Earth system science related imaging spectroscopy—An assessment

Schaepman, Michael E.; Ustin, Susan L.; Plaza, Antonio; Painter, T. H.; Verrelst, Jochem; Liang, Shunlin

doi:10.1016/j.rse.2009.03.001

Cited by 309 publications

(171 citation statements)

References 275 publications

(208 reference statements)

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“…Moreover, it also permits us to acquire images in flexible dates, exploiting the same sunny illumination conditions and eliminating problems due to clouds, in such a way that the imagery of two different periods can be compared. In addition, the possibility to mount a hyperspectral camera on it has allowed us also to fully take advantage of all the potentials of remote sensing, processing hundreds of narrow contiguous spectral bands to which the vegetation is sensitive [4,[12][13][14][15][16]. Indeed, in general, quite homogeneous grassland vegetation cover has relatively small variations in reflectance properties of the canopy, which could be visible and analyzed only from a continuous reflectance spectrum.…”

Section: Discussionmentioning

confidence: 99%

“…The subsequent introduction of hyperspectral sensors has allowed researchers to improve the retrieval of grassland traits considerably [3][4][5]. Based on its narrow contiguous wavebands, hyperspectral sensors are more sensitive to vegetation variables since they provide a continuous reflectance spectrum of the vegetation target [4,[12][13][14][15][16]. Nevertheless, the hyperspectral images include hundreds of spectral bands, many of which are strongly correlated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

115

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Grassland ecosystems cover around 40% of the entire Earth's surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Rededge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Capolupo

Kooistra

Berendonk³

et al. 2015

IJGI

115

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“…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 . Distinctive features are imprinted in these spectra as light interacts with the chemical bonds and structural composition of plants.…”

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

confidence: 99%

Erratum: Monitoring plant functional diversity from space

Jetz¹,

Cavender‐Bares²,

Pavlick³

et al. 2016

Nature Plants

Self Cite

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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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“…Airborne imaging spectrometers are used in a wide range of studies related to earth system sciences [1,2]. The generation of well-calibrated data requires an accurate instrument model based on a careful system characterization and a regular system calibration to ensure the validity of calibration coefficients over time and an instrument-specific data calibration process based on the instrument model [3], converting digital numbers (DNs) to International System of Units (SI) traceable units and essentially making data comparable over space, time, and different sensor systems [4].…”

Section: Introductionmentioning

confidence: 99%

Impacts of dichroic prism coatings on radiometry of the airborne imaging spectrometer APEX

Hueni

Schlaepfer²,

Jehle

et al. 2014

Appl. Opt.

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The generation of well-calibrated radiometric measurements from imaging spectrometer data requires careful consideration of all influencing factors, as well as an instrument calibration based on a detailed sensor model. Deviations of ambient parameters (i.e., pressure, humidity, temperature) from standard laboratory conditions during airborne operations can lead to biases that should be accounted for and properly compensated by using dedicated instrument models. This study introduces a model for the airborne imaging spectrometer airborne prism experiment (APEX), describing the impact of spectral shifts as well as polarization effects on the radiometric system response due to changing ambient parameters. Key issues are related to changing properties of the dichroic coating applied to the dispersing elements within the optical path. We present a model based on discrete numerical simulations. With the improved modeling approach, we predict radiometric biases with an root mean square error (RMSE) below 1%, leading to a substantial improvement of radiometric stability and predictability of system behavior. The generation of well-calibrated radiometric measurements from imaging spectrometer data requires careful consideration of all influencing factors, as well as an instrument calibration based on a detailed sensor model. Deviations of ambient parameters (i.e., pressure, humidity, temperature) from standard laboratory conditions during airborne operations can lead to biases that should be accounted for and properly compensated by using dedicated instrument models. This study introduces a model for the airborne imaging spectrometer airborne prism experiment (APEX), describing the impact of spectral shifts as well as polarization effects on the radiometric system response due to changing ambient parameters. Key issues are related to changing properties of the dichroic coating applied to the dispersing elements within the optical path. We present a model based on discrete numerical simulations. With the improved modeling approach, we predict radiometric biases with an root mean square error (RMSE) below 1%, leading to a substantial improvement of radiometric stability and predictability of system behavior.

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Earth system science related imaging spectroscopy—An assessment

Cited by 309 publications

References 275 publications

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

Erratum: Monitoring plant functional diversity from space

Impacts of dichroic prism coatings on radiometry of the airborne imaging spectrometer APEX

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