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  • 1. Jonsson, A. M.
    et al.
    Eklundh, L.
    Hellstrom, M.
    Bärring, Lars
    SMHI, Research Department, Climate research - Rossby Centre.
    Jonsson, P.
    Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology2010In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 114, no 11, p. 2719-2730Article in journal (Refereed)
    Abstract [en]

    Remote sensing provides spatially and temporally continuous measures of forest reflectance, and vegetation indices calculated from satellite data can be useful for monitoring climate change impacts on forest tree phenology. Monitoring of evergreen coniferous forest is more difficult than monitoring of deciduous forest, as the new buds only account for a small proportion of the green biomass, and the shoot elongation process is relatively slow. In this study, we have analyzed data from 186 coniferous monitoring sites in Sweden covering boreal, southern-boreal, and boreo-nemoral conditions. Our objective was to examine the possibility to track seasonal changes in coniferous forests by time-series of MODIS eight-day vegetation indices, testing the coherence between satellite monitored vegetation indices (VI) and temperature dependent phenology. The relationships between two vegetation indices (NDVI and WDRVI) and four phenological indicators (length of snow season, modeled onset of vegetation period, tree cold hardiness level and timing of budburst) were analyzed. The annual curves produced by two curve fitting methods for smoothening of seasonal changes in NDVI and WDRVI were to a large extent characterized by the occurrence of snow, producing stable seasonal oscillations in the northern part and irregular curves with less pronounced annual amplitude in the southern part of the country. Measures based on threshold values of the VI-curves, commonly used for determining the timing of different phenological phases, were not applicable for Swedish coniferous forests. Evergreen vegetation does not have a sharp increase in greenness during spring, and the melting of snow can influence the vegetation indices at the timing of bud burst in boreal forests. However, the interannual variation in VI-values for specific eight-day periods was correlated with the phenological indicators. This relation can be used for satellite monitoring of potential climate change impacts on northern coniferous spring phenology. (C) 2010 Elsevier Inc. All rights reserved.

  • 2.
    Karlsson, Karl-Göran
    et al.
    SMHI, Research Department, Atmospheric remote sensing.
    Johansson, Erik
    SMHI, Research Department, Atmospheric remote sensing.
    Devasthale, Abhay
    SMHI, Research Department, Atmospheric remote sensing.
    Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data2015In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 158, p. 126-139Article in journal (Refereed)
    Abstract [en]

    Two alternative methods for probabilistic cloud masking of images from the Advanced Very High Resolution Radiometer (AVHRR) sensor have been examined. Both methods are based on Bayesian theory and were trained using data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidar onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. Results were evaluated by comparing to independent CALIPSO-CALIOP observations and to a one-year ground-based cloud dataset composed from five different remote sensing systems over the observation site in Cabauw in the Netherlands. In addition, results were compared to two different cloud masks; one derived from the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor and one from the Climate Monitoring Satellite Application Facility Clouds (CMSAF), Albedo and Radiation dataset from AVHRR data (CLARA-A1). It was demonstrated that the probabilistic methods compare well with the referenced satellite datasets and for daytime conditions they provide even better performance than the reference methods. Among the two probabilistic approaches, it was found that the formulation based on a Naive Bayesian formulation (denoted PPS-Prob Naive) performed clearly superior to the formulation based on a linear summation of conditional cloud probabilities (denoted PPS-Prob SPARC) for daytime conditions. For the study based on the observations over the Cabauw site, the overall daytime Kuipers Skill Score for PPS-Prob Naive was 0.84, for PPS-Prob SPARC 0.79, for CLARA-A1 0.74 and for SEVIRI 0.66. Corresponding results for night-time conditions were less favourable for the probabilistic formulations (Kuipers Skill Score 0.74 for PPS-Prob Naive, 0.68 for PPS-Prob SPARC, 0.80 for CLARA-A1 and 0.79 for SEVIRI) but still relatively close to the reference dataset The Cabauw distribution of cloudiness occurrences in different octa categories was reproduced very closely by all methods, including the probabilistic formulations. Results based on Cabauw observations were also largely in good agreement with results deduced from comparisons with the CALIPSO-CALIOP cloud mask. The PPS-Prob Naive approach will be implemented in an upcoming version of the Polar Platform System (PPS) cloud software issued by the EUMETSAT Nowcasting Satellite Application Facility (NWC SAF). It will also be used in the second release of the CMSAF CLARA cloud climate data record based on historic AVHRR GAC data (to be denoted CIARA-A2). (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-SA license

  • 3. Lauer, Axel
    et al.
    Eyring, Veronika
    Righi, Mattia
    Buchwitz, Michael
    Defourny, Pierre
    Evaldsson, Martin
    SMHI, Research Department, Climate research - Rossby Centre.
    Friedlingstein, Pierre
    de Jeu, Richard
    de Leeuw, Gerrit
    Loew, Alexander
    Merchant, Christopher J.
    Mueller, Benjamin
    Popp, Thomas
    Reuter, Maximilian
    Sandven, Stein
    Senftleben, Daniel
    Stengel, Martin
    Van Roozendael, Michel
    Wenzel, Sabrina
    Willén, Ulrika
    SMHI, Research Department, Climate research - Rossby Centre.
    Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool2017In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 203, p. 9-39Article in journal (Refereed)
  • 4.
    Michelson, Daniel
    et al.
    SMHI, Core Services.
    Liljeberg, B M
    Pilesjo, P
    Comparison of algorithms for classifying Swedish landcover using Landsat TM and ERS-1 SAR data2000In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 71, no 1, p. 1-15Article in journal (Refereed)
    Abstract [en]

    Sixteen landcover classes in a representative Swedish environment were analyzed and classified using one Landsat TM scene and seven ERS-1 SARPRI images acquired during 1993. Spectral and backscattering signature separabilities are analyzed using the Jeffries-Matusita distance measure to determine which combinations of channels/images contained the most information. Maximum likelihood, sequential maximum a posteriori (SMAP, a Bayesian image segmentation algorithm), and back propagation neural network classification algorithms were applied and their performances evaluated. Results of the separability analyses indicated that the multitemporal SAR data contained more separable landcover information than did the multispectral TM data; the highest separabilities were achieved when the TM and SAR data were combined. Classification accuracy evaluation results indicate that the SMAP algorithm out-performed the maximum likelihood algorithm which, in turn, outperformed the neural network algorithm. The best KAPPA values, using combined data, were 0.495 for SMAP, 0.0445 for maximum likelihood, and 0.432 for neural network. Corresponding overall accuracy values were 57.1%, 52.4%, and 51.2%, respectively. A comparison between lumped crop area statistics with areal sums calculated from the classified satellite data gave the highest correspondence where the SMAP algorithm was used, followed by the maximum likelihood and neural network algorithms. Based on our application, we can therefore confirm the value of a multisource optical/SAR approach for analyzing landcover and the improvements to classification achieved using the SMAP algorithm. (C)Elsevier Science Inc., 2000.

  • 5. Pierson, Donald C.
    et al.
    Kratzer, Susanne
    Strombeck, Niklas
    Håkansson, Bertil
    SMHI, Core Services.
    Relationship between the attenuation of downwelling irradiance at 490 nm with the attenuation of PAR (400 nm-700 nm) in the Baltic Sea2008In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 112, no 3, p. 668-680Article in journal (Refereed)
    Abstract [en]

    The vertical attenuation coefficient of diffuse downwelling irradiance at 490 nm (K-d 490) is a parameter that we routinely derive from SeaWiFS images of the Baltic Sea. Here, through model simulations, we examine the relationship between Kd(490), and the vertical attenuation coefficient of PAR (Kd PAR), as this later coefficient determines the light available for aquatic photosynthesis. A simple semi-analytical model is used to predict Kd(490) and Kd(PAR), as a function of the concentrations of chlorophyll, colored dissolved organic material (CDOM), suspended inorganic, and suspended organic particulate material. A series of model simulations based on variations in these optically significant constituents over a range realistic for the Baltic Sea, are used to define the relationship between the two attenuation coefficients. K-d(PAR) = 0.6677K(d)(490)(0.6763). This relationship was verified, using data collected independently from the data set used to derive model coefficients, and appears robust when applied to the Baltic Sea. Comparison to other studies and model sensitivity analyses suggest that the relationship will be dependent on relatively large regional variations in CDOM absorption. A relationship between K-d(490) and Secchi disk depth was also developed and verified. This relationship while useful was more uncertain. The uncertainty was related to a greater influence of scattering on Secchi disk depth estimates and the corresponding parameterization of scattering in our model. (C) 2007 Elsevier Inc. All rights reserved.

  • 6. Stengel, M.
    et al.
    Mieruch, S.
    Jerg, M.
    Karlsson, Karl-Göran
    SMHI, Research Department, Atmospheric remote sensing.
    Scheirer, Ronald
    SMHI, Research Department, Atmospheric remote sensing.
    Maddux, B.
    Meirink, J. F.
    Poulsen, C.
    Siddans, R.
    Walther, A.
    Hollmann, R.
    The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements2015In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 162, p. 363-379Article in journal (Refereed)
    Abstract [en]

    Cloud property retrievals from 3 decades of the Advanced Very High Resolution Radiometer (AVHRR) measurements provide a unique opportunity for a long-term analysis of clouds. In this study, the accuracy of AVHRR-derived cloud properties cloud mask, cloud-top height, cloud phase and cloud liquid water path is assessed using three state-of-the-art retrieval schemes. In addition, the same retrieval schemes are applied to the AVHRR heritage channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) to create AVHRR-like retrievals with higher spatial resolution and based on presumably more accurate spectral calibration. The cloud property retrievals were collocated and inter-compared with observations from CloudSat, CALIPSO and AMSR-E The resulting comparison exhibited good agreement in general. The schemes provide correct cloud detection in 82 to 90% of all cloudy cases. With correct identification of clear-sky in 61 to 85% of all clear areas, the schemes are slightly biased towards cloudy conditions. The evaluation of the cloud phase classification shows correct identification of liquid clouds in 61 to 97% and a correct identification of ice clouds in 68 to 95%, demonstrating a large variability among the schemes. Cloud-top height (CTH) retrievals were of relatively similar quality with standard deviations ranging from 2.1 km to 2.7 km. Significant negative biases in these retrievals are found in particular for cirrus clouds. The biases decrease if optical depth thresholds are applied to determine the reference CTH measure. Cloud liquid water path (LWP) is also retrieved well with relative low standard deviations (20 to 28 g/m(2)), negative bias and high correlations. Cloud ice water path (IWP) retrievals of AVHRR and MODIS exhibit a relative high uncertainty with standard deviations between 800 and 1400 g/m2, which in relative terms exceed 100% when normalized with the mean IWP. However, the global histogram distributions of IWP were similar to the reference dataset MODIS retrievals are for most comparisons of slightly better quality than AVHRR-based retrievals. Additionally, the choice of different near-infrared channels, 3.7 mu M as opposed to 1.6 mu m, can have a significant impact on the retrieval quality, most pronounced for IWP, with better accuracy for the 1.6 mu m channel setup. This study presents a novel assessment of the quality of cloud properties derived from AVHRR channels, which quantifies the accuracy of the considered retrievals based on common approaches and validation data. Furthermore, it assesses the capabilities of AVHRR-like spectral information for retrieving cloud properties in the light of generating climate data records of cloud properties from three decades of AVHRR measurements. (C) 2013 Elsevier Inc. All rights reserved.

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  • Other style
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