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三种活跃火遥感产品(MODIS C6、VIIRS V1和VIIRS J1)一致性与差异性对比
引用本文:李鹏,刘颖,施冬,肖池伟.三种活跃火遥感产品(MODIS C6、VIIRS V1和VIIRS J1)一致性与差异性对比[J].地理研究,2022,41(5):1481-1495.
作者姓名:李鹏  刘颖  施冬  肖池伟
作者单位:1.中国科学院地理科学与资源研究所,北京1001012.长江大学地球科学学院, 武汉430100
基金项目:国家自然科学基金项目(41971242);国家自然科学基金项目(42001226);中国科学院青年创新促进会会员人才专项(CAS2020055)
摘    要:MODIS C6、VIIRS V1和VIIRS J1是NASA火灾信息资源管理系统(FIRMS)2020年首次同步发布的三套全球活跃火遥感产品。鉴于卫星过赤道时间、空间分辨率、火情监测算法有别,分析MODIS和VIIRS不同活跃火产品监测结果一致性和差异性,对明确不同火产品的适用性至关重要。利用中南半岛同年活跃火矢量产品,通过GIS渔网(1 km)定量分析不同时间尺度(全年、旱/雨季和昼/夜)活跃火发生的频次与强度特征,横向对比了三套活跃火产品的一致性与差异。结果表明:① 中南半岛三套产品逐月发生频次一致性最高,逐日次之,逐时最低。全年“有火区”格网占比37.23%,其中VIIRS相应比重三倍于MODIS,且J1略高于V1。三套产品两两比较时,VIIRS V1/VIIRS J1组合总体一致性最高,VIIRS J1/MODIS C6组合一致性最低。② 三套产品旱季“有火区”占比、活跃火发生频次与强度均显著高于雨季,且VIIRS V1与J1活跃火发生强度空间分布一致性更高。旱季V1活跃火累积发生频次最高、发生强度更强;雨季J1相应频次最高,发生强度较强。③ 三套产品白天“有火区”占比、活跃火累积发生频次均高于夜间,发生强度空间分布一致性亦高于夜间,其中VIIRS V1与J1夜间一致性最高。且昼/夜V1活跃火累积发生频次和“有火区”占比均最高,但J1发生强度最强。④ VIIRS V1与J1监测精度更高,空间覆盖范围更广,且VIIRS V1更适用于旱季、白天和夜间活跃火相关研究,而VIIRS J1对雨季活跃火的探测水平更高,但MODIS C6的优势在于历史数据丰富。

关 键 词:MODIS  C6  VIIRS  V1  VIIRS  J1  活跃火  数据对比  中南半岛  
收稿时间:2021-05-26

Comparison of the consistency and discrepancy of three remotely-sensed active fire products (MODIS C6, VIIRS V1 and VIIRS J1)
LI Peng,LIU Ying,SHI Dong,XIAO Chiwei.Comparison of the consistency and discrepancy of three remotely-sensed active fire products (MODIS C6, VIIRS V1 and VIIRS J1)[J].Geographical Research,2022,41(5):1481-1495.
Authors:LI Peng  LIU Ying  SHI Dong  XIAO Chiwei
Institution:1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China2. School of Geosciences, Yangtze University, Wuhan 430100, China
Abstract:MODIS C6, VIIRS V1 and VIIRS J1 are the three standard active fire products provided by the Fire Information Resource Management System (FIRMS), which jointly record active fire detections for the first time in 2020. In view of the differences in satellite overpass time, spatial resolution, and detection algorithms, a comparison of the consistency and discrepancy of MODIS and VIIRS active fires is important for understanding their usability. Here, the consistency and discrepancy were quantitatively cross-compared via examining the frequency and intensity characteristics of fire occurrence and their spatial differences in Mainland Southeast Asia (MSEA) at hourly, diurnal, monthly, and seasonal scales through fishnet analysis (1 km). The results show that: (1) The highest consistency of occurrence frequency of MSEA’s active fire among the three products is seen at the monthly level, followed by those at the daily and hourly ones. MODIS- or VIIRS-derived active fires occurred and re-occurred in a total of 37.23% fishnet cells within MSEA, or the “fire-affected grids(FAGs)” with at least once. The proportion of VIIRS FAGs is three times more than that of MODIS, and J1 slightly outnumbers V1. The consistency between VIIRS V1 and J1 is the highest, while that of VIIRS J1/MODIS C6 is the lowest. (2) FAGs proportion, occurrence frequency and intensity of these three products during the dry season are significantly higher than those during the rainy season, particularly the spatial distribution of fire occurrence intensity between VIIRS V1 and J1. The fire occurrence and intensity of VIIRS V1 ranks the first in the dry season, while J1 performs the best in the rainy season. (3) The proportion, occurrence frequency and intensity of these products in the daytime are higher than those in the nighttime. Daytime consistency of spatial distribution of active fire occurrence intensity is also higher, while VIIRS V1 and J1 are mostly consistent in the nighttime. V1-derived active fires have the largest occurrence frequency and FAGs proportion at day/night, but the occurrence intensity of J1 active fires is stronger. (4) VIIRS V1 and J1 have higher monitoring accuracy and wider space coverage. The former is more suitable for active fire analysis in the dry season, daytime and nighttime, while the latter has a higher detection level in the rainy season, and MODIS C6 has longer time series.
Keywords:MODIS C6  VIIRS V1  VIIRS J1  active fire  data comparison  Mainland Southeast Asia  
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