A Restricted Boltzman Neural Net to Infer Carbon Uptake from OCO-2 Satellite Data

Abstract:

For several decades, scientists have been using satellite observations to infer climate budgets of terrestrial carbon uptake employing inverse methods in conjunction with ecosystem models and coupled global climate models. This is an extremely important Big Data calculation today since the net annual photosynthetic carbon uptake changes annually over land and removes on average ~20% of the emissions from human contributions to atmospheric loading of CO2 from fossil fuels. Unfortunately, such calculations have large uncertainties validated with in-situ networks of measuring stations across the globe. One difficulty in using satellite data for these budget calculations is that the models need to assimilate surface fluxes of CO2 as well as soil moisture, vegatation cover and the eddy covariance of latent and sensible heat to calculate the carbon fixed in the soil while satellite spectral observations only provide near surface concentrations of CO2.

In July 2014, NASA successfully launched OCO-2 which provides 3km surface measurements of CO2 over land and oceans. We have collected nearly one year of Level 2 XCO2 data from the OCO-2 satellite for 3 sites of ~200 km² at equatorial, temperate and high latitudes. Each selected site was part of the Fluxnet or ARM system with tower stations for measuring and collecting CO2 fluxes on an hourly basis, in addition to eddy transports of the other parameters. We are also planning to acquire the 4km NDVI products from MODIS and registering the data to the 3km XCO2 footprints for the three sites. We have implemented a restricted Boltzman machine on the quantum annealing D-Wave computer, a novel deep learning neural net, to be used for training with station data to infer CO2 fluxes from collocated XCO2, MODIS vegetative land cover and MERRA reanalysis surface exchange products. We will present performance assessments of the D-Wave Boltzman machine for generating XCO2 fluxes from the OCO-2 satellite observations for the 3 sites by validating with monthly station flux data for one year as a potential assimilation input to the LIS model for obtaining the Net Ecosystem Exchange.