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 r2 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 r2 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.
Highlights
An understanding of surface reflectance over the solar reflective domain is important in order to monitor the land surface with spaceborne passive optical sensors
Many fields of the Barrax region have relatively low leaf area index (LAI) in range from 0.5 to 1.0, meaning the soil reflectance will have a strong influence on the overall reflectance signal and that incorrect soil description can be a source of uncertainties (Table 1)
We propose a way of using the Earth Observation-Land Data Assimilation System (EO-LDAS) data assimilation system to reproduce hyperspectral information with multisensor and multi-angular constraints, using a restricted number of known biophysical parameters derived from by multi-spectral sensors
Summary
2500 nm) is important in order to monitor the land surface with spaceborne passive optical sensors. These sensors acquire data in a limited set of bands (e.g. multispectral sensors, such as Landsat (6 bands), Moderate Resolution Imaging Spectroradiometer (MODIS) (7 bands) or Sentinel-2/MSI (12 bands). We note that a number of space missions are expected to be launched in the few years with the remit of acquiring hyperspectral data Among these missions are the Environmental Mapping and Analysis Program (EnMAP), Hyperspectral PRecursor of the Application Mission (PRISMA) and Hyperspectral Infrared Imager (HyspIRI) (Guanter et al, 2015; Candela et al, 2016; Lee et al, 2015)
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