Intercomparison of near-real-time biomass burning emissions estimates constrained by satellite fire data

被引：0

作者：

NASA Langley Research Center, Hampton, VA, United States ^{[1
]}

不详 ^{[2
]}

不详 ^{[3
]}

不详 ^{[4
]}

不详 ^{[5
]}

不详 ^{[6
]}

不详 ^{[7
]}

不详 ^{[8
]}

不详 ^{[9
]}

机构：

来源：

Journal of Applied Remote Sensing | 2008年 / 2卷 / 01期

关键词：

We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detections and instantaneous fire size estimates from geostationary GOES sensors. Each technique uses a different approach for estimating trace gas and particulate emissions from active fires. Here we evaluate monthly area burned and CO emission estimates for most of 2006 over the contiguous United States domain common to all four techniques. Two techniques provide global estimates and these are also compared. Overall we find consistency in temporal evolution and spatial patterns but differences in these monthly estimates can be as large as a factor of 10. One set of emission estimates is evaluated by comparing model CO predictions with satellite observations over regions where biomass burning is significant. These emissions are consistent with observations over the US but have a high bias in three out of four regions of large tropical burning. The large-scale evaluations of the magnitudes and characteristics of the differences presented here are a necessary first step toward an ultimate goal of reducing the large uncertainties in biomass burning emission estimates; thereby enhancing environmental monitoring and prediction capabilities. © 2008 Society of Photo-Optical Instrumentation Engineers;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Journal article (JA)

引用

共 50 条

[31] Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products
Belabid, Nasreddine
Zhao, Feng
Brocca, Luca
Huang, Yanbo
Tan, Yumin
REMOTE SENSING, 2019, 11 (03)
[32] Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood
Li, Wenzhao
Li, Dongfeng
Fang, Zheng N.
HYDROLOGY, 2023, 10 (01)
[33] AUTOMATIC NEAR-REAL-TIME IMAGE PROCESSING CHAIN FOR VERY HIGH RESOLUTION OPTICAL SATELLITE DATA
Ostir, K.
Cotar, K.
Marsetic, A.
Pehani, P.
Perse, M.
Zaksek, K.
Zaletelj, J.
Rodic, T.
36TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT, 2015, 47 (W3): : 669 - 676
[34] Near-real-time estimation of global horizontal irradiance from Himawari-8 satellite data
Tan, Yunhui
Wang, Quan
Zhang, Zhaoyang
RENEWABLE ENERGY, 2023, 215
[35] An evaluation and regional error modeling methodology for near-real-time satellite rainfall data over Australia
Pipunic, Robert C.
Ryu, Dongryeol
Costelloe, Justin F.
Su, Chun-Hsu
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2015, 120 (20) : 10767 - 10783
[36] A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture
Kumar, Ashish
Ramsankaran, Raaj
Brocca, Luca
Munoz-Arriola, Francisco
REMOTE SENSING, 2019, 11 (19)
[37] X-HYBRIDJOIN for Near-Real-Time Data Warehousing
Naeem, Muhammad Asif
Dobbie, Gillian
Weber, Gerald
ADVANCES IN DATABASES, 2011, 7051 : 33 - 47
[38] Near-real-time connectivity estimation for multivariate neural data
Smith, Anne C.
Fall, Christopher P.
Sornborger, Andrew T.
2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2011, : 4721 - 4724
[39] Near-real-time adjusted reanalysis forcing data for hydrology
Berg, Peter
Donnelly, Chantal
Gustafsson, David
HYDROLOGY AND EARTH SYSTEM SCIENCES, 2018, 22 (02) : 989 - 1000
[40] Reaching near-real-time data replication: Part 2
1600, CMP Asia Ltd.- New York Office

← 1 2 3 4 5 →