  Data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite is essential
to many carbon management strategies. A retrieval algorithm is used to estimate
CO2 concentration using the radiance data measured by OCO-2. However, due to
factors such as cloud cover and cosmic rays, the spatial coverage of the
retrieval algorithm is limited in some areas of critical importance for carbon
cycle science. Mixed land/water pixels along the coastline are also not used in
the retrieval processing due to the lack of valid ancillary variables including
land fraction. We propose an approach to model spatial spectral data to solve
these two problems by radiance imputation and land fraction estimation. The
spectral observations are modeled as spatially indexed functional data with
footprint-specific parameters and are reduced to much lower dimensions by
functional principal component analysis. The principal component scores are
modeled as random fields to account for the spatial dependence, and the missing
spectral observations are imputed by kriging the principal component scores.
The proposed method is shown to impute spectral radiance with high accuracy for
observations over the Pacific Ocean. An unmixing approach based on this model
provides much more accurate land fraction estimates in our validation study
along Greece coastlines.
