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Microwaves have the advantage of penetrating vegetation and exhibit sensitivity to properties such as vegetation water content (VWC); yet, their potential utility in the fire domain is infrequently investigated. This study elucidates the different impacts of the microwave VWC index EDVI on fire radiative energy (FRE) across various biome types and the significant predictive power for high-severity fires (defined based on FRE) in mainland Southeast Asia. While EDVI exhibits lower predictive power for high severe fires compared to the commonly used fire weather indices (e.g., FWI), an enhancement is observed when these predictors are used in combination. Either by employing EDVI or fire weather indices, the predictability of fires is found to be highest over forests and lowest over croplands. Factors such as increasing human influence and fuel limitation in croplands are likely reducing the roles of VWC and weather on fires, contributing to the lower prediction skill of EDVI and fire weather. These results indicate the usefulness of microwave VWC index in fire studies. Although fire weather presents more considerable impacts on fires, the microwave VWC index seem to still provide some complementary information in fire danger assessment. [ABSTRACT FROM AUTHOR]

• A small bias in EDVI-ET is found over eight river basins using water balance method. • EDVI-ET performs a larger magnitude and higher accuracy than MOD16 over crop lands. • Cloud-induced changes in ET vary from 10% to 40% compared to clear sky conditions. • Cloud impacts are more significant for forests, savannas and crops than barrens. • EDVI-ET is most sensitive to radiation for forests, but to humidity for arid barrens. Evapotranspiration (ET) is an important component of the hydrological cycle and energy balance in a land-atmosphere system. Satellite remote sensing has been widely used to estimate regional and global ET, but most previous methods depend on optical measurements that are limited to cloud-free conditions. This makes ET estimation challenging under cloudy sky. Currently, evaluations of satellite ET estimation under various cloud conditions remain lacking at the regional scale. Owing to the ability to penetrate clouds, satellite passive microwave measurements are powerful tools for retrieving ET under clouds. This study evaluated a satellite microwave-based daily ET method under all sky conditions over the part of China between 18°N and 50°N from 2003 to 2010, using microwave emissivity difference vegetation index (EDVI) as the proxy of vegetation water content (VWC). Validations using the surface water balance method found that the estimated ET (EDVI-ET) had an overall small bias (6.18%) in eight river basins. EDVI-ET displayed consistent spatiotemporal patterns with global MOD16 ET, with high spatial correlation (R >0.71) and monthly temporal correlation (R >0.82) throughout four seasons. Their differences were also small (<0.56mmday−1) in forests, savannas, grass/shrubs, and croplands. Furthermore, cloud impacts on the regional ET were found to be significant and spatiotemporally heterogeneous. Both EDVI-ET and in-situ observations at seven flux towers indicated that cloud-induced reduction in daily ET could exceed 30% when the cloud cover increased by 60% (R2 of 0.42, fitting line slope of 0.80, p <0.001). Under increased cloud conditions in summer, the changes in net radiation dominated the ET over dense vegetation in southern China, while the roles of air temperature and humidity increased over water-stressed barrens and short vegetation in northwest China. VWC affected EDVI-ET under clouds in temperate transitional zones from flatlands to highlands. This study highlighted the importance of cloud impacts in satellite ET estimation. [ABSTRACT FROM AUTHOR]

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 (GPP obs) than other optic-based FPAR schemes. This capability of nEDVI was more noticeable under moderate and heavy cloud cover (Frc) conditions. Validations against daily GPP obs at all sites showed that EDVI-based GPP (GPP EDVI) 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 GPP EDVI 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 GPP obs from clear sky to cloudy sky (R2 of 0.93) with stable accuracies. Furthermore, the accuracy of daily GPP EDVI was found to be comparable with global satellite optic MOD17 GPP (GPP MOD17) and EC-LUE GPP (GPP ECLUE) from 8-day to yearly scales across the sites. In particular, GPP EDVI performed generally smaller bias at evergreen broadleaf forests, while both of GPP MOD17 and GPP ECLUE 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, GPP EDVI generated lower bias (−29.8%) than GPP MOD17 (−66.8%) and GPP ECLUE (−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. • A LUE model coupled with microwave vegetation index (EDVI) for daily GPP estimation. • FPAR and evaporative fraction were quantified by EDVI-based schemes. • 8-year in-situ GPP from seven eddy covariance flux sites of ChinaFLUX were used. • Model performed daily GPP estimation stably under various cloud cover conditions. • Model had a better accuracy than MOD17 and EC-LUE GPP at forests and crop sites. [ABSTRACT FROM AUTHOR]