Remote sensing of crop residue burning in Punjab (India): a study on burned area estimation using multi-sensor approach

2009 ◽  
Vol 24 (4) ◽  
pp. 273-292 ◽  
Author(s):  
Gurdeep Singh ◽  
Yogesh Kant ◽  
V. K. Dadhwal
Keyword(s):  
Remote Sensing ◽  
Crop Residue ◽  
Burned Area ◽  
Area Estimation ◽  
Crop Residue Burning

scholarly journals New eastern China agricultural burning fire emission inventory and trends analysis from combined geostationary (Himawari-8) and polar-orbiting (VIIRS-IM) fire radiative power products

2020 ◽  
Author(s):  
Tianran Zhang ◽  
Mark C. de Jong ◽  
Martin J. Wooster ◽  
Weidong Xu ◽  
Lili Wang
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Crop Residue ◽  
Eastern China ◽  
Post Harvest ◽  
Crop Residue Burning ◽  
Radiative Power ◽  
Agricultural Burning ◽  
Emissions Inventories ◽  
Fire Radiative Power

Abstract. Open burning of agricultural crop residues is widespread across eastern China, and during certain post-harvest periods this activity is believed to significantly influence air quality. However, the exact contribution of crop residue burning to major air quality exceedances and air quality episodes has proven difficult to quantify. Whilst highly successful in many regions, in areas dominated by agricultural burning MODIS-based fire emissions inventories such as GFAS and GFED are suspected of significantly underestimating the magnitude of biomass burning emissions due to the typically very small, but highly numerous, fires involved that are quite easily missed by coarser spatial resolution remote sensing observations. To address this issue, we here use twice daily fire radiative power (FRP) observations from the ‘small fire optimised’ VIIRS-IM FRP product, and combine it with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. Using this we generate a unique high spatio-temporal resolution agricultural burning inventory for eastern China for the years 2012–2015, designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO2, CO, PM2.5 and black carbon. We calculate DMB totals 100 to 400 % higher than reported by GFAS and GFED4.1s, and quantify interesting spatial and temporal patterns previously un-noted. Wheat residue burning, primarily occurring in May–June, is responsible for more than half of the annual crop residue burning emissions of all species, whilst a secondary peak in autumn (Sept–Oct) is associated with rice and corn residue burning. We further identify a new winter (Nov–Dec) burning season, hypothesised to be caused by delays in burning driven by the stronger implementation of residue burning bans during the autumn post-harvest season. Whilst our emissions estimates are far higher than those of other satellite-based emissions inventories for the region, they are lower than estimates made using traditional ‘crop yield-based approaches’ (CYBA) by a factor of between 2 and 5 x. We believe that this is at least in part caused by outdated and overly high burning ratios being used in the CYBA approach, leading to the overestimation of DMB. Therefore we conclude that that satellite remote sensing approaches which adequately detect the presence of agricultural fires are a far better approach to agricultural fire emission estimation.

scholarly journals Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products

2020 ◽  
Vol 20 (17) ◽  
pp. 10687-10705
Author(s):  
Tianran Zhang ◽  
Mark C. de Jong ◽  
Martin J. Wooster ◽  
Weidong Xu ◽  
Lili Wang
Keyword(s):  
Remote Sensing ◽  
Air Quality ◽  
Crop Residue ◽  
Eastern China ◽  
Fire Emissions ◽  
Crop Residue Burning ◽  
Radiative Power ◽  
Agricultural Burning ◽  
Emissions Inventories ◽  
Fire Radiative Power

Abstract. Open burning of agricultural crop residues is widespread across eastern China, and during certain post-harvest periods this activity is believed to significantly influence air quality. However, the exact contribution of crop residue burning to major air quality exceedances and air quality episodes has proven difficult to quantify. Whilst highly successful in many regions, in areas dominated by agricultural burning, MODIS-based (MODIS: Moderate Resolution Imaging Spectroradiometer) fire emissions inventories such as the Global Fire Assimilation System (GFAS) and Global Fire Emissions Database (GFED) are suspected of significantly underestimating the magnitude of biomass burning emissions due to the typically very small, but highly numerous, fires involved that are quite easily missed by coarser-spatial-resolution remote sensing observations. To address this issue, we use twice-daily fire radiative power (FRP) observations from the “small-fire-optimised” VIIRS-IM FRP product and combine them with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. Using this we generate a unique high-spatio-temporal-resolution agricultural burning inventory for eastern China for the years 2012–2015, designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO2, CO, PM2.5, and black carbon. We calculate DMB totals 100 % to 400 % higher than reported by the GFAS and GFED4.1s, and we quantify interesting spatial and temporal patterns previously un-noted. Wheat residue burning, primarily occurring in May–June, is responsible for more than half of the annual crop residue burning emissions of all species, whilst a secondary peak in autumn (September–October) is associated with rice and corn residue burning. We further identify a new winter (November–December) burning season, hypothesised to be caused by delays in burning driven by the stronger implementation of residue burning bans during the autumn post-harvest season. Whilst our emissions estimates are far higher than those of other satellite-based emissions inventories for the region, they are lower than estimates made using traditional “crop-yield-based approaches” (CYBAs) by a factor of between 2 and 5. We believe that this is at least in part caused by outdated and overly high burning ratios being used in the CYBA, leading to the overestimation of DMB. Therefore, we conclude that satellite remote sensing approaches which adequately detect the presence of agricultural fires are a far better approach to agricultural fire emission estimation.

Estimation of emissions from crop residue burning using remote sensing data

2019 ◽  
Vol 19 (1/2) ◽  
pp. 94
Author(s):  
Kubra Bahsi ◽  
Betul Salli ◽  
Doğushan Kılıç ◽  
Elif Sertel
Keyword(s):  
Remote Sensing ◽  
Crop Residue ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Crop Residue Burning

scholarly journals Estimation of Field-Level NOx Emissions from Crop Residue Burning Using Remote Sensing Data: A Case Study in Hubei, China

2021 ◽  
Vol 13 (3) ◽  
pp. 404
Author(s):  
Yonglin Shen ◽  
Changmin Jiang ◽  
Ka Lok Chan ◽  
Chuli Hu ◽  
Ling Yao
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Crop Residue ◽  
Emission Inventory ◽  
Remote Sensing Data ◽  
Satellite Observations ◽  
Nox Emission ◽  
Nox Emissions ◽  
Sensing Data ◽  
Crop Residue Burning

Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate emission inventory for crop residue burning. In this study, we proposed a remote sensing-based method to estimate nitrogen oxide (NOx) emissions related to crop residue burning at the field level over Hubei, China. The new method considers differences in emission factors and the spatial distribution for different crop types. Fire radiative power (FRP) derived from moderate-resolution imaging spectroradiometer (MODIS) was used to quantify NOx emissions related to agricultural biomass combustion. The spatial distribution of different crops classified by multisource remote sensing data was used as an a priori constraint. We derived a new NOx emission database for Hubei from 2014 to 2016 with spatial resolution of 1 × 1 km. Significant seasonal patterns were observed from the NOx emission database. Peak NOx emission occurring in October was related to the residue burning in late autumn harvesting. Another peak was observed between January and April, which was due to the frequent burning of stubble before spring sowing. Our results were validated by comparing our emission inventory with geostationary satellite observations, previous studies, global fire emission database (GFED), NO2 vertical column densities (VCDs) from ozone monitoring instrument (OMI) satellite observations, and measurements from environmental monitoring stations. The comparisons showed NOx emission from GFED database was 47% lower than ours, while the evaluations from most of the statistical studies were significantly higher than our results. The discrepancies were likely related to the differences of methodology and data sources. The spatiotemporal variations of NOx emission in this study showed strong correlations with NO2 VCDs, which agreed well with geostationary satellite observations. A reasonable correlation between in situ NO2 observations and our results in agricultural regions demonstrated that our method is reliable. We believe that the new NOx emission database for crop residue burning derived in this study can potentially improve the understanding of pollution sources and can provide additional information for the design of pollution control measures.

scholarly journals Spatiotemporal Variations and Uncertainty in Crop Residue Burning Emissions over North China Plain: Implication for Atmospheric CO2 Simulation

2021 ◽  
Vol 13 (19) ◽  
pp. 3880
Author(s):  
Yu Fu ◽  
Hao Gao ◽  
Hong Liao ◽  
Xiangjun Tian
Keyword(s):  
Air Quality ◽  
Biomass Burning ◽  
Crop Residue ◽  
North China Plain ◽  
North China ◽  
Transport Model ◽  
Spatial Uncertainty ◽  
Burned Area ◽  
Crop Residue Burning ◽  
The Impact

Large uncertainty exists in the estimations of greenhouse gases and aerosol emissions from crop residue burning, which could be a key source of uncertainty in quantifying the impact of agricultural fire on regional air quality. In this study, we investigated the crop residue burning emissions and their uncertainty in North China Plain (NCP) using three widely used methods, including statistical-based, burned area-based, and fire radiative power-based methods. The impacts of biomass burning emissions on atmospheric carbon dioxide (CO2) were also examined by using a global chemical transport model (GEOS-Chem) simulation. The crop residue burning emissions were found to be high in June and followed by October, which is the harvest times for the main crops in NCP. The estimates of CO2 emission from crop residue burning exhibits large interannual variation from 2003 to 2019, with rapid growth from 2003 to 2012 and a remarkable decrease from 2013 to 2019, indicating the effects of air quality control plans in recent years. Through Monte Carlo simulation, the uncertainty of each estimation was quantified, ranging from 20% to 70% for CO2 emissions at the regional level. Concerning spatial uncertainty, it was found that the crop residue burning emissions were highly uncertain in small agricultural fire areas with the maximum changes of up to 140%. While in the areas with large agricultural fire, i.e., southern parts of NCP, the coefficient of variation mostly ranged from 30% to 100% at the gridded level. The changes in biomass burning emissions may lead to a change of surface CO2 concentration during the harvest times in NCP by more than 1.0 ppmv. The results of this study highlighted the significance of quantifying the uncertainty of biomass burning emissions in a modeling study, as the variations of crop residue burning emissions could affect the emission-driven increases in CO2 and air pollutants during summertime pollution events by a substantial fraction in this region.

Estimation of emissions from crop residue burning using remote sensing data

2019 ◽  
Vol 19 (1/2) ◽  
pp. 94
Author(s):  
Doğushan Kılıç ◽  
Betul Salli ◽  
Kubra Bahsi ◽  
Elif Sertel
Keyword(s):  
Remote Sensing ◽  
Crop Residue ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Crop Residue Burning
Sign in / Sign up

Export Citation Format

Share Document