Dataset of top-down nitrogen oxides fire emission estimation in northeastern Asia.

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.

Investigating the impacts of satellite fire observation accuracy on the top-down nitrogen oxides emission estimation in northeastern Asia.

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.

Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades--an overview.

Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.