TY - JOUR A1 - Segl, Karl A1 - Guanter, Luis A1 - Kaufmann, Hermann A1 - Schubert, Josef A1 - Kaiser, Stefan A1 - Sang, Bernhard A1 - Hofer, Stefan T1 - Simulation of spatial sensor characteristics in the context of the EnMAP Hyperspectral mission N2 - The simulation of remote sensing images is a valuable tool for defining future Earth observation systems, optimizing instrument parameters, and developing and validating data-processing algorithms. A scene simulator for optical Earth observation data has been developed within the Environmental Mapping and Analysis Program (EnMAP) hyperspectral mission. It produces EnMAP-like data following a sequential processing approach consisting of five independent modules referred to as reflectance, atmospheric, spatial, spectral, and radiometric modules. From a modeling viewpoint, the spatial module is the most complex. The spatial simulation process considers the satellite-target geometry, which is adapted to the EnMAP orbit and operating characteristics, the instrument spatial response, and the sources of spatial nonuniformity (keystone, telescope distortion and smile, and detector coregistration). The spatial module of the EnMAP scene simulator is presented in this paper. The EnMAP spatial and geometric characteristics will be described, the simulation methodology will be presented in detail, and the capability of the EnMAP simulator will be shown by illustrative examples. Y1 - 2010 U6 - https://doi.org/10.1109/Tgrs.2010.2042455 SN - 0196-2892 ER - TY - GEN A1 - Hollstein, André A1 - Segl, Karl A1 - Guanter, Luis A1 - Brell, Maximilian A1 - Enesco, Marta T1 - Ready-to-Use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images T2 - remote sensing N2 - Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2's of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage, spectral coverage, and repetition rate. Efficient algorithms are needed to process, or possibly reprocess, those big amounts of data. Techniques based on top-of-atmosphere reflectance spectra for single-pixels without exploitation of external data or spatial context offer the largest potential for parallel data processing and highly optimized processing throughput. Such algorithms can be seen as a baseline for possible trade-offs in processing performance when the application of more sophisticated methods is discussed. We present several ready-to-use classification algorithms which are all based on a publicly available database of manually classified Sentinel-2A images. These algorithms are based on commonly used and newly developed machine learning techniques which drastically reduce the amount of time needed to update the algorithms when new images are added to the database. Several ready-to-use decision trees are presented which allow to correctly label about 91% of the spectra within a validation dataset. While decision trees are simple to implement and easy to understand, they offer only limited classification skill. It improves to 98% when the presented algorithm based on the classical Bayesian method is applied. This method has only recently been used for this task and shows excellent performance concerning classification skill and processing performance. A comparison of the presented algorithms with other commonly used techniques such as random forests, stochastic gradient descent, or support vector machines is also given. Especially random forests and support vector machines show similar classification skill as the classical Bayesian method. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 455 KW - Sentinel-2 MSI KW - cloud detection KW - snow detection KW - cirrus detection KW - shadow detection KW - Bayesian classification KW - machine learning KW - decision trees Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-407938 ER - TY - JOUR A1 - Brell, Maximilian A1 - Rogass, Christian A1 - Segl, Karl A1 - Bookhagen, Bodo A1 - Guanter, Luis T1 - Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data JF - IEEE transactions on geoscience and remote sensing N2 - Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration. KW - Airborne laser scanning (ALS) KW - coregistration KW - direct georeferencing KW - imaging spectroscopy KW - multisensor KW - parametric georeferencing KW - preprocessing KW - ray tracing KW - rigorous geocoding KW - sensor alignment KW - sensor fusion Y1 - 2016 U6 - https://doi.org/10.1109/TGRS.2016.2518930 SN - 0196-2892 SN - 1558-0644 VL - 54 SP - 3460 EP - 3474 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER - TY - JOUR A1 - Brell, Maximilian A1 - Segl, Karl A1 - Guanter, Luis A1 - Bookhagen, Bodo T1 - Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration JF - IEEE transactions on geoscience and remote sensing N2 - The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimen-sionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications. KW - Airborne laser scanning (ALS) KW - deshadowing KW - imaging spectroscopy KW - in-flight KW - mosaicking KW - pixel-level fusion KW - preprocessing KW - radiometric alignment KW - ray tracing KW - sensor alignment KW - sensor fusion Y1 - 2017 U6 - https://doi.org/10.1109/TGRS.2017.2654516 SN - 0196-2892 SN - 1558-0644 VL - 55 SP - 2799 EP - 2810 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER - TY - JOUR A1 - Walther, Sophia A1 - Guanter, Luis A1 - Heim, Birgit A1 - Jung, Martin A1 - Duveiller, Gregory A1 - Wolanin, Aleksandra A1 - Sachs, Torsten T1 - Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis JF - Biogeosciences N2 - High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR <= GPP < SIF < VIs/VOD. SIF as an indicator of photosynthesis is maximised around the time of highest annual temperatures. The modelled GPP peaks at a similar time to APAR. The time lag of the annual peak between APAR and instantaneous SIF fluxes indicates that the SIF data do contain information on light-use efficiency of tundra vegetation, but further detailed studies are necessary to verify this. Delayed peak greenness compared to peak photosynthesis is consistently found across years and land-cover classes. A particularly late peak of the normalised difference vegetation index (NDVI) in regions with very small seasonality in greenness and a high amount of lakes probably originates from artefacts. Given the very short growing season in circumpolar areas, the average time difference in maximum annual photosynthetic activity and greenness or growth of 3 to 25 days (depending on the data sets chosen) is important and needs to be considered when using satellite observations as drivers in vegetation models. Y1 - 2018 U6 - https://doi.org/10.5194/bg-15-6221-2018 SN - 1726-4170 SN - 1726-4189 VL - 15 IS - 20 SP - 6221 EP - 6256 PB - Copernicus CY - Göttingen ER - TY - GEN A1 - Walther, Sophia A1 - Guanter, Luis A1 - Heim, Birgit A1 - Jung, Martin A1 - Duveiller, Gregory A1 - Wolanin, Aleksandra A1 - Sachs, Torsten T1 - Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - High-latitude treeless ecosystems represent spatially highly heterogeneous landscapes with small net carbon fluxes and a short growing season. Reliable observations and process understanding are critical for projections of the carbon balance of the climate-sensitive tundra. Space-borne remote sensing is the only tool to obtain spatially continuous and temporally resolved information on vegetation greenness and activity in remote circumpolar areas. However, confounding effects from persistent clouds, low sun elevation angles, numerous lakes, widespread surface inundation, and the sparseness of the vegetation render it highly challenging. Here, we conduct an extensive analysis of the timing of peak vegetation productivity as shown by satellite observations of complementary indicators of plant greenness and photosynthesis. We choose to focus on productivity during the peak of the growing season, as it importantly affects the total annual carbon uptake. The suite of indicators are as follows: (1) MODIS-based vegetation indices (VIs) as proxies for the fraction of incident photosynthetically active radiation (PAR) that is absorbed (fPAR), (2) VIs combined with estimates of PAR as a proxy of the total absorbed radiation (APAR), (3) sun-induced chlorophyll fluorescence (SIF) serving as a proxy for photosynthesis, (4) vegetation optical depth (VOD), indicative of total water content and (5) empirically upscaled modelled gross primary productivity (GPP). Averaged over the pan-Arctic we find a clear order of the annual peak as APAR ≦ GPP