Cristin-resultat-ID: 1940863
Sist endret: 29. september 2021, 19:13
Resultat
Vitenskapelig foredrag
2021

Retrieving fractional snow-covered area from optical satellites using data assimilation

Bidragsytere:
  • Kristoffer Aalstad
  • Sebastian Westermann
  • Norbert Pirk
  • Joel Fiddes og
  • Laurent Bertino

Presentasjon

Navn på arrangementet: SIOS Online conference on remote sensing in Svalbard
Sted: Online
Dato fra: 8. juni 2021
Dato til: 10. juni 2021

Arrangør:

Arrangørnavn: Svalbard Integrated Arctic Earth Observing System (SIOS)

Om resultatet

Vitenskapelig foredrag
Publiseringsår: 2021

Klassifisering

Vitenskapsdisipliner

Naturgeografi

Emneord

Permafrost • Snø • Fjernanalyse

Beskrivelse Beskrivelse

Tittel

Retrieving fractional snow-covered area from optical satellites using data assimilation

Sammendrag

Mapping from noisy observations to the latent states that may have generated them falls under the umbrella of inverse problems. These problems are abundant in Earth system science since our uncertain mechanistic models need to be fully specified while the system is only partially and imperfectly observed. Combined with a steadily growing observing system, this abundance has fueled the development of probabilistic Data Assimilation (DA) schemes that use Bayesian inference to fuse uncertain information from models and observations. Widely used applications of DA include the production of global atmospheric reanalyses and initializing numerical weather predictions. At the same time, the added value that DA can bring to remote sensing as a generalized framework for building retrieval algorithms remains largely untapped. In our contribution, we demonstrate the potential of data assimilation in the task of retrieving fractional snow-covered area (fSCA) from multispectral satellite imagery from moderate (MODIS) and higher (Sentinel-2 MSI, Landsat 8 OLI) optical sensors. In this analysis, we build on our previous work by focusing on the Bayelva catchment near Ny-Ålesund we have access to independent high-quality validation data obtained from terrestrial photography. We show how the general problem of linear spectral unmixing that is widely used for land cover classification can be recast as a Bayesian inverse problem. This can then be readily solved using ensemble-based data assimilation schemes, where we test both vanilla and sophisticated flavors of the particle filter and the ensemble Kalman filter, as well as Markov chain Monte Carlo benchmarks. By solving the problem in a transformed parameter space, the physical constraints of spectral unmixing are satisfied while reducing the need for ad hocery. The Bayesian data assimilation fSCA retrieval approach lets us deal with ill-posedness, incorporate physical knowledge, and account for uncertainty in the observed reflectances. It performs favorably compared to widely used techniques for fSCA retrieval such as thresholding of the NDSI, regression on the NDSI, and classical (non-negative least squares) spectral unmixing. This method is also much more scalable than classical unmixing since iterations are pre-determined and can fully exploit vectorization. Furthermore, it does not require any tuning on in-situ observations and it can also be used to solve the endmember selection problem using the concept of model evidence. Crucially, the retrieved fSCA includes dynamic uncertainty estimates that are required for satellite retrievals to be of any use in dynamic data assimilation frameworks. We envisage further validation by leveraging the network of terrestrial cameras operated by our partners in the PASSES consortium (Salzano et al., 2021; SESS Report 2021, Ch. 10). Our aim is to exploit these satellite retrievals in our ongoing efforts to produce tailored high resolution permafrost and snow reanalyses in cold regions, including Svalbard. At the same time, the approach outlined here could also be modified to retrieve surface albedo and (sub-pixel) land cover globally with even broader implications to Earth system science.

Bidragsytere

Kristoffer Aalstad

  • Tilknyttet:
    Forfatter
    ved Seksjon for naturgeografi og hydrologi ved Universitetet i Oslo

Sebastian Westermann

  • Tilknyttet:
    Forfatter
    ved Seksjon for naturgeografi og hydrologi ved Universitetet i Oslo

Norbert Pirk

  • Tilknyttet:
    Forfatter
    ved Seksjon for naturgeografi og hydrologi ved Universitetet i Oslo

Joel Fiddes

  • Tilknyttet:
    Forfatter

Laurent Bertino

  • Tilknyttet:
    Forfatter
    ved Nansen Senter for Miljø og Fjernmåling
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