The multi-date NDVI distance measure emerges as very good quantitative surrogate for forest type and is a useful complement to existing forest classification systems. This surrogate quantifies forest type variability on a single, continuous quantitative scale and has important applications in conservation planning and mapping and monitoring of hydrologic and carbon storage and sequestration services.

  • 6.
    Mortin, Jonas
    et al.
    Stockholm University, Faculty of Science, Department of Meteorology .
    Howell, Stephen E. L.
    Climate Research Division, Environment Canada.
    Wang, Libo
    Climate Research Division, Environment Canada.
    Derksen, Chris
    Climate Research Division, Environment Canada.
    Svensson, Gunilla
    Stockholm University, Faculty of Science, Department of Meteorology .
    Graversen, Rune G.
    Stockholm University, Faculty of Science, Department of Meteorology .
    Schrøder, Thomas M.
    California Institute of Technology, USA.
    Extending the QuikSCAT record of seasonal melt–freeze transitions over Arctic sea ice using ASCAT2014In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 141, no 5, p. 214-230Article in journal (Refereed)
    Abstract [en]

    The seasonal melt–freeze transitions are important to continuously monitor over Arctic sea ice in order to better understand Arctic climate variability. The Ku-band scatterometer QuikSCAT (13.4 GHz), widely used to retrieve pan-Arctic seasonal transitions, discontinued its decadal long record in 2009. In this study, we show that the C-band scatterometer ASCAT (5.3 GHz), in orbit since 2006 and with an anticipated lifetime through 2021, can be used to extend the QuikSCAT record of seasonal melt–freeze transitions. This is done by (1) comparing back- scatter measurements over multiyear and first-year ice, and by (2) retrieving seasonal transitions from resolution-enhanced ASCAT and QuikSCAT measurements and comparing the results with independent datasets. Despite operating in different frequencies, ASCAT and QuikSCAT respond similarly to surface transitions. However, QuikSCAT measurements respond slightly stronger to the early melt of first-year ice, making it less sensitive to sea-ice dynamics. To retrieve the transitions, we employed an improved edge-detector algorithm, which was iterated and constrained using sea-ice concentration data, efficiently alleviating unreasonable outliers. This gives melt–freeze transitions over all Arctic sea ice north of 60°N at a 4.45 km resolution during 1999–2009 and 2009–2012 for QuikSCAT and ASCAT, respectively. Using the sensor overlap period, we show that the retrieved transitions retrieved from the different instruments are largely consistent across all regions in the Arctic sea-ice domain, indicating a robust consistency.

  • 7. Santoro, Maurizio
    et al.
    Beaudoin, Andre
    Beer, Christian
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry.
    Cartus, Oliver
    Fransson, Johan B. S.
    Hall, Ronald J.
    Pathe, Carsten
    Schmullius, Christiane
    Schepaschenko, Dmitry
    Shvidenko, Anatoly
    Thurner, Martin
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. Max-Planck Institute for Biogeochemistry, Germany.
    Wegmueller, Urs
    Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR2015In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 168, p. 316-334Article in journal (Refereed)
    Abstract [en]

    This paper presents and assesses spatially explicit estimates of forest growing stock volume (GSV) of the northern hemisphere (north of 10 degrees N) from hyper-temporal observations of Envisat Advanced Synthetic Aperture Radar (ASAR) backscattered intensity using the BIOMASAR algorithm. Approximately 70,000 ASAR images at a pixel size of 0.01 degrees were used to estimate GSV representative for the year 2010. The spatial distribution of the GSV across four ecological zones (polar, boreal, temperate, subtropical) was well captured by the ASAR-based estimates. The uncertainty of the retrieved GSV was smallest in boreal and temperate forest (<30% for approximately 80% of the forest area) and largest in subtropical forest. ASAR-derived GSV averages at the level of administrative units were mostly in agreement with inventory-derived estimates. Underestimation occurred in regions of very high GSV (>300 m(3)/ha) and fragmented forest landscapes. For the major forested countries within the study region, the relative RMSE between ASAR-derived GSV averages at provincial level and corresponding values from National Forest Inventory was between 12% and 45% (average: 29%).

  • 8. Sterckx, S.
    et al.
    Knaeps, S.
    Kratzer, Susanne
    Stockholm University, Faculty of Science, Department of Ecology, Environment and Plant Sciences.
    Ruddick, K.
    SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters2015In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 157, p. 96-110Article in journal (Refereed)
    Abstract [en]

    The launch of several new satellites such as Sentinel-2, Sentinel-3, HyspIRI, EnMAP and PRISMA in the very near future, opens new perspectives for the inland and coastal water community. The monitoring of the water quality closer to the coast, within estuaries or small lakes with satellite data will become feasible. However for these inland and nearshore coastal waters, adjacency effects may hamper the correct retrieval of water quality parameters from remotely sensed imagery. Here, we present a sensor-generic adjacency pre-processing method, SIMilarity Environment Correction (SIMEC). The correction algorithm estimates the contribution of the background radiance based on the correspondence with the Near-INfrared (NIR) similarity spectrum. The performance of SIMEC was tested on MERIS FR images both above highly reflecting waters with high SPM loads, as well as dark lake waters with high CDOM absorption. The results show that SIMEC has a positive or neutral effect on the normalized remote sensing reflectance above optically-complex waters, retrieved with the MERIS MEGS or UR processor.

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