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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]

In the context of global warming, forest fires are expected to occur more frequently and intensively, and impose more significant impacts on human society, terrestrial ecosystems, and atmosphere. Most of the existing methods in monitoring large-scale forest fire are based either on satellite visible and infrared observations or weather-based indices. This work explored the advantages of a new satellite microwave-based vegetation index in monitoring forest fire occurrence and fire intensity in Northeastern Asia and Southern China. Specifically, we used satellite observations during 2002–2011 to investigate the correlation at different temporal scales between forest fire properties (fire count, FC; fire radiative power, FRP) and the vegetation water content proxy of the Microwave Emissivity Difference Vegetation Index (EDVI) derived from the Moderate Resolution Imaging Spectroradiometer and the Advanced Microwave Scanning Radiometer-EOS on Aqua satellite. The correlations were compared to that with three weather-based indices including the Fine Fuel Moisture Code, Initial Spread Index and Fire Weather Index (FWI) to determine whether EDVI provides new independent information of forest fires. Finally, EDVI and the weather-based indices FWI were combined to establish multivariate linear regression models to estimate FC and FRP. Results show that: 1) the temporal variations of FC and total FRP are negatively correlated with EDVI using the daily and monthly observations at 1° grid and regional scales; and overall opposite annual cycles and interannual variations between FC (and total FRP) and EDVI are observed in Northeastern Asia and Southern China; 2) compared to the weather-based indices, EDVI shows higher correlation with the temperate forest fire properties in Southern China while shows weaker correlation with the forest fire properties in Northeastern Asia; and a combination of the two kind of indices is found to improve the explained variance for fire properties in both regions; 3) multivariate linear regression models based on EDVI and FWI provide better estimation of FC and FRP compared to the linear regression models based on FWI alone. To our knowledge, this is the first work that comprehensively investigates the potential application of the microwave-based vegetation water content index in forest fire count and fire intensity. [ABSTRACT FROM AUTHOR]

NO x fire emissions greatly affect atmosphere and human society. The top-down NO x fire emission estimation is highly influenced by satellite fire observation performance (e.g., fire detection) by affecting the derivation of emission coefficient (EC) and fire radiative power (FRP) magnitude. However, such influence is lack of comprehensive study. Here, we developed an algorithm to evaluate such impacts in northeastern Asia using multi-source data during 2012–2019. Specifically, we extracted near-concurrent fire observations from MODIS and its successor VIIRS over their orbit-overlapping area and combined respectively with OMI NO 2 concentration to derive NO x EC. We compared EC between MODIS and VIIRS, and defined a synergetic effect index (SEI) to explore the combined effects on NO x fire emission estimation due to potentially different ECs and FRP between the two sensors. Finally, we applied EC to estimate NO x emission and made comparison between MODIS and VIIRS. Results show that: 1) both sensors derived considerably higher NO x EC for low-biomass vegetation fires (e.g., grassland fires) than other vegetation fires; however, MODIS EC is about 30% lower than VIIRS EC while similar values are derived for forest fires; 2) synergetic effects induced by different ECs and FRP magnitudes between the two sensors are more significant during fall and winter than in spring and summer; 3) annual NO x emissions based on MODIS EC are 15–23% lower than that from VIIRS EC during 2012–2019, while both are lower than the conventional bottom-up emission inventories GFED and FINN by an average of 23–44%; nevertheless, the EC-based NO x estimations presented high spatiotemporal correlation of R usually between 0.70 and 0.95 with GFED and FINN. These results reveal and quantify the critical impacts of satellite fire observation performance on EC derivation and fire emission estimation, which is helpful in reducing estimation uncertainty. [ABSTRACT FROM AUTHOR]