Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX)

Schaepman, Michael E.; Jehle, Michael; Hueni, Andreas; D’Odorico, Petra; Damm, Alexander; Weyermann, Jürg; Schneider, Fabian; Laurent, Valérie; Popp, Christoph; Seidel, F. C.; Lenhard, Karim; Gege, Peter; Küchler, Christoph; Brazile, Jason; Köhler, Peter; Vos, Lieve De; Meuleman, Koen; Meynart, Roland; Schläpfer, Daniel; Kneubühler, Mathias; Itten, K.I.

doi:10.1016/j.rse.2014.11.014

Cited by 172 publications

(127 citation statements)

References 75 publications

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“…In the field of airborne hyperspectral remote sensing, the FP7 project EUropean Facility for Airborne Research (EUFAR) developed a set of harmonized QIs for Level 1 (calibrated at-sensor radiance) and Level 2 (orthorectified ground reflectance) data [4], which also included the development of a full error propagation concept [5]. Extending the EUFAR developments, an exhaustive uncertainty analysis was carried out for the Airborne Prism Experiment (APEX) sensor [6,7], and, on a smaller scale, for parts of the DLR hyperspectral pre-processing chain [8].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

et al. 2015

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As part of the EnMAP preparation activities this study aims at estimating the uncertainty in the EnMAP L2A ground reflectance product using the simulated scene of Barrax, Spain. This dataset is generated using the EnMAP End-to-End Simulation tool, providing a realistic scene for a well-known test area. Focus is set on the influence of the expected radiometric calibration stability and the spectral calibration stability. Using a Monte-Carlo approach for uncertainty analysis, a larger number of realisations for the radiometric and spectral calibration are generated. Next, the ATCOR atmospheric correction is conducted for the test scene for each realisation. The subsequent analysis of the generated ground reflectance products is carried out independently for the radiometric and the spectral case. Findings are that the uncertainty in the L2A product is wavelength-dependent, and, due to the coupling with the estimation of atmospheric parameters, also spatially variable over the scene. To further illustrate the impact on subsequent data analysis, the influence on two vegetation indices is briefly analysed. Results show that the radiometric and spectral stability both have a high impact on the uncertainty of the narrow-band Photochemical Reflectance Index (PRI), and also the broad-band Normalized Difference Vegetation Index (NDVI) is affected.

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Section: Introductionmentioning

confidence: 99%

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

et al. 2015

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“…For this purpose, column water vapor value equals 2.0 g cm −2 are used and the sensor altitude is approximately 5 km above sea level resulting in pixel size of 4 m. To obtain a realistic range of AOD for the forward modeling, the method of [25] was applied on three real APEX sensor [45] radiance cubes of the Coast of Belgium. The reason for using the coastal area is its diversity, as the scene is covered by both sea and land.…”

Section: Profile Of the Condition Parameters For The Usual Scatteringmentioning

confidence: 99%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

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Abstract:A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (L rs,t (λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (ρ t,pre ) where effects associated with L rs,t (λ) are less influential. The method identifies pixels comprising pure materials from ρ t,pre . AOD values at the pure pixels are iteratively estimated using l 2 -norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06-0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5-10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

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“…In contrast, imaging spectroscopy is a well-established, continuously advancing technology capable of monitoring terrestrial plant functional biodiversity in a way that is vastly richer and more sensitive than other remote sensing techniques 22,27,28 . It captures environmental information at extremely fine spectral resolution by simultaneously mapping the reflectance and emission of light from the Earth's surface in hundreds of narrow spectral bands, producing essentially continuous spectra from the visible to infrared wavelengths 29 .…”

Section: The World's Ecosystems Are Losing Biodiversity Fast a Satelmentioning

confidence: 99%

“…Data on plant productivity, phenology, land-cover and other environmental parameters from MODIS and Landsat satellites currently serve as reasonably effective covariates for spatiotemporal biodiversity models based on in situ data 12,20,26 . However, the coarse spectral resolution of current satellite-borne sensors has so far prevented a more direct capture of biodiversity, and correlative models are limited by the above-mentioned data gaps.In contrast, imaging spectroscopy is a well-established, continuously advancing technology capable of monitoring terrestrial plant functional biodiversity in a way that is vastly richer and more sensitive than other remote sensing techniques 22,27,28 . It captures environmental information at extremely fine spectral resolution by simultaneously mapping the reflectance and emission of light from the Earth's surface in hundreds of narrow spectral bands, producing essentially continuous spectra from the visible to infrared wavelengths 29 .…”

mentioning

confidence: 99%

Erratum: Monitoring plant functional diversity from space

Jetz¹,

Cavender‐Bares²,

Pavlick³

et al. 2016

Nature Plants

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The world's ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change and how to manage it.The ability to view Earths' vegetation from space is a hallmark of the space age. Yet decades of satellite measurements have provided relatively little insight into the immense diversity of form and function in the plant kingdom in space and time. Humans are rapidly impacting biodiversity around the globe 1,2 , leading to the loss of ecosystem function 3 , and the goods and services they provide 4,5 . Recognizing the gravity of this threat, the international community has committed to urgent action to halt biodiversity loss [6][7][8][9] .Ecosystem processes [10][11][12] are often directly linked to the functional biodiversity of plants, that is, to a wide range of plant chemical, physiological and structural properties, connected to the uptake, use and allocation of resources. The functional biodiversity of plants varies in space and time and across scales of biological organization. Capturing and understanding this variation is vitally important for tracking the status and resilience of Earth's ecosystems, and for predicting how our ecological life support systems will function in the future.We currently lack consistent, repeated, high-resolution global-scale data on the functional biodiversity of the Earth's vegetation 2,10-12 . However, the technological tools, informatics infrastructure, theoretical basis, and analytical capability now exist to produce this essential data. Here we suggest that this capability is utilized in a satellite mission supporting a Global Biodiversity Observatory, that tracks temporal changes in plant functional traits across the globe to fill critical knowledge gaps, aid in the assessment of global environmental change, and improve predictions of future change. The continuous, global coverage in space and time such a mission would provide has the potential to transform basic and applied science on diversity and function, and to pave the way to a more mechanistically detailed representation of the terrestrial biosphere in Earth system models.The data and knowledge gap Plant functional biodiversity encompasses the wide-ranging variation in the chemical physiological and morphological properties of plants, such as the concentration of metabolites and nonstructural carbohydrates in leaves and the ratio of leaf mass to leaf area. These attributes are related functionally to the uptake, allocation and use of resources, such as carbon and nutrients, within the plant, and to defense against pests and environmental stresses.Functional properties vary within and among individuals (for instance as determined by the position of a leaf on a plant, or a tree in a forest), populations, species and communities, and may be measured at any of these levels of biological organization. With increasing spatial scale (and thus decreasing spatial resolution of measurem...

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Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX)

Cited by 172 publications

References 75 publications

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

Estimating the Influence of Spectral and Radiometric Calibration Uncertainties on EnMAP Data Products—Examples for Ground Reflectance Retrieval and Vegetation Indices

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Erratum: Monitoring plant functional diversity from space

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