基于地球静止气象卫星的地表参数遥感研究进展
Retrieval of land surface parameters from geostationary satellite data: An overview of recent developments
- 2021年25卷第1期 页码:109-125
纸质出版日期: 2021-01-07
DOI: 10.11834/jrs.20210194
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纸质出版日期: 2021-01-07
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地表参数定量遥感反演是遥感科学研究的重要环节。21世纪以来,地球静止气象卫星数据在地表参数遥感反演中受到越来越多的重视。本文对利用地球静止气象卫星进行地表参数遥感反演研究的进展进行了综述。文章首先简单介绍了当前正在运行的欧盟Meteosat、美国GOES-R、日本葵花和中国风云静止卫星系统,随后详细总结了不同卫星系统估算各种地表参数的方法。在此基础上,文章对进一步利用静止卫星估算地表参数的研究展开讨论,指出未来的研究应重点关注几个方面:(1)探索和运用新技术提高静止卫星数据获取和处理的效率和精度;(2)融合全球多颗静止气象卫星,同时与极轨卫星融合,生产覆盖全球的长时序地表参数产品;(3)探索地表参数的高效获取方法,对静止气象卫星地表参数产品开展真实性检验,满足地表过程研究和资源环境动态监测对高质量地表参数产品的需求。
Since the beginning of this century, more and more attention has been given to using geostationary meteorological satellite data in the retrieval of land surface parameters. This paper gives an overview of the recent developments on the retrieval of land surface parameters from geostationary satellite data. Geostationary meteorological satellites have been developed in Europe (Meteosat), United Sates (GOES-R), Japan (Himawari), and China (FY series). The geostationary satellite data volume is usually high because of the high temporal revisit frequency, which poses great challenges to data storage, parameter retrieval and product distribution. Nonetheless, each satellite program has developed a series of land surface products to support near real-time applications.
Various methods to estimate land surface parameters are described. The Meteosat SEVIRI is leading the development of land surface products, especially the unique long-term Thematic Climate Data Record (TCDR) products. The standard products include Land Surface Temperature (LST), longwave and shortwave radiances, albedo, fraction vegetation cover, leaf area index, the fraction of absorbed photosynthetic active radiation, and gross primary production, evapotranspiration, latent and sensible heat flux, wild fire, and snow cover. Among the standard products, the LST, radiance, and albedo are also distributed in the TCDR products. GOES-R and FY-4A are also releasing similar preliminary products, but their quality still need to be fully validated. The Himawai-8 products are still in the research stage. Further physical retrieval methods and various machine learning inversion methods can be explored to improve the accuracy of parameter inversion. It is also necessary to improve the quality of auxiliary atmospheric field data and model simulations to facilitate the land surface parameter retrieval.
Multiple geostationary satellites can be integrated to provide long-term observations and global coverage. EUMETSAT has integrated all TCDR products generated from the first-and second-generation Meteosat data, and the National Oceanic and Atmospheric Administration (NOAA) has released all GOES level-1B data since 1979. These two dataset can be used to generate long-term continuous land surface products. GOES-R and Himawari-8 data have also been combined to generate high quality top-of-atmosphere reflectance and bright temperature products, which will greatly facilitate the generation of other land surface parameters. Geostationary satellite data can also be combined with the polar orbiting satellite data in land surface parameter retrieval; however, current studies are mostly carried out on small regional scales, whereas national and global studies are relatively deficient.
Land surface parameters retrieved from geostationary satellites can be validated through comparison with concurrent ground measurements, polar orbiting satellite products and model simulations. However, simultaneous real-time surface data are lacking. On the other hand, current geostationary meteorological satellite data are in the kilometric spatial resolution and are limited for high resolution applications. With its high temporal and spatial resolutions (50 m), the GF-4 satellite launched by China provides a great potential for high resolution land surface monitoring and is worthy of further exploration.
Future researches using geostationary satellite data to estimate land surface parameters include: (1) exploration of new techniques to improve the efficiency and accuracy of geostationary satellite data acquisition and processing; (2) integration of multiple geostationary and polar-orbiting satellite data to produce long term global land surface parameters; (3) exploration of automatic field measurement methods to enhance the validation of land surface parameters derived from geostationary satellite data.
地表特征参数如地表温度、地表辐射、植被指数和地表蒸散发(ET)等是研究陆地生态系统碳循环、水循环和能量循环的重要参数。定量遥感研究的一个重要环节就是从不同的卫星观测数据中获取这些重要的地表特征参数(
21世纪70年代后期,美国和欧盟分别发射了首颗静止气象卫星GOES-1(1975年)和 Meteosat-1(1977年),分别由美国国家海洋与大气局(NOAA)和欧洲气象卫星应用组织(EUMETSAT)运营。后续美国和欧盟又分别发射了一系列静止气象卫星,形成了连续稳定的观测序列。日本也于1977年发射了第一颗葵花(Himawari)静止气象卫星,由日本气象局运行。俄罗斯(1994年)、印度(2002年)和韩国(2010年)都先后发射了各自的静止气象卫星(http://rammb.cira.colostate.edu/dev/hillger/geo-wx.htm[2020-06-08])。
中国于1997-06-10发射了第一颗试验型静止气象卫星风云二号A星(FY-2A),这之后的FY-2系列卫星构成了中国第一代地球轨道静止气象卫星。作为世界气象组织全球业务卫星序列中的一员,中国气象卫星构成了全球综合地球观测系统的重要组成部分,为区域乃至全球许多国家的经济发展做出了重要贡献(
虽然静止气象卫星主要为气象业务服务,由于地球静止卫星重复观测频率高,空间覆盖范围广,业务化水平高,研究人员也一直在利用静止气象卫星开展地表参数估算的研究。早在20世纪80年代,就有研究利用GOES卫星估算地表温度(Chen 和Allen,1987)、太阳入射辐射(
进入21世纪,随着地球静止卫星技术日渐成熟,利用静止卫星反演地表参数发展迅速,开始受到了定量遥感界越来越多的关注。欧盟、美国和中国都分别利用各自的静止卫星平台开始业务化的地表参数产品生产,比如中国建立的风云卫星数据和产品服务系统(http://satellite.nsmc.org.cn/portalsite/default.aspx[2020-06-08])。海量静止卫星数据为地表碳循环、能量和水循环研究带来了新的机遇和挑战(
本文对目前业务运行的欧盟、美国、日本和中国的静止气象卫星进行了分析,对它们各自的地表参数反演算法进行了总结,对当前和未来的发展进行了讨论。本文涉及的地球静止卫星大多是静止气象卫星,在讨论中也包括个别非气象应用卫星,如中国的高分四号卫星(GF-4)。
EUMETSAT运营的Meteosat系列卫星目前是第二代(MSG),共有8—11号4颗,每颗卫星设计运行时间7 a,布置在不同的定点位置。Meteosat-11是目前在轨运行的主要卫星,每15 min提供一个全圆盘图像。Meteosat-10和Meteosat-9提供快速扫描服务,每五分钟可对欧洲、非洲和周边地区进行扫描一次。MSG搭载旋转增强可见近红外成像仪(SEVIRI),共有12个波段,其中高分辨率可见光波段1 km,其他波段3 km(
国家/组织 | 卫星 | 成像仪 | 发射日期 | 定点位置 | 波段数 | 空间分辨率 | 网址 |
---|---|---|---|---|---|---|---|
欧盟 | Meteosat-11 | SEVIRI | 2015-07-15 | 0° | 12 | 1 km (VIS), 3 km | WWW1 |
美国 | GOES-16 (GOES-R) | ABI | 2016-11-19 | 75.2°W | 16 | 0.5 km (0.64μm), 1.0 km (VIS-NIR), 2 km (>2.0 μm) | WWW2 |
日本 | Himawari-8 | AHI | 2015-07-07 | 140.7°E | 16 | 0.5 km (0.65 μm), 1.0 km (VIS-NIR), 2 km (>1.6 μm) | WWW3 |
中国 | FY-4A | AGRI | 2016-12-11 | 99.5°E | 14 | 0.5-1.0 km (VIS-NIR), 2.0-4.0 km (>1.3 μm) | WWW4 |
注: WWW1 卫星和传感器(https://www.eumetsat.int/website/home/Satellites/CurrentSatellites/Meteosat/index.html[2020-06-08]),产品(https://landsaf.ipma.pt/en/about/catalogue/[2020-06-08])WWW2 卫星和传感器(http://www.goes-r.gov/[2020-06-08]),产品(https://www.ncdc.noaa.gov/data-access/satellite-data/goes-r-series-satellites[2020-06-08])WWW3 卫星和传感器(http://www.jma.go.jp/jma/jma-eng/satellite/[2020-06-08])WWW4 卫星和传感器(http://fy4.nsmc.org.cn/portal/cn/satellite/FY4A.html[2020-06-08]),产品 (http://fy4.nsmc.org.cn/portal/cn/theme/FY4A_product.html[2020-06-08])。
Meteosat提供了完整体系的地表系列产品(
参数 | 时间分辨率 | 空间分辨率 | NRT覆盖时段 | CDR覆盖时段 | 目标误差(Accuracy)* |
---|---|---|---|---|---|
地表温度 | 15’ | 3 km | 2005-05— | 2004—2015 | 2 K (RMSE) |
10 d | 3 km | 2015-11— | 2 K (RMSE) | ||
下行长波和短波辐射 | 30’ | 3 km | 2005— | 2004—2015 | 10% |
1 d | 3 km | 2015-11— | 10% | ||
总下行地表短波辐射 | 15’ | 3 km | 2020-04— | 20 (TDSSR<200 W/m2), 10% (TDSSR≥200 W/m2) | |
散射比 | 15’ | 3 km | 2020-04— | 0.1 (fd<0.5), 20% (fd≥0.5) | |
反照率 | 1 d | 3 km | 2005-05— | 2004—2015 | 10% |
10 d | 3 km | 2009-02— | 2004—2015 | 10% | |
植被覆盖度/叶面积指数/光合有效辐射吸收效率 | 1 d | 3 km | 2006-01— | 2004—2015 | 20%/40%/20% |
10 d | 3 km | 2013-01— | 20%/40%/20% | ||
总初级生产力 | 10 d | 3 km | 2018-03— | 67% | |
蒸散发 | 30’ | 3 km | 2005-04— | 25% (ET>0.4 mm/h), 0.1 mm/h (其他) | |
1 d | 3 km | 2010-12— | 20% (ET>2.5 mm), 0.5 mm (其他) | ||
潜热 | 30’ | 3 km | 2019-03— | 25% (LE>250), 63 W/m2 (其他) | |
显热 | 30’ | 3 km | 2019-03— | 35% (H>250), 87.5 W/m2 (其他) | |
参考ET | 1 d | 3 km | 2016-01— | 2004—2015 | 10% |
火点探测 | 15’ | 3 km | 2010-04— | 5% (与MODIS比较) | |
火点辐射能量 | 15’ | 3 km | 2008-03— | 2004—2015 | n.a. |
火点辐射能量(格网) | 1 h | 5° | 2008-07— | 2004—2015 | n.a. |
火险图(欧洲) | 1 d | 3 km | 2010-02— | 5.0 | |
积雪覆盖 | 1 d | 3 km | 2005-01— | 75% (林区) /90% |
注: * 除了标注项目和文中说明,一般表示为绝对或相对误差。数据来自(https://landsaf.ipma.pt/en/about/catalogue/[2020-06-08])除了火险图覆盖欧洲区,其余产品覆盖全圆盘。TDSSR:总下行地表短波辐射。
2.1.1 地表温度
地表温度产品提供15 min和每旬的晴空地表温度信息。计算方法为通用的辟窗算法(Wan 和Dozier,1996),该方法通过两个相邻红外波段(10.8 μm和12.0 μm)对大气的不同吸收率来进行大气校正并计算地表温度。计算中所需的地表比辐射率数据通过植被覆盖度估算得到。
2.1.2 长波与短波辐射
地表下行短波辐射(DSSR,W/m2)是指到达地表的0.3—4.0 μm范围内的辐射能量。DSSR的估算按晴天和有云情况用不同的算法。有云时先估算大气顶层(TOA)反照率,然后通过辐射传输模型基于3个短波波段(0.6 μm、0.8 μm和1.6 μm)估算DSSR (
地表下行长波辐射(DSLR,W/m2)的波长范围为4.0—100 μm。由于卫星无法直接探测DSLR,SEVIRI先根据ECMWF模型的气温和水汽数据以及卫星观测到的云量情况大致赋值(
2.1.3 反照率
SEVIRI基于3个短波波段(0.6 μm,0.8 μm,1.6 μm)计算得到地表的可见光、近红外和总体短波波段反照率(
2.1.4 植被参数
SEVIRI的植被参数包括植被覆盖度(FVC)、叶面积指数(LAI)和光合有效辐射吸收效率(FAPAR)以及总初级生产力(GPP)等(
LAI产品通过与FVC的半经验关系估算得到(
FVC=1-exp(-b·G(0)·Ω·LAI) | (1) |
式中, b为冠层后向散射参数,假设为0.945,G(0)为太阳天顶角为0°时叶片投影函数,在假设叶倾角球形分布的情况下G(0)=0.5。Ω为聚集指数,算法根据不同生态型赋值一个静态的聚集指数数据(
FAPAR则利用经验统计关系从一个简单的重归一化差值植被指数(RDVI)进行估算(Roujean 和Bréon,1995),该经验关系基于SAIL模型在主平面的模拟得到(太阳天顶角45°,观测天顶角60°)(
GPP根据光能利用率(LUE)计算(
GPP = PAR × FAPAR × LUE | (2) |
式中,PAR为光合有效辐射,LUE跟水胁迫因素有关,可从SEVIRI每天的实际和参考ET产品计算得到。SEVIRI先根据DSSR和FAPAR计算每天的GPP,然后生产旬产品(
2.1.5 ET与能量通量
SEVIRI通过卫星观测所得到的辐射信息、生物物理参数,土壤湿度和辅助气象数据来驱动SVAT物理模型计算ET(mm/h)、潜热和显热通量(W/m2)(
2.1.6 野火
SEVIRI火点探测与监测主要利用火点像元与周边像元的辐射差异做阈值分割得到(
综合火点监测情况、植被信息以及ECMWF天气预报情况(气温、相对湿度、风速和24小时累计降水),SEVIRI进一步对未来3天的野火风险等级进行预测并生产火灾风险图。该图目前只覆盖欧洲区,时间上自2010-02起始,火险等级计算参考了加拿大林火风险分级系统(
2.1.7 积雪覆盖
积雪覆盖产品从可见光和红外波段观测得到的辐射、亮温和地表温度信息来判定是云像元、完全积雪、部分积雪还是无雪像元(Siljamo 和Hyvärinen,2011)。该算法依靠NWC SAF提供云掩膜信息,先得到15 min的积雪覆盖信息然后融合成逐日产品。目标精度在林区为75%,其他区域则为90%。
SEVIRI对历年获取的准实时产品利用新算法进行了再处理,选择其中的高质量数据形成连续的气候数据记录(CDR)产品(
EUMETSAT进一步整合了所有第一和第二代的Meteosat数据,利用统计和模型反演方法生成了1991年—2015年的地表温度产品,形成了专题气候数据记录(TCDR)(
参数 | 传感器 | 时期/年 | 时间分辨率 | 空间分辨率 | 数据引用(DOI) |
---|---|---|---|---|---|
地表温度 | MFG/MSG | 1991—2015 | 1 h, 1 mo | 0.05° | 10.5676/EUM_SAF_CM/LST_METEOSAT/V001 |
辐照度 | MFG | 1983—2005 | 1 h, 1 d, 1 mo | 0.03° | 10.5676/EUM_SAF_CM/RAD_MVIRI/V001 |
反照率 | MFG | 1982—2006 | 10 d, 1 mo | 3 km | 10.15770/EUM_SEC_CLM_0001 |
注: MFG:Meteosat First Generation; MSG: Meteosat Second Generation。
除了标准产品外,一些研究者利用SEVIRI数据反演地表太阳入射辐射和PAR(
SEVIRI的逐日产品在许多研究中都得到了应用。比如,
GOES-R系列静止气象卫星是目前NOAA运行的最新一代静止气象卫星。该系列卫星包括GOES-R(GOES-16),GOES-S(GOES-17),GOES-T,GOES-U等4颗卫星,其中前两颗星分别在2016.11.19和2018.3.1发射运行,分别定轨在75.2°W和137.2°W,后两颗预计在2021年和2024年发射(http://www.goes-r.gov/[2020-06-08])。GOES-R上搭载了数个传感器,与陆面产品相关的传感器主要是高级基线成像仪(ABI)。ABI有16个波段,空间分辨率分别为0.5 km(0.64 μm),1.0 km(可见光—近红外)和2.0 km(>2 μm)(
在众多GOES-R产品中,主要的是大气产品,少数的几个地面产品包括地表温度、下行短波辐射、火点/热点和积雪覆盖度等(https://www.goes-r.gov/products/overview.html#ABI[2020-06-08],
参数 | 覆盖范围 | 时间分辨率 | 空间分辨率/km | 覆盖时段 | 误差 | 精度 | 制图误差/km |
---|---|---|---|---|---|---|---|
地表温度 | 全圆盘 | 1 h | 10 | 2017-05-24— | 2.5 K | 2.3 K | 5 |
美国大陆 | 1 h | 2 | 2017-05-24— | 2.5 K | 2.3 K | 1 | |
中尺度区域 | 1 h | 2 | 2017-05-24— | 2.5 K | 2.3 K | 1 | |
下行短波辐射 | 全圆盘 | 1 h | 50 | 2017-06-23— | 65—110 W/m2 | 100—130 W/m2 | 4 |
美国大陆 | 1 h | 25 | 2017-06-23— | 65—110 W/m2 | 100—130 W/m2 | 2 | |
中尺度区域 | 1 h | 5 | 2017-06-23— | 65—110 W/m2 | 100—130 W/m2 | 1 | |
火点/热点温度 | 全圆盘 | 15’ | 2 | 2017-05-24— | 2 K | 2 K | 1 |
美国大陆 | 5’ | 2 | 2017-05-24— | 2 K | 2 K | 1 | |
积雪覆盖 | 全圆盘 | 1 h | 2 | 2017-12-12— | 0.15 | 0.30 | 1 |
美国大陆 | 1 h | 2 | 2017-12-12— | 0.15 | 0.30 | 1 | |
中尺度区域 | 1 h | 2 | 2017-12-12— | 0.15 | 0.30 | 1 |
注: 表4来源网址https://www.goes-r.gov/products/overview.html#ABI[2020-06-08],美国大陆和中尺度区域分布覆盖3000 km × 5000 km以及1000 km × 1000 km范围。
3.1.1 地表温度
ABI地表温度产品从14和15波段(11.2 μm和12.3 μm)L1B的亮温数据运用分裂窗算法反演得到(
3.1.2 下行短波辐射
地表下行短波辐射产品利用1—6波段的反射率作为输入数据,利用查找表方法计算地表瞬时直射和漫射短波辐射产品(W/m2),生产时需要云、气溶胶和降水等辅助信息。该算法有直接和间接两种计算方式,直接方法利用ABI的云和气溶胶产品以及地表反照率数据直接估算(Charlock 和Alberta,1996),当输入ABI产品有缺失时则启用间接算法,从TOA反照率反演得到下行短波辐射产品(
3.1.3 火点与热点
火点温度产品从L1B短波反射波段、中红外和热红外发射波段图像经过一系列阈值分割计算得到,主要原理是基于第7波段(3.9 μm)对亚像元中高温异常的敏感性特征,其中需要用到NCEP的降水数据作为辅助,另外需要波段2数据(0.64 μm)用于云剔除和计算地表反照率(
3.1.4 积雪覆盖度
ABI借鉴了MODIS利用归一化差值积雪指数(NDSI)提取积雪覆盖产品的方法,从地表多波段多角度反射率数据中基于混合像元分解方法提取积雪覆盖比率(
GOES-R项目设计时还计划生成洪水、冰覆盖、平原积雪厚度、地表反照率、比辐射率、植被覆盖度和植被指数(NDVI)等地表产品(https://www.goes-r.gov/syseng/docs/MRD.pdf[2020-06-08]),因为优先级的原因,目前这些产品还在逐步生产中(
覆盖范围 | 时间分辨率/h | 空间分辨率/km | 误差 | 精度 | 滞后时间/h | 制图误差/km | |
---|---|---|---|---|---|---|---|
洪水/积水 | 全圆盘 | 1 | 10 | 60% | N/A | 6 | 5 |
中尺度区域 | 1 | 10 | 60% | N/A | 6 | 5 | |
冰覆盖 | 全圆盘 | 3 | 2 | 85% | N/A | 24 | 1 |
积雪厚度(平原) | 全圆盘 | 1 | 2 | 9 cm | 15 cm | 1 | 1 |
美国大陆 | 1 | 2 | 9 cm | 15 cm | 1 | 1 | |
中尺度区域 | 1 | 2 | 9 cm | 15 cm | 1 | 1 | |
地表反照率 | 全圆盘 | 1 | 2 | 0.08 | 10% | 1 | 2 |
比辐射率 | 美国大陆 | 1 | 10 | 0.05 | 0.05 | 1 | 5 |
植被覆盖度 | 全圆盘 | 1 | 2 | 0.10 | 0.10 | 1 | 1 |
美国大陆 | 1 | 2 | 0.10 | 0.10 | 1 | 1 | |
植被指数(NDVI) | 全圆盘 | 1 | 2 | 0.04 | 0.04 | 1 | 1 |
美国大陆 | 1 | 2 | 0.04 | 0.04 | 1 | 1 |
除了上述业务产品,围绕GOES-R还有一些探索性研究工作。
美国宇航局(NASA)的GeoNEX项目对GEOS-R数据进行大气校正获得了地表反射率产品,可用于植被指数估算和LAI以及FAPAR等参数的反演(https://www.nasa.gov/nex/[2020-06-08])。
葵花(Himawari)系列静止气象卫星目前在轨运行的有Himawari-8和Himawari-9号两颗卫星,分别于2015-07-07和2016-11-02发射,定点位置140.7°E。目前葵花-8号做主要观测,9号星待机作为备用,在2022年后两颗卫星主备用互换,两颗星预计运行到2029年。Himawari-8和Himawari-9号卫星上搭载了先进葵花成像仪(AHI),有16个波段,其空间分辨率为1 km(可见光—近红外)和2 km(>1.6 μm),其中0.65 μm为0.5 km(
葵花卫星本身主要作为气象部门的业务卫星,其产品主要是大气特征参数,如大气运动矢量、晴空辐射、云和气溶胶特征以及海面风场等。官方地表产品,目前只有Beta版本的地表短波辐射和野火产品,还没有形成系统的业务化地表参数产品(https://www.eoyc.jaxa.jp/ptyee/useiguide.htmt[2020-06-08])。由于葵花传感器良好的几何和辐射特性,利用AHI进行地表植被、能量循环参数和应急响应方面的研究非常活跃。
4.1.1 植被参数
利用静止气象卫星的高时频特征,一些研究人员利用AHI准实时反射率和植被指数数据开展了植被物候监测研究 (
在获得了AHI的反射率和植被指数以后,可以进一步进行LAI和FAPAR的估算。例如,
4.1.2 地表温度
与SEVIRI和ABI类似,AHI地表温度也常常利用辟窗算法由热红外波段的亮温信息反演得到。例如,
4.1.3 长波与短波辐射
基于遥感估算地表辐射通常包括经验统计方法、基于辐射传输模型的方法以及机器学习方法等,这些方法都曾用于从AHI估算地表辐射。
4.1.4 反射率与反照率
地表反射率产品通过对TOA辐射值的大气校正得到。
4.2.1 火情监测
与SEVIRI和ABI类似,AHI火点探测的基本原理也是基于中红外和热红外的亮温异常状况以及不同波段增长幅度差异进行阈值判断(
4.2.2 积雪覆盖
在常用的归一化差值积雪指数(NDSI)基础上,
4.2.3 沙尘检测
风云四号系列气象卫星的首颗卫星FY-4A于2016-12-11发射,定点位置99.5°E(http://fy4.nsmc.org.cn/portal/cn/theme/FY4A.html[2020-06-08])。与地表相关的仪器主要是多通道扫描成像辐射计(AGRI),有14个波段(6个可见/近红外波段,2个中波红外波段,2个水汽波段和4个长波红外波段),空间分辨率可见/近红外波段为0.5—1 km、红外波段为2—4 km(
FY-4A AGRI地表参数可以从风云卫星遥感数据服务网得到(http://satellite.nsmc.org.cn/portalsite/default.aspx[2020-06-08])。该站点提供的陆表产品和部分辐射产品都与地表有关,其中陆表产品包括火点/热点检测、地表比辐射率、积雪覆盖、地表温度和反照率等。辐射类产品主要是反映地气系统的辐射收支情况,包括地表下行长波辐射、地表入射太阳辐射和地表上行长波辐射等(
时间分辨率 | 空间分辨率 | 覆盖时段 | |
---|---|---|---|
地表温度 | 15’ | 4 km | 2019-08-01— |
反照率 | 15’ | 4 km | |
火点/热点监测 | 15’ | 4 km | |
比辐射率 | 15’ | 12 km | 2019-01-18— |
积雪覆盖 | 15’ | 4 km | |
下行长波辐射 | 15’ | 4 km | 2019-01-18— |
入射太阳辐射 | 15’ | 4 km | 2018-03-12— |
上行长波辐射 | 15’ | 4 km | 2019-01-18— |
注: 数据参见http://satellite.nsmc.org.cn/portalsite/default.aspx[2020-06-08];覆盖范围均为全圆盘。
AGRI所有地表参数都提供每15 min的准实时产品,空间分辨率为4 km,其中比辐射率为12 km。本文成文时,辐射类产品已全部上线,陆表产品已上线地表比辐射率和陆表温度产品。产品为NetCDF格式。但目前关于产品的算法文档和验证文档还待进一步完善。
除了FY-4A标准产品,研究人员也利用AGRI数据进行了大量的地表参数遥感反演研究。
AGRI火点探测与动态评估方法也是基于多波段的辐射信息差异(
在干旱快速监测方面,
风云二号气象卫星(FY-2)是中国自行研制的第一代地球轨道静止卫星,第一颗试验型卫星FY-2A于1997-06-10发射。首批地表参数产品则由2004-10-19发射的FY-2C开始生产。2018-06-05发射的FY-2H星,定位在79°E,为西亚、中亚、非洲和欧洲等“一带一路”沿线国家和地区提供良好的高频次观测。国家卫星气象中心利用风云二号系列业务卫星(FY-2C至FY-2H)生产了自2005年以来的地表温度、入射太阳辐射、积雪覆盖和沙尘监测等地表信息产品(
卫星 | 定点位置 | 地表温度 | 入射太阳辐射 | 积雪覆盖 | 沙尘监测 |
---|---|---|---|---|---|
FY-2C | 105°E | — |
2005-06-09— 2009-11-24 (1 d) |
2005-06-14— 2009-11-24 (1 d) |
2005-06-20— 2009-11-24 (1 h) |
FY-2D | 86.5°E | — |
2007-02-14— 2015-06-30 (1 d) |
2007-07-06— 2015-06-30 (1d, 10 d) |
2007-05-24— 2015-06-30 (30’) |
FY-2E |
105°E/86.5°E (2015-07-01后) | — |
2009-12-10— 2019-01-17 (1 d) |
2010-01-07— 2019-01-17 (1d) |
2010-01-28— 2019-01-17(1 h) |
FY-2F | 112°E |
2012-11-20— (1 h, 1 d, 5 d, 10 d, 1 mo) |
2012-11-11— (1 d) |
2012-11-09— (1 d, 10 d) |
2012-10-31— (30’) |
FY-2G | 105°E |
2015-06-03— (1 h, 1 d, 5 d, 10 d, 1 mo) |
2015-06-03— (1 d) |
2015-06-03— (1 d, 10 d) |
2015-06-03— (1 h) |
FY-2H | 79°E |
2018-06-05— (1 h, 1 d, 5 d, 10 d, 1 mo) |
2018-06-05— (1 d) |
2018-06-05— (1 d) |
2018-06-05— (30’) |
注: 数据参见http://satellite.nsmc.org.cn/portalsite/default.aspx[2020-06-08]。
与此同时,诸多的地面参数遥感反演研究也围绕风云二号卫星展开,比如基于分裂窗算法和地表温度日周期变化反演多时相地表温度(
本节针对静止气象卫星的数据处理、参数反演方法、卫星产品验证和应用等方向进行探讨,分析当前存在的问题和不足,探索未来的发展方向,为进一步提升卫星参数反演与应用服务。
因为静止卫星的高时频和准实时特征,获取的地面数据无疑是海量的,这对数据的存储、处理和服务都提出了新的挑战。总体而言,利用地球静止卫星反演地表参数的方法以实用为主,以满足高时频连续观测和实时产品生产的需求。从产品方面来看,各卫星均开发了一系列地表参数产品,支持近实时应用,但也存在产品空间分辨率低、全球覆盖不足的缺点。同时各卫星数据产品开发进度不一,比如SEVIRI起步较早,业务化水平高,数据产品较为全面,特别是独具特色的长时期TCDR产品。GOES-R和FY-4A紧随其后,也发布了一些初级产品,但产品质量还没有得到充分的检验,而葵花卫星的产品还处在研究阶段。
目前,需要进一步突破静止气象卫星跨平台数据快速汇聚、处理与反演方面的关键技术,提升海量数据的快速汇集、处理和在线服务功能。一些研究性的工作可以借鉴原先比较成熟的MODIS和Landsat系列产品的算法(Ganguly 等,2017;
由于单颗静止气象卫星的时空覆盖范围有限,必须联合运用多颗静止气象卫星的综合优势,获取长时期连续覆盖数据,以满足地表环境变化监测的需求。比如EUMETSAT对所有第一和第二代Meteosat数据的整合分析生成的TCDR产品(
与此同时,为了获取全球覆盖图像,必须综合运用全球的静止气象卫星观测(
多星联合反演与应用中,必须首先对不同卫星进行几何和辐射定标,以消除不同卫星传感器的几何和辐射差异。最近,研究人员对GOES和葵花卫星数据进行了定标处理,生成了高质量的TOA反射率和亮温产品,该数据集将为后续地表参数的生产提供极大的便利(
由于静止卫星的观测几何问题,在高纬度地区的图像变形较大,成像效果不佳,而极轨卫星能弥补高纬度地区的观测。同时静轨与极轨卫星的结合,能弥补极轨卫星在观测时相上的局限,特别是云覆盖对地表信息提取造成的障碍。为了综合发挥静止与极轨卫星的优势,许多研究人员利用二者进行综合反演地表参数。相关的融合方法大致可以分为两类,第一类直接从静止与极轨卫星数据联合反演地表参数,如
与其他卫星产品的地面验证方式类似,静止卫星产品的验证也主要通过与用地面同步测量数据、极轨卫星数据或者模型模拟数据对比分析(
尽管静止气象卫星数据已经在多种环境和灾害监测中得到了应用,但是数据的空间分辨率还是比较低,主流的空间分辨率都在千米级。这一空间分辨率对于全球性应用还可以,区域性的应用则需要百米级甚至十米级的静止卫星数据。
除了静止气象卫星系列,高分四号(GF-4)是中国的另一颗令人瞩目的静止卫星。GF-4于2015-12-29成功发射,定点位置105.6°E,搭载的凝视相机空间分辨率分别为50 m(可见光—近红外)和400 m(中红外)(http://www.cresda.com/CN/Satellite/9855.shtml[2020-06-08])。因GF-4具备高时间分辨率和较高空间分辨率的优势,对地表共性参数的提取以及应对突发灾害具有重要意义,受到很多的关注(http://gaofenplatform.com/channels/8.html[2020-06-08])。但由于GF-4的空间覆盖范围限制,目前地表参数的生产和应用都还处于初级阶段,仅有少量报导进行了参数估算应用,如地表反照率估算(
过去20余年,伴随着静止气象卫星在气象业务上的应用,利用静止气象卫星获取地表参数也得到了很大的发展,这些参数在地表碳、能量和水循环研究以及地表资源环境动态监测中发挥了极其重要的作用。本文对目前业务运行的静止卫星和地表参数反演算法进行了系统分析和总结,并对当前和未来的发展方向进行了探讨。
总体来看,欧盟的MSG SEVIRI提供了完整系列的地表参数和气候数据记录数据,参数反演方法清晰实用。美国利用GOES-R系列卫星推出了几套高质量重点产品,后续的植被和反照率等产品也在逐步生产中。日本的葵花系列卫星虽未推出官方业务产品,但该卫星获得了中国研究人员的广泛青睐,被用来生成了多种陆表参数产品。中国的风云静止气象卫星也生产了系列地表参数产品,后续开放获取、地面验证和算法文档也在逐步推进。利用中国的GF-4静止卫星也在局地研究中做出了许多有特色的工作。
为了充分发挥静止气象卫星的优势,需要进一步探索新的数据处理方法,提高算法精度和效率,同时充分利用各种平台和高低轨道卫星数据,生成高质量的长时序地表参数产品,以满足地表过程研究和资源环境动态监测的需求。
致谢:撰写过程中博士生张英慧同学提出了部分修改意见并帮助整理了初稿,国家卫星气象中心的李贵才研究员参与了交流并提供了部分FY-4A资料,在此一并致谢。
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