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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 Accurate estimation of evapotranspiration (ET) can be affected by cloud change, while cloud‐induced bias errors in ET change remain poorly understood. Here we evaluated seven ET data sets from satellite microwave and optical models, atmospheric reanalysis and land surface models over East Asia. Results showed that although the data sets estimated ET changes overall well under mild cloud increase, all of them had a systematic overestimation (by 73.8%–159.8%) and deteriorated capability of capturing ET change under heavy cloud increase, especially over dense vegetation with large cloud cover. Overestimated incident solar radiation under clouds contributed to the deteriorated performances of ET. Cloud‐induced change in radiation and its errors played larger role in summer ET change over densely vegetated lands, while those in air temperature and relative humidity were dominant jointly over grass and barren lands. These results can serve as a reference for improving ET models and their forcing inputs under clouds.

Fire emission is a major source of atmospheric nitrogen oxides (NOx = NO2 + NO), accounting for a large part of global NOx emission, which profoundly changes atmosphere physicochemical property and impacts human society. An effective evaluation of these impacts relies on accurate NOx fire emission estimation. In this article, we developed a full top-down NOx fire emission dataset for northeastern Asia based on the satellite-derived emission coefficient (EC) and fire radiative power (FRP) density. In the dataset, daily NOx fire emissions during 2012–2019 were estimated at 1°x1° resolution across northeastern Asia, which can be used as fundamental input data in driving climate and weather models, and can be applied to investigate the characteristic of fire emission, fire-climate interaction, air pollution and human health effect. As a full top-down emission dataset, it can also serve as a reference for other existing emission inventories that are mostly based on bottom-up approaches.

NOx fire emissions greatly affect atmosphere and human society. The top-down NOx 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 NO2 concentration to derive NOx EC. We compared EC between MODIS and VIIRS, and defined a synergetic effect index (SEI) to explore the combined effects on NOx fire emission estimation due to potentially different ECs and FRP between the two sensors. Finally, we applied EC to estimate NOx emission and made comparison between MODIS and VIIRS. Results show that: 1) both sensors derived considerably higher NOx 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 NOx 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 NOx 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 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.

Isoprene is one of the most important biogenic volatile organic compounds (BVOCs) emitted by vegetation. The biogenic isoprene emissions are widely estimated by the Model of Emission of Gases and Aerosols from Nature (MEGAN) considering different environmental stresses. The response of isoprene emission to the water stress is usually parameterized using soil moisture in previous studies. In this study, we designed a new parameterization scheme of water stress in MEGAN as a function of a novel, satellite, passive microwave-based vegetation index, Emissivity Difference Vegetation Index (EDVI), which indicates the vegetation inner water content. The isoprene emission rates in southeastern China were simulated with different water stress indicators including soil moisture, EDVI, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Then the simulated isoprene emission rates were compared to associated satellite top-down estimations. The results showed that in southeastern China, the spatiotemporal correlations between those simulations and top-down retrieval are all high with different biases. The simulated isoprene emission rates with EDVI-based water stress factor are most consistent with top-down estimation with higher temporal correlation, lower bias and lower RMSE, while soil moisture alters the emission rates little, and optical vegetation indices (NDVI and EVI) slightly increase the correlation with top-down. The temporal correlation coefficients are increased after applied with EDVI water stress factor in most areas; especially in the Yunnan-Guizhou Plateau and Yangtze River Delta (>0.12). Overall, higher consistency of simulation and top-down estimation is shown when EDVI is applied, which indicates the possibility of estimating the effect of vegetation water stress on biogenic isoprene emission using microwave observations.

Abstract In this study, we used data from multiple sensors onboard NASA Aqua satellite to conduct a 10‐year (2002–2011) remote sensing of microwave emissivity difference vegetation index (EDVI) over China. We investigated the spatial and temporal variations of EDVI in tropical and subtropical evergreen forest, deciduous forest, rice and wheat farmlands, grassland, and montane vegetation regions. The average of China's EDVI is positive in dense vegetation regions and negative in sparse vegetation regions, depending on the proportion of bare soil and open water. In all selected studying regions, the seasonal variation of EDVI follows the trend of vegetation phenology, even in regions with large proportion of open water. EDVI is positively correlated to the greenness of vegetation (normalized difference vegetation index [NDVI]) with certain phase difference in their seasonal cycle. In autumn, EDVI begins to decline earlier and faster than NDVI. In tropical rainforest, EDVI also starts to increase earlier than NDVI in spring. The large‐scale spatial distribution of EDVI under clear sky and cloudy sky is similar. In montane vegetation regions, EDVI under heavy clouds (90% fraction) conditions is significantly greater than that under clear sky (10% fraction), indicating a possible cloud induced enhancement of vegetation water content. In forests and croplands in the plains, such effect is not remarkable.

Microwave land surface emissivity (MLSE) is an important geophysical parameter to determine the microwave radiative transfer over land and has broad applications in satellite remote sensing of atmospheric parameters (e.g., precipitation, cloud properties), land surface parameters (e.g., soil moisture, vegetation properties), and the parameters of interactions between atmosphere and terrestrial ecosystem (e.g., evapotranspiration rate, gross primary production rate). In this study, MLSE in China under both clear and cloudy sky conditions was retrieved using satellite passive microwave measurements from Aqua Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), combined with visible/infrared observations from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) atmosphere reanalysis dataset of ERA-20C. Attenuations from atmospheric oxygen and water vapor, as well as the emissions and scatterings from cloud particles are taken into account using a microwave radiation transfer model to do atmosphere corrections. All cloud parameters needed are derived from MODIS visible and infrared instantaneous measurements. Ancillary surface skin temperature as well as atmospheric temperature-humidity profiles are collected from ECMWF reanalysis data. Quality control and sensitivity analyses were conducted for the input variables of surface skin temperature, air temperature, and atmospheric humidity. The ground-based validations show acceptable biases of primary input parameters (skin temperature, 2 m air temperature, near surface relative humidity, rain flag) for retrieving using. The subsequent sensitivity tests suggest that 10 K bias of skin temperature or observed brightness temperature may result in a 4% (~0.04) or 7% (0.07) retrieving error in MLSE at 23.5 GHz. A nonlinear sensitivity in the same magnitude is found for air temperature perturbation, while the sensitivity is less than 1% for 300 g/m2 error in cloud water path. Results show that our algorithm can successfully retrieve MLSE over 90% of the satellite detected land surface area in a typical cloudy day (cloud fraction of 64%), which is considerably higher than that of the 29% area by the clear-sky only algorithms. The spatial distribution of MLSE in China is highly dependent on the land surface types and topography. The retrieved MLSE is assessed by compared with other existing clear-sky AMSR-E emissivity products and the vegetation optical depth (VOD) product. Overall, high consistencies are shown for the MLSE retrieved in this study with other AMSR-E emissivity products across China though noticeable discrepancies are observed in Tibetan Plateau and Qinling-Taihang Mountains due to different sources of input skin temperature. In addition, the retrieved MLSE exhibits strong positive correlations in spatial patterns with microwave vegetation optical depth reported in the literature.

Fire omission and commission errors, and the accuracy of fire radiative power (FRP) from satellite moderate-resolution impede the studies on fire regimes and FRP-based fire emissions estimation. In this study, we compared the accuracy between the extensively used 1-km fire product of MYD14 from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375-m fire product of VNP14IMG from the Visible Infrared Imaging Radiometer Suite (VIIRS) in Northeastern Asia using data from 2012–2017. We extracted almost simultaneous observation of fire detection and FRP from MODIS-VIIRS overlapping orbits from the two fire products, and identified and removed duplicate fire detections and corresponding FRP in each fire product. We then compared the performance of the two products between forests and low-biomass lands (croplands, grasslands, and herbaceous vegetation). Among fire pixels detected by VIIRS, 65% and 83% were missed by MODIS in forests and low-biomass lands, respectively; whereas associated omission rates by VIIRS for MODIS fire pixels were 35% and 53%, respectively. Commission errors of the two fire products, based on the annual mean measurements of burned area by Landsat, decreased with increasing FRP per fire pixel, and were higher in low-biomass lands than those in forests. Monthly total FRP from MODIS was considerably lower than that from VIIRS due to more fire omission by MODIS, particularly in low-biomass lands. However, for fires concurrently detected by both sensors, total FRP was lower with VIIRS than with MODIS. This study contributes to a better understanding of fire detection and FRP retrieval performance between MODIS and its successor VIIRS, providing valuable information for using those data in the study of fire regimes and FRP-based fire emission estimation.