Daily estimation of gross primary production under all sky using a light use efficiency model coupled with satellite passive microwave measurements

Abstract

Although satellite-based light use efficiency (LUE) model is widely used to estimate gross primary production (GPP) of terrestrial ecosystems, microwave observations have not been integrated into LUE models. This study developed a new LUE model coupled with a passive microwave vegetation index (Emissivity Difference Vegetation Index, EDVI) for daily GPP estimation. Normalized EDVI (nEDVI), an indicator of canopy-scale leaf development and biomass change, was used as a proxy of fraction of photosynthetically active radiation (FPAR). EDVI-based evaporative fraction (EDVI-EF) under all sky was used to indicate synoptic-scale water stress on LUE. 8-year in-situ measurements from seven flux tower sites (four forests, two grasslands and one croplands) of ChinaFLUX network were used to evaluate the model. We found that nEDVI-based FPAR better captured the short-term variations in daily in-situ GPP (GPPobs) than other optic-based FPAR schemes. This capability of nEDVI was more noticeable under moderate and heavy cloud cover (Frc) conditions. Validations against daily GPPobs at all sites showed that EDVI-based GPP (GPPEDVI) generated an overall small bias of −0.47 gC m−2 day−1 (−8.1%) and good Taylor score (S) of 0.86 at the daily scale. Better accuracy of GPPEDVI was found at forests sites with R2 of 0.43 to 0.73, bias of-5.29% to 3.03% and S of 0.58 to 0.78, respectively. At a tropical forest site with most frequent cloud cover, the model also well captured the variation in GPPobs from clear sky to cloudy sky (R2 of 0.93) with stable accuracies. Furthermore, the accuracy of daily GPPEDVI was found to be comparable with global satellite optic MOD17 GPP (GPPMOD17) and EC-LUE GPP (GPPECLUE) from 8-day to yearly scales across the sites. In particular, GPPEDVI performed generally smaller bias at evergreen broadleaf forests, while both of GPPMOD17 and GPPECLUE were overestimated, suggesting that there could be less saturation for microwave-based LUE model over dense vegetation. Although all three satellite LUE models severely underestimated GPP of crop, GPPEDVI generated lower bias (−29.8%) than GPPMOD17 (−66.8%) and GPPECLUE (−59.5%). Overall, this study is the first attempt toward the integration of microwave-derived variables into LUE model for daily GPP estimation. The microwave-based LUE model has a potential of mapping spatiotemporally continues daily GPP under various clouds.