@article{WaltherGuanterHeimetal.2018,
  author    = {Walther, Sophia and Guanter, Luis and Heim, Birgit and Jung, Martin and Duveiller, Gregory and Wolanin, Aleksandra and Sachs, Torsten},
  title     = {Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis},
  series = {Biogeosciences},
  volume    = {15},
  journal   = {Biogeosciences},
  number    = {20},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1726-4170},
  doi       = {10.5194/bg-15-6221-2018},
  pages     = {6221 -- 6256},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{WaltherDuveillerJungetal.2019,
  author    = {Walther, Sophia and Duveiller, Gregory and Jung, Martin and Guanter, Luis and Cescatti, Alessandro and Camps-Valls, Gustau},
  title     = {Satellite Observations of the Contrasting Response of Trees and Grasses to Variations in Water Availability},
  series = {Geophysical research letters},
  volume    = {46},
  journal   = {Geophysical research letters},
  number    = {3},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1029/2018GL080535},
  pages     = {1429 -- 1440},
  year      = {2019},
  abstract  = {Interannual variations in ecosystem primary productivity are dominated by water availability. Until recently, characterizing the photosynthetic response of different ecosystems to soil moisture anomalies was hampered by observational limitations. Here, we use a number of satellite-based proxies for productivity, including spectral indices, sun-induced chlorophyll fluorescence, and data-driven estimates of gross primary production, to reevaluate the relationship between terrestrial photosynthesis and water. In contrast to nonwoody vegetation, we find a resilience of forested ecosystems to reduced soil moisture. Sun-induced chlorophyll fluorescence and data-driven gross primary production indicate an increase in photosynthesis as a result of the accompanying higher amounts of light and temperature despite lowered light-use-efficiency. Conversely, remote sensing indicators of greenness reach their detection limit and largely remain stable. Our study thus highlights the differential responses of ecosystems along a tree cover gradient and illustrates the importance of differentiating photosynthesis indicators from those of greenness for the monitoring and understanding of ecosystems. Plain Language Summary The capacity of vegetation to thrive and to sequester carbon depends on how much water they can have access to. In this work, we evaluate how different types of satellite observations can describe the response of vegetation to changes in soil moisture over the entire planet. The first source of observation measures only the greenness of the land surface, the second measures light that is emitted by pigments in plants which are photosynthetically active (chlorophyll fluorescence), and the third are simulations of gross carbon uptake derived from machine learning techniques. For periods of water shortage all three indicate a reduction of growth in ecosystems with few trees. However, in cold boreal forests, when soil moisture is particularly low, we still detect an increase in photosynthesis due to higher light and temperature conditions, but this is not reflected in the greenness indicator. This work illustrates how lack of water is not necessarily harmful for catching carbon through photosynthesis, but to monitor this effect, we need remote sensing indicators that measure more than just how green the plants are, and fluorescence is likely a good candidate.},
  language  = {en}
}
@article{BrellSeglGuanteretal.2019,
  author    = {Brell, Maximilian and Segl, Karl and Guanter, Luis and Bookhagen, Bodo},
  title     = {3D hyperspectral point cloud generation},
  series = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing},
  volume    = {149},
  journal   = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0924-2716},
  doi       = {10.1016/j.isprsjprs.2019.01.022},
  pages     = {200 -- 214},
  year      = {2019},
  abstract  = {Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land cover mapping and environmental applications.},
  language  = {en}
}
@misc{WaltherGuanterHeimetal.2018,
  author    = {Walther, Sophia and Guanter, Luis and Heim, Birgit and Jung, Martin and Duveiller, Gregory and Wolanin, Aleksandra and Sachs, Torsten},
  title     = {Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1025},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44620},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-446205},
  pages     = {6221 -- 6256},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{MilewskiChabrillatBrelletal.2019,
  author    = {Milewski, Robert and Chabrillat, Sabine and Brell, Maximillian and Schleicher, Anja Maria and Guanter, Luis},
  title     = {Assessment of the 1.75μm absorption feature for gypsum estimation using laboratory, air- and spaceborne hyperspectral sensors},
  series = {International Journal of Applied Earth Observation and Geoinformation},
  volume    = {77},
  journal   = {International Journal of Applied Earth Observation and Geoinformation},
  publisher = {Elsevier Science},
  address   = {Amsterdam [u.a.]},
  issn      = {0303-2434},
  doi       = {10.1016/j.jag.2018.12.012},
  pages     = {69 -- 83},
  year      = {2019},
  abstract  = {High spectral resolution (hyperspectral) remote sensing has already demonstrated its capabilities for soil constituent mapping based on absorption feature parameters. This paper tests different parametrizations of the 1.75 μm gypsum feature for the determination of gypsum abundances, from the laboratory to remote sensing applications of recent as well as upcoming hyperspectral sensors. In particular, this study focuses on remote sensing imagery over the large body of the Omongwa pan located in the Namibian Kalahari. Four common absorption feature parameters are compared: band ratio through the introduction of the Normalized Differenced Gypsum Index (NDGI), the shape-based parameters Slope, and Half-Area, and the Continuum Removed Absorption Depth (CRAD). On laboratory soil samples from the pan, CRAD and NDGI approaches perform best to determine gypsum content tested in cross validated regression models with XRD mineralogical data (R² = 0.84 for NDGI and R² = 0.86 for CRAD). Subsequently the laboratory prediction functions are transferred to remote sensing imagery of spaceborne Hyperion, airborne HySpex and simulated spaceborne EnMAP sensor. Variable results were obtained depending on sensor characteristics, data quality, preprocessing and spectral parameters. Overall, the CRAD parameter in this wavelength region proved not to be robust for remote sensing applications, and the simple band ratio based parameter, the NDGI, proved robust and is recommended for future use for the determination of gypsum content in bare soils based on remote sensing hyperspectral imagery.},
  language  = {en}
}
@article{BrellSeglGuanteretal.2017,
  author    = {Brell, Maximilian and Segl, Karl and Guanter, Luis and Bookhagen, Bodo},
  title     = {Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration},
  series = {IEEE transactions on geoscience and remote sensing},
  volume    = {55},
  journal   = {IEEE transactions on geoscience and remote sensing},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Piscataway},
  issn      = {0196-2892},
  doi       = {10.1109/TGRS.2017.2654516},
  pages     = {2799 -- 2810},
  year      = {2017},
  abstract  = {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.},
  language  = {en}
}
@article{BrellRogassSegletal.2016,
  author    = {Brell, Maximilian and Rogass, Christian and Segl, Karl and Bookhagen, Bodo and Guanter, Luis},
  title     = {Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data},
  series = {IEEE transactions on geoscience and remote sensing},
  volume    = {54},
  journal   = {IEEE transactions on geoscience and remote sensing},
  publisher = {Inst. of Electr. and Electronics Engineers},
  address   = {Piscataway},
  issn      = {0196-2892},
  doi       = {10.1109/TGRS.2016.2518930},
  pages     = {3460 -- 3474},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@misc{HollsteinSeglGuanteretal.2016,
  author    = {Hollstein, Andr{\´e} and Segl, Karl and Guanter, Luis and Brell, Maximilian and Enesco, Marta},
  title     = {Ready-to-Use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images},
  series = {remote sensing},
  journal   = {remote sensing},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407938},
  pages     = {18},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@article{SeglGuanterKaufmannetal.2010,
  author    = {Segl, Karl and Guanter, Luis and Kaufmann, Hermann and Schubert, Josef and Kaiser, Stefan and Sang, Bernhard and Hofer, Stefan},
  title     = {Simulation of spatial sensor characteristics in the context of the EnMAP Hyperspectral mission},
  issn      = {0196-2892},
  doi       = {10.1109/Tgrs.2010.2042455},
  year      = {2010},
  abstract  = {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.},
  language  = {en}
}