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To understand the relative importance of biogenic emission, biomass burning emission on volatile organic compounds in Amazon, the spatial and temporal correlations between atmospheric column‐integrated formaldehyde (HCHO) and fire count, vegetation hydrological states index, surface solar radiation flux, near‐surface air temperature are studied using synergized satellite products and reanalysis data. A recently developed microwave‐based vegetation index (emissivity difference vegetation index, EDVI) with high temporal resolution is used for the linkages between vegetation and HCHO at daily scale with and without fire contaminations. At large regional scale, EDVI shows highest spatial correlation with HCHO indicating the important controlling effect of biogenic emission. In given subregions with frequent fires, the temporal variations of monthly HCHO show much stronger correlations with fire count. The temporal correlations between monthly HCHO and EDVI are vague and even negative in some subregions. Radiation and temperature show stable positive temporal correlations with HCHO, particularly in areas with few fires. After excluding the samples contaminated by fires, the daily temporal correlation between vegetation (EDVI) and HCHO becomes significant and positive in most areas except the northern rainforest with weak temporal variations of EDVI. We proposed a bilinear model of biogenic‐emission‐induced HCHO (B‐HCHO) using radiation and EDVI as inputs. The bias between modeled long‐term mean B‐HCHO and satellite observation is less than 20%. And the daily time series of modeled B‐HCHO matches observations as well. It is the first time to provide satellite observational evidences of the relative importance of biogenic emission and biomass burning emission on HCHO, the proxy of atmospheric volatile organic compounds concentration.

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.