Simulating arbitrary hyperspectral bandsets from multispectral observations via a generic Earth Observation-Land Data Assimilation System (EO-LDAS). Issue 7 (1st October 2018)

• Record Type: Journal Article
• Authors: Chernetskiy, M.; Gobron, N.; Gómez-Dans, J.; Morgan, O.; Disney, M.; Lewis, P.; Schmullius, C.
• Is Part Of: Advances in space research. Volume 62:Issue 7(2018)
• Page Start: 1654
• Page End: 1674
• Publication Date: 2018-10-01
• Subjects: MISR -- Landsat -- CHRIS/PROBA -- EO-LDAS -- Semi-discrete radiative transfer model -- Barrax; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2018.07.015
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: This paper presents results of using multi-sensor and multi-angular constraints in the generic Earth Observation-Land Data Assimilation System (EO-LDAS) for reproducing arbitrary bandsets of hyperspectral reflectance at the top-of-canopy (TOC) level by merging observations from multispectral sensors with different spectral characteristics. This is demonstrated by combining Multi-angle Imaging Spectroradiometer (MISR) and Landsat Enhanced Thematic Mapper Plus (ETM+) data to simulate the Compact High Resolution Imaging Spectrometer CHRIS/PROBA hyperspectral signal over an agricultural test site, in Barrax, Spain. However, the method can be more generally applied to any combination of spectral data, providing a tool for merging EO data to any arbitrary hyperspectral bandset. Comparisons are presented using both synthetic and observed MISR and Landsat data, and retrieving surface biophysical properties. We find that when using simulated MISR and Landsat data, the CHRIS/PROBA hyperspectral signal is reproduced with RMSE 0.0001–0.04. LAI is retrieved with r 2 from 0.97 to 0.99 and RMSE of from 0.21 to 0.38. The results based on observed MISR and Landsat data have lower performances, with RMSE for the reproduced CHRIS/PROBA hyperspectral signal varying from 0.007 to 0.2. LAI is retrievedwith r 2 from 0.7 to 0.9 and RMSE from 0.7 to 1.4. We found that for the data considered here the main spectral variations in the visible and near infrared regions can be described by a limited number of parameters (3–4) that can be estimated from multispectral information. Results show that the method can be used to simulate arbitrary bandsets, which will be of importance to any application which requires combining new and existing streams of new EO data in the optical domain, particularly intercalibration of EO satellites in order to get continuous time series of surface reflectance, across programmes and sensors of different designs.