改进的浓密植被法反演Landsat-8 OLI气溶胶光学厚度

针对利用传统浓密植被法难以准确确定地表反射率的不足,分析了Landsat-8 OLI影像红蓝波段的地表反射率与归一化植被指数、散射角对短波红外波段(2.1μm)地表反射率的关系。分析表明,红、蓝波段与短波红外波段(2.1μm)地表反射率之间的比例关系随归一化植被指数和散射角的改变而不同,据此提出构建用于确定红、蓝波段地表反射率的关系模型,用于实现气溶胶光学厚度的反演。选取美国中东部云覆盖较小的地区进行气溶胶反演,使用AERONET站点数据进行反演结果的验证。结果表明,使用该模型得到反演结果与AERONET站点的实测值具有很好的一致性,拟合结果较好;大约70%的数据位于误差线内,反演结果满足精度要求。     

基于Himawari-8的气溶胶反演研究

为获取中国区域高时空分辨率、高精度的气溶胶光学厚度（AOD）产品。基于Himawari-8卫星数据和MODIS地表反射率产品,反演了中国区域2018年4月~2019年4月逐10分钟的AOD,该方法可同时对暗像元、亮像元区域进行反演。依托全球气溶胶观测网（AERONET）中国境内的6个观测站数据,对反演结果进行一致性检验,同时将AOD反演结果与Himawari-8官方最新发布的AOD产品（020版）进行对比。结果表明：①红蓝比值法AOD反演结果与AERONET AOD之间相关性很高,除包头站外,其余5个站点的相关系数R在0.728~0.863之间,EE（误差期望）范围内样本点百分比在47.7%~68.6%之间,与Himawari-8 AOD产品相比有很大优势;②红蓝比值法AOD反演结果与AERONET AOD在时间序列走势上基本一致,但在AOD >1时,反演结果较AERONET AOD偏高。Himawari-8 AOD春夏季走势相对于AERONET AOD较为一致,但由于秋冬季Himawari-8 AOD有明显的日变化,且日变化较大,其走势与AERONET AOD偏离较大;③红蓝比值法AOD反演结果与MODIS AOD产品空间分布基本一致,AOD反演结果总体较MODIS AOD略为偏低。在冬季时红蓝比值法反演范围较MOD04_3K AOD的反演范围广;④红蓝比值法AOD在冬春季的北方高反射率地表区域的反演结果精度和反演范围较Himawari-8 AOD产品和MOD04_3K产品有很大优势。     

资源一号04星WFI数据气溶胶反演与验证——以北京市及北京周边地区为例

相比目前常用的传感器,资源一号04星(CBERS04)WFI传感器具有较优的时间分辨率、空间分辨率和幅宽优势,对区域气溶胶遥感估算具有较大的应用潜力。针对WFI数据,结合暗像元法和深蓝算法,构建动态查找表参与辐射传输计算,有效地反演出复杂地表上空的气溶胶光学厚度(aerosol optical depth,AOD)。结果表明,所提出的构建动态查找表并集成暗像元法和深蓝算法的方法,能较好地反映实际气溶胶分布情况;基于WFI反演的AOD空间分布与MODIS气溶胶产品基本一致,但具有更广的空间分布,更能反映区域AOD的分布细节;与AERONET站点AOD值间具有显著相关性(r0.94),且67%的反演结果位于误差区间以内,但低值AOD容易出现高估现象。     

从短波红外与红光波段反演华北地区气溶胶

针对MODIS数据监测华北地区陆地气溶胶存在的植被稀少和仪器老化的问题,开展了基于短波红外波段和红光波段地表反射率比值反演气溶胶。讨论了华北地区MODIS传感器红波段和短波红外波段地表反射率的时间变化特征,发现二者之间的比值较为稳定,据此去除地表反射贡献,建立了陆地气溶胶反演算法。利用2016年9月—2017年8月过境华北地区的MODIS数据进行了算法测试和验证,该算法能够同时在植被稠密和稀疏的地区获得反演结果,较好地反映了气溶胶的空间分布。与AERONET北京Radi站和香河站气溶胶产品对比表明,该算法对于城市和乡村区域的应用效果较好,与地面观测结果的相关系数高于0.9,但算法出现了整体低估;分季节验证结果表明,季节之间的差异较小。     

高分一号数据的气溶胶光学厚度反演和验证

针对高分辨率卫星遥感反演气溶胶光学厚度地表噪声难以分离的问题,利用国产"高分一号"(GF-1)的数据特点,提出了一种气溶胶光学厚度反演方法和处理流程。该方法分别基于暗像元和深蓝算法去除了浓密植被和城市亮目标地区的地表贡献,并应用于我国污染较为严重的京津冀、长三角、珠三角等示范区域。利用北京、杭州、香港AERONET地基观测数据,对GF-1反演得到的气溶胶光学厚度进行验证,结果表明:气溶胶高值均集中在三大区域工业排放大和人类活动密集的核心城市,年均光学厚度值在1左右。卫星和地基的相关性总体较好,三大区域的相关系数分别达到了0.71、0.55、0.54。受云识别、亮地表覆盖和气溶胶模式假设等影响,GF-1反演的气溶胶光学厚度存在一定程度的偏差。     

利用GF-4 PMS数据反演城市地区气溶胶光学厚度

高分四号是我国发射的第一颗地球同步轨道卫星,具有较高的空间分辨率及快速重复成像能力,在城市大气环境的高动态监测方面拥有较大的应用潜力。针对城市地区地表反射率高、地表类型复杂,传统的单一算法较难实现大气气溶胶光学厚度有效反演等问题,以北京市为研究区,使用了一种暗目标法和地表反射率数据库相结合的方法,分别对2017年5月25日和10月5日两个时相的气溶胶光学厚度进行了估算。结果表明,该方法能够有效实现城市暗目标区和亮目标区的气溶胶反演,且与MODIS标准陆地气溶胶产品及AERONET站点地面观测数值较为一致,相关系数超过了0.9。     

基于HJ-1卫星数据反演长江三角洲地区气溶胶光学厚度

探索利用我国HJ-1卫星CCD数据,运用深蓝算法开展长江三角洲地区气溶胶光学厚度反演的可行性,并将结果与其他气溶胶光学厚度产品进行比较。针对HJ-1A和HJ-1B数据,反演结果分别与MODIS气溶胶光学厚度产品以及AERONET地基观测数据进行对比验证。结果表明:深蓝算法得到A星、B星的反演结果与MODIS气溶胶产品呈显著相关,但在数值上普遍高于MODIS产品;反演结果与AERONET站点数据之间的误差介于0.008~0.364之间,研究时段内站点数据缺乏,未对误差进行严格的统计分析。基于深蓝算法的HJ-1卫星数据反演结果虽然在数值上与MODIS气溶胶光学厚度产品存在系统性偏差,但在空间上能够较好地反映长江三角洲地区大气气溶胶分布状况,且具有空间分辨率高的优势。     

深蓝算法应用于GF-1 16m相机反演陆地气溶胶

高分一号卫星是我国发展的新一代高分辨率对地观测卫星,如何应用该数据进行环境空气监测是目前迫切需要解决的问题。在深蓝算法基础上,根据GF-1星16 m相机的波段特征,借助MODIS的地表反射率产品去除地表贡献,从蓝波段数据反演了陆地气溶胶,实现了深蓝算法在GF-1星16m相机的应用。在此基础上,收集了2014年8~11月过境北京地区的GF-1星16m相机数据进行了反演实验,结果表明:该算法获取的气溶胶反演结果较好地反映了气溶胶的空间分布。同时,利用同期的AERONET/PHOTONS北京站的地面监测数据进行了算法验证,结果表明,本算法与地面数据有较好的相关性,相关系数大约为0.9,但该算法结果明显高于地面观测结果,可能是MODIS与GF-1星16m相机的波段响应不同导致的结果。     

天宫二号数据大气细颗粒物遥感反演

针对天宫二号(TG-2)大气细颗粒物遥感反演及可行性问题,利用TG-2宽波段成像仪的可见近红外波段数据,结合MOD09A1地表反射率产品构建地表反射率库,使用6S传输模型和深蓝算法反演气溶胶光学厚度(aerosol optical depth, AOD),并结合能见度和相对湿度数据对AOD进行订正,利用统计模型估算了鲁豫皖典型地区的大气细颗粒物PM_2.5浓度,并对PM_2.5浓度的空间分布进行分析。结果表明：天宫二号数据在AOD和PM_2.5遥感反演中具有较高的可行性,拟合度分别达到0.910和0.902;经过垂直订正和湿度订正,PM_2.5和AOD之间的相关性大幅度提高;PM_2.5的空间分布呈“污染岛”特征,这在气溶胶排放和扩散研究中起着重要作用。天宫二号PM_2.5反演为区域大气污染监测提供了一种有效手段。     

Sentinel-2A卫星大气校正方法及校正效果

针对Sentinel-2A卫星大气校正研究的不足,对Sentinel-2A大气校正方法进行介绍,并选取森林、水体、城市建筑物3种地物为研究对象,分析Sentinel-2A单波段通道大气校正前后反射率变化;以Landsat-8、高分一号(GF-1)为辅助数据,从3种传感器大气校正后均质像元反射率曲线、大气校正前后植被指数变化3个方面进行研究。结果表明:1)Sentinel-2A大气校正后,可见光通道反射率变小,波长越长,大气校正效果越不显著;近红外、短波红外反射率增加。2)大气校正后,3种数据源同种地物光谱曲线趋于一致,其中Sentinel-2A水体与植被光谱曲线更能反映地物特征。3)与Landsat-8相比,Sentinel-2A、GF-1WFV1大气校正后林地NDVI明显增大,Sentinel-2A高植被覆盖区增大,低植被覆盖区减小,最能反映植被特征;Sentinel-2ANDWI变化不如Landsat-8NDWI变化显著。     

Retrieval of AOD from Sentinel-2 Data based on Deep Blue Algorithm

Medium-to-high resolution aerosol information is of great significance for surface reflectance inversion and urban ambient air quality monitoring. However, the high-precision aerosol optical thickness (AOD) retrieval in bright areas, such as cities and sparse vegetation areas, has long plagued the quantitative remote sensing applications. Taking Beijing urban area and Baotou desert area as examples, using MODIS surface reflectance products to construct prior knowledge constraints, the AOD inversion of 13 scenes Sentinel-2 images in bright areas was realized based on the deep blue algorithm. To verify the accuracy of the algorithm, the result were compared with the Sentinel-2 official algorithm processing result, the Landsat-8 official aerosol products and the ground-measured AOD data from the Global Aerosol Automated Observing Network (AERONET). The results indicate that the retrieved AOD values from deep blue algorithm is significantly correlated with the measured value of AERONET(R² > 0.90, RMSE = 0.056 0), and the AOD spatial distributions are also well consistent with those from Landsat-8, which reflects the characteristics of human activities. But, whether in desert bright area or urban bright area with less vegetation, the AOD values retrieved by Sen2Cor plug-in are fixed, no spatial distribution and do not conform to the actual situation. In general, compared with the current official products, the deep blue algorithm is suitable for aerosol retrieval in high-brightness areas of Sentinel-2 data，and has obvious advantages in terms of estimation accuracy and spatial distribution trend.     

京津冀冬季气溶胶光学厚度遥感反演

针对传统的暗像元算法难以满足植被稀疏陆表气溶胶遥感监测需求的问题,提出了冬季植被稀疏的京津冀地区气溶胶光学厚度的遥感反演方法。以2016—2018年连续3年1—2月的AQUA/MODIS L1B数据为数据源,采用暗像元算法与深蓝算法结合的方法对冬季京津冀地区的气溶胶光学厚度进行了遥感监测。使用AERONET数据对结果进行了验证,并与MODIS MYD04_L2暗像元-深蓝气溶胶产品进行了对比。结果表明,该算法在冬季京津冀地区的气溶胶监测效果远好于暗像元算法,并与MODIS气溶胶产品表现出了显著的相关性,且有效监测范围更大、空间分辨率更高。根据连续监测结果,分析了京津冀地区冬季气溶胶光学厚度空间分布特征及其影响因素。     

Retrieval of Aerosol Optical Depth based on Himawari-8

To obtain Aerosol Optical Depth (AOD) products with high spatial and temporal resolution and high precision in China. We enhance the AOD retrieval algorithm by applying the MODIS red and blue surface re?ectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOD over both dark and bright surfaces., we retrieve the 10-minute AOD of China from April 2018 to April 2019. The AOD retrievals from the enhanced red-blue ratio algorithm (RB AOD) were validated by the Level 1.5 AERONET (Aerosol Robotic Network) sunphotometer measurements and MOD04_3K AOD , and the retrieval of AOD were compared with the latest AOD product (version 020) released by Himawari-8.The results show that :(1) The AOD retrievals from the enhanced algorithm agreed well with those from the AERONET. Except Baotou station, the correlation coefficient R of the other five stations is between 0.728-0.863, and the percentage of sample points within the range of EE (Expectation of Error) is between 47.7% and 68.6%, which has great advantages over Himawari-8 AOD products.(2) The RB AOD are basically consistent with AERONET AOD in time series trend. the RB AOD results are higher than those of AERONET AOD when AOD > 1.The spring and summer trend of Himawari-8 AOD is relatively consistent with that of AERONET AOD. However, due to the obvious diurnal change of Himawari-8 AOD in autumn and winter, and the diurnal change is relatively large, its trend deviates greatly from AERONET AOD.(3) The spatial distribution of RB AOD is basically consistent with that of MODIS AOD products, and the retrieved results are slightly lower than those of MODIS AOD.In winter, the inversion range of red-blue ratio method is wider than that of MOD04_3K AOD.(4) the red-blue ratio retrieval algorithm has great advantages over Himawari-8 AOD and MOD04_3K in precision and range of retrieval results of high-reflectance surface area in north China in winter and spring.     

Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager

An aerosol retrieval algorithm for the first Geostationary Ocean Color Imager (GOCI) to be launched in March 2010 onboard the Communication, Ocean, and Meteorological Satellite (COMS) is presented. The algorithm retrieves aerosol optical depth (AOD), fine-mode fraction (FMF), and aerosol type in 500 m × 500 m resolution. All the products are retrieved over clear water which is defined by surface reflectance ratio between 640 nm and 860 nm (SRR) less or equal to 2.5, while only AOD is retrieved over turbid water (SRR > 2.5) due to high surface reflectance. To develop optimized algorithm for the target area of GOCI, optical properties of aerosol are analyzed from extensive observation of AERONET sunphotometers to generate lookup table. Surface reflectance of turbid water is determined from 30-day composite of Rayleigh- and gas corrected reflectance. By applying the present algorithm to MODIS top-of-the atmosphere reflectance, three different aerosol cases dominated by anthropogenic aerosol contains black carbon (BC), dust, and non-absorbing aerosol are analyzed to test the algorithm. The algorithm retrieves AOD, and size information together with aerosol type which are consistent with results inferred by RGB image in a qualitative way. The comparison of the retrieved AOD with those of MODIS collection 5 and AERONET sunphotometer observations shows reliable results. Especially, the application of turbid water algorithm significantly increases the accuracy in retrieving AOD at Anmyon station. The sensitivity study between MODIS and GOCI instruments in terms of relative sensitivity and scattering angle shows promising applicability of the present algorithm to future GOCI measurements.     

On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD. We applied the methodology to Landsat 5 Thematic Mapper data in Queensland (QLD) and New South Wales (NSW), Australia, and determined that AOD must be estimated within approximately 0.05 of actual AOD for retrieval of foliage projective cover (FPC) to a precision of 10%. That knowledge was then used to determine the relative merit of using a fixed constant, Aerosol Robotic Network (AERONET) climatology, or dense dark vegetation (DDV) method for estimating AOD in QLD and NSW. It was found that using a fixed AOD of 0.05 allows estimates of FPC within 10% of their true value when the true value of AOD is less than 0.1. Such AOD values account for approximately 90% of all inland observations and 65% of coastal observations as determined by analysis of data obtained from AERONET. Using an AERONET climatology to estimate AOD was found to increase the likelihood of accurate FPC retrieval in coastal locations to 83%, although it should be noted that AERONET data are very sparse. DDV has potential in eastern and central areas for retrieving AOD observations with greater precision than fixed values or climatologies. However, more work is needed to understand the temporal variation of vegetation reflectance before the DDV method can be used operationally.     

Aerosol optical depth retrieval over snow using AATSR data

Aerosol observations over the Arctic are important because of the effects of aerosols on Arctic climate, such as their direct and indirect effects on the Earth's radiation balance and on snow albedo. Although information on aerosol properties is available from ground-based measurements, passive remote sensing using satellite measurements would offer the advantage of large spatial coverage with good temporal resolution, even though, due to light limitations, this is only available during the Arctic summer. However, aerosol optical depth (AOD) retrieval over the Arctic region is a great challenge due to the high reflectance of snow and ice and due to the high solar zenith angle. In this article, we describe a retrieval algorithm using Advanced Along-Track Scanning Radiometer (AATSR) data, a radiometer flying on the European Space Agency (ESA) Environmental Satellite (ENVISAT), which offers two views (near nadir and at 55° forward) at seven wavelengths in the visible thermal-infrared (VIS-TIR). The main idea of the Dual-View Multi-Spectral (DVMS) approach is to use the dual view to separate contributions to reflectance measured at the top of the atmosphere (TOA) due to atmospheric aerosol and the underlying surface. The algorithm uses an analytical snow bidirectional reflectance distribution function (BRDF) model for the estimation of the ratio of snow reflectances in the nadir and forward views, as well as an estimate of the atmospheric contribution to TOA reflectance obtained using the dark pixel method over the adjacent ocean surface, assuming that this value applies over nearby land surfaces in the absence of significant sources across the coastline. An iteration involving all four AATSR wavebands in the visible near-infrared (VIS-NIR) is used to retrieve the relevant information. The method is illustrated for AATSR overpasses over Greenland with clear sky in April 2009. Comparison of the retrieved AOD with AErosol Robotic Network (AERONET) data shows a correlation coefficient of 0.75. The AODs retrieved from AATSR using the DVMS approach and those obtained from AERONET data show similar temporal trends, but the AERONET results are more variable and the highest AOD values are mostly missed by the DVMS approach. Limitations of the DVMS method are discussed. The pure-snow BRDF model needs further correction in order to obtain a better estimation for mixtures of snow and ice.