基于FY3B/MERSI水稻长势监测及其与AQUA/MODIS数据对比分析

为利用中分辨率光谱成像仪FY3B/MERSI数据进行水稻种植面积信息提取及长势监测,首先对FY3B/MERSI与AQUA/MODIS单通道反射率进行对比分析;其次,通过目视解译和遥感监督分类方法提取了环鄱阳湖区双季早稻的空间分布;最后,基于面积识别结果,利用NDVI对环鄱阳湖区双季早稻长势进行监测。结果表明:(1)FY3B/MERSI与AQUA/MODIS水稻种植区红光和近红外通道反射率具有极显著相关性,且MERSI红光波段及近红外波段反射率都略小于对应MODIS反射率;(2)水稻的提取精度总体达到了92%,可以满足其生育期长势遥感监测的需求;(3)FY3B/MERSI和AQUA/MODIS计算的水稻区NDVI存在显著正相关,且二者测得的水稻长势均与实际苗情数据基本吻合,因此,由FY3B/MERSI遥感影像直接得到的水稻苗情分类基本可以满足业务化需要。

Terra MODIS和Aqua MODIS波段反射率及植被指数比较

对Terra MODIS和Aqua MODIS之间单波段反射率及植被指数进行了比较。结果表明:Terra MODIS和Aqua MODIS单波段反射率及植被指数具有极显著的相关性,植被指数较单波段反射率相关性更高些;Terra MODIS单波段反射率值普遍较Aqua MODIS值低,而植被指数值普遍较Aqua MODIS值高;不同时段Terra MODIS和Aqua MODIS单波段反射率及植被指数间差异不同,植被指数在冬季差异最大,而单波段反射率则在夏秋季差异较大;不同植被类型Terra MODIS和Aqua MODIS间植被指数差异总体规律相似,但单波段反射率间差异较为复杂;草甸、草原无论是单波段反射率还是植被指数,Terra MODIS和Aqua MODIS的差异均比其他几种植被类型小,而阔叶林和一年两熟作物则差异相对大些。

利用Terra和Aqua双星MODIS数据协同研究沙尘暴

利用2005-04-28上午星Terra和下午星Aqua白天和晚上共4景MODIS数据对影响华北地区的一次沙尘暴进行了研究。对于白天的MODIS数据,利用热红外亮度温度差值法结合可见光近红外波段反射率阈值法,可以很好地把沙尘与背景地物和云分离开来;对于夜晚的MODIS数据,利用热红外亮度温度差值法也能较好地提取沙尘信息。研究表明,MODIS数据在沙尘暴等灾害研究方面具有很大的潜力。

基于遗传算法综合Terra/Aqua MODIS热红外数据反演地表组分温度

混合像元组分温度相对来说更有应用价值,而多角度热红外遥感的发展推动了混合像元组分温度反演基础和方法的发展.根据前期数值模拟得到Terra和Aqua卫星上的MODIS测量可以认为是同一卫星在两个不同观测时间和观测角度上的测量,综合利用Terra和Aqua卫星上的MODIS数据反演混合像元内土壤和植被组分温度.根据混合像元热红外辐射模型,利用遗传算法,分别模拟Terra卫星MODIS的32和33通道,以及Terra和Aqua卫星上MODIS的32通道辐射反演了河北怀来试验区范围内植被覆盖率、土壤组分温度和比辐射率、植被组分温度和比辐射率等表面参数.通过与实测数据进行比较,综合利用上午Terra和下午Aqua卫星32通道数据反演的上午植被组分温度与地面同步测量温度偏差在1℃内,而利用上午Terra卫星32和33通道数据反演的上午植被组分温度与地面同步测量值偏差在1.4℃内.尽管利用双星数据反演的组分温度精度相对较高,但针对同一个像元,两个方案反演的结果有一定偏差.

基于MODIS/Aqua的胶州湾及青岛近海叶绿素a浓度年变化特征分析

利用2002-2013年MODIS/Aqua的Level 1B数据,经标准大气校正算法和叶绿素a浓度[chl-a]波段比经验算法(OC2M-HI),获得近12 a的胶州湾及青岛近海海域晴空的MOIDS/Aqua叶绿素a浓度。根据GOCI/COMS和MODIS/Aqua叶绿素a浓度产品在胶州湾及青岛近海交叉比较的结果[1],对2002-2013年的MODIS/Aqua[chl-a]进行了修正。基于修正后的MODIS/Aqua[chl-a]分析了胶州湾及青岛近海的叶绿素a浓度年变化特征。该海域的叶绿素a浓度大致呈现北高南低,湾内高于近海的特点,且每年空间分布趋势基本一致;近12 a的[chl-a]呈小的上升趋势,胶州湾的上升趋势大于青岛近海。胶州湾跨海大桥建设前,其附近区域叶绿素a浓度以0.47μg/L/year的趋势上升,基值为2.62μg/L;大桥开建及通车后,其附近叶绿素a浓度年变化趋势不明显,但基值明显增大(4.00μg/L)。

南海海域MODIS-Aqua叶绿素浓度产品的精度对比和区域性算法修正

利用2004～2012年在南海获得的9个航次的实测Chl-a数据,采用NASA标准业务化算法OC3和针对低Chl-a水体所发展的最新算法OCI反演获得了相应的MODIS-Aqua Chl-a产品.通过建立实测与遥感产品的时空匹配数据对,开展了Chl-a产品的适用性评估,并对比分析了上述两种算法的性能.在此基础上,利用南海实测遥感反射率（Rrs（λ））和MODIS-AquaRrs（λ）产品以及相应实测Chl-a的匹配数据集,分别对算法OC3和OCI进行了区域性修正.结果显示：基于算法OC3和OCI反演所得的MODIS-Aqua Chl-a产品值均高估了实测值,平均绝对误差（APD）的精度分别为56.30％和42.58％,且算法OCI可明显改善低Chl-a水体（＜0.25 mg· m^-3）的反演精度;采用南海MODIS-AquaRrs（λ）产品与实测Chl-a匹配数据集（N=82）修正后的区域性算法NOC3和NOCI的精度均有不同程度提高,APD精度分别为37.85％和36.74％;采用现场实测Rrs（λ）与Chl-a匹配数据集（N=123）进行区域性修正后的算法INOC3和INOCI的APD精度分别为36.61％和37.79％,上述两种方案精度较为接近.因此,对于南海海域而言,算法的区域性修正对于改善MODIS-Aqua Chl-a产品精度非常重要.

Extraction of Desertification Information in Hulun Buir Based on MODIS Image Data

[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.

MODIS-Aqua漫射衰减产品K_d(490)在南海海域的精度对比

基于遥感手段准确估算水体漫射衰减系数Kd(490),可为有效评价近岸海域水质、准确估算真光层深度提供有力数据和方法支撑。利用2004~2012年在南海海域获得的9个航次的实测Kd(490)数据,通过建立实测与遥感产品的时空匹配数据对,获取与Kd(490)实测数据相对应的MODIS-Aqua遥感反射率(Rrs(λ))和叶绿素a浓度(Chl-a)产品,分别采用算法Muller,Werdell,Morel,Lee和MODIS业务化算法KD2M对南海海域Kd(490)遥感产品进行精度评估与对比;在此基础上,考虑到南海海域水体的特异性,基于相应的实测Rrs(λ)和Chl-a数据集,利用上述算法对Kd(490)反演产品再次进行精度评估与对比,进而探讨Kd(490)产品的误差来源和算法区域化修正的必要性。基于MODIS-Aqua遥感产品,利用上述五种算法获得的MODIS-Aqua Kd(490)产品值与实测值的平均相对误差(APD)分别是20.12%,29.22%,17.98%,22.61%和21.61%,其中,Morel算法最优,且相关系数最高达0.5;基于相应实测数据,利用上述算法反演获得的Kd(490)产品精度均有不同程度的提高,特别是算法Lee和Werdell,APD分别为17.86%和17.97%,提高了5%~11%,最优算法为Morel,其APD可达15.44%。合理选取评估算法及进一步开展算法的区域性修正对于准确估算南海海域Kd(490)产品是非常必要的。

Effects of Freezing Disaster on Green-up Date of Vegetation Using MODIS/EVI Time Series Data

In the field of global changes, the relationship between plant phenology and climate, which reflects the response of terrestrial ecosystem to global climate change, has become a key subject that is highly concerned. Using the moderate-resolution imaging spectroradiometer (MODIS)/enhanced vegetation index(EVI) collected every eight days during January- July from 2005 to 2008 and the corresponding remote sensing data as experimental materials, we constructed cloud-free images via the Harmonic analysis of time series (HANTS). The cloud-free images were then treated by dynamic threshold method for obtaining the vegetation phenology in green up period and its distribution pattern. And the distribution pattern between freezing disaster year and normal year were comparatively analyzed for revealing the effect of freezing disaster on vegetation phenology in experimental plot. The result showed that the treated EVI data performed well in monitoring the effect of freezing disaster on vegetation phenology, accurately reflecting the regions suffered from freezing disaster. This result suggests that processing of remote sensing data using HANTS method could well monitor the ecological characteristics of vegetation.

Review of large scale crop remote sensing monitoring based on MODIS data

China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.

联合Terra/Aqua MODIS多角度多光谱数据反演冬小麦叶面积指数

Terra与Aqua双星搭载的MODIS传感器可实现每日上下午分别对同一地点观测一次,并且由于卫星轨道漂移形成累积连续多天的多角度观测特点,加上多通道的光谱响应,极大地丰富了地表目标的观测信息,为LAI等地表参数的实时准确反演提供了可能。该文利用MODIS双星高质量的连续多天的多波段地表反射率数据,结合PROSAIL(PROSPECT+SAIL,properties spectra+scattering by arbitrarily inclined leaves)模型和查找表方法反演冬小麦LAI,并与MODIS LAI产品及野外采样点实测LAI对比,结果表明,联合双星高质量的多角度多波段数据能够较准确反演冬小麦LAI,其反演结果无论从空间分布还是时序变化特征来讲,较MODIS LAI产品更符合实际情况,也更接近地面实测值。该文的研究为充分利用MODIS数据的角度和光谱信息反演小麦等农作物的LAI提供了一定的借鉴。

Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data

By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.

The Regional Surface Heating Field over the Heterogeneous Landscape of the Tibetan Plateau Using MODIS and In Situ Data

In this study, a parameterization scheme based on Moderate Resolution Imaging Spectroradiometer （MODIS） data and in-situ data was tested for deriving the regional surface heating field over a heterogeneous landscape. As a case study, the methodology was applied to the whole Tibetan Plateau （TP） area. Four images of MODIS data （i.e., 30 January 2007, 15 April 2007, 1 August 2007, and 25 October 2007） were used in this study for comparison among winter, spring, summer, and autumn. The results were validated using the observations measured at the stations of the Tibetan Observation and Research Platform （TORP）. The results show the following： （1） The derived surface heating field for the TP area was in good accord with the land-surface status, showing a wide range of values due to the strong contrast of surface features in the area. （2） The derived surface heating field for the TP was very close to the field measurements （observations）. The APD （absolute percent difference） between the derived results and the field observations was 〈10%. （3） The mean surface heating field over the TP increased from January to April to August, and decreased in October. Therefore, the reasonable regional distribution of the surface heating field over a heterogeneous landscape can be obtained using this methodology. The limitations and further improvement of this method are also discussed.

Vegetation NPP Distribution Based on MODIS Data and CASA Model——A Case Study of Northern Hebei Province

Net Primary Productivity （NPP） is one of the important biophysical variables of vegetation activity, and it plays an important role in studying global carbon cycle, carbon source and sink of ecosystem, and spatial and temporal distribution of CO2. Remote sensing can provide broad view quickly, timely and multi-temporally, which makes it an attractive and powerful tool for studying ecosystem primary productivity, at scales ranging from local to global. This paper aims to use Moderate Resolution Imaging Spectroradiometer （MODIS） data to estimate and analyze spatial and temporal distribution of NPP of the northern Hebei Province in 2001 based on Carnegie-Ames-Stanford Approach （CASA） model. The spatial distribution of Absorbed Photosynthetically Active Radiation （APAR） of vegetation and light use efficiency in three geographical subregions, that is, Bashang Plateau Region, Basin Region in the northwestern Hebei Province and Yanshan Mountainous Region in the Northern Hebei Province were analyzed, and total NPP spatial distribution of the study area in 2001 was discussed. Based on 16-day MODIS Fraction of Photosynthetically Active Radiation absorbed by vegetation （FPAR） product, 16-day composite NPP dynamics were calculated using CASA model; the seasonal dynamics of vegetation NPP in three subreglons were also analyzed. Result reveals that the total NPP of the study area in 2001 was 25.1877 × 10^6gC/（m^2.a）, and NPP in 2001 ranged from 2 to 608gC/（m^2-a）, with an average of 337.516gC/（m^2.a）. NPP of the study area in 2001 accumulated mainly from May to September （DOY 129-272）, high NIP values appeared from June to August （DOY 177-204）, and the maximum NPP appeared from late July to mid-August （DOY 209-224）.

基于MODIS-MOD09Q1数据的雅氏落叶松尺蠖灾区提取及其适生气候特征分析

通过MODIS-MOD09Q1遥感数据,使用归一化植被指数(NDVI)、比值植被指数(RVI)和近红外波段反射率(NIR)3个易获取且与虫害发生程度具有响应的指标,划分灾区受害度等级植被指数的变化,构建虫害综合指数(PCI)模型,实现雅氏落叶松尺蠖(Eeannis jacobssoni)灾区信息快速提取。在此基础上,借助气温和降水量数据,结合GIS空间叠加分析方法,揭示了害虫适生气候特征。结果表明,利用虫害综合指数能够准确提取害虫灾区严重度信息,其总体精度和Kappa系数分别为85.00%和0.81;雅氏落叶松尺蠖适宜于冬季、春季降水量较少、夏季降水量较多,气温不宜太高的气候,这与其生物学特性相吻合。该气候与大兴安岭林区相似,入侵风险较大,应引起中国林业部门的高度重视。

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS

Clouds have important effects on the infi＇ared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var （Global and Regional Assimilation and Prediction Enhanced System）, cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS Ⅱ: IMPACTS ON RAINSTORM FORECASTING

Satellite observations provide large amount of information of clouds and precipitation and play an important role in the forecast of heavy rainfall.However,we have not fully taken advantage of satellite observations in the data assimilation of numerical weather predictions,especially those in infrared channels. It is common to only assimilate radiances under clear-sky conditions since it is extremely difficult to simulate infrared transmittance in cloudy sky.On the basis of the Global and Regional Assimilation and Prediction Enhanced System 3-dimensional variance(GRAPES-3DVar),cloud liquid water content, ice-water content and cloud cover are employed as governing variables in the assimilation system.This scheme can improve the simulation of infrared transmittance by a fast radiative transfer model for TOVS (RTTOV)and adjust the atmospheric and cloud parameters based on infrared radiance observations.In this paper,we investigate a heavy rainfall over Guangdong province on May 26,2007,which is right after the onset of a South China Sea monsoon.In this case,channels of the Moderate Resolution Imaging Spectroradiometer(MODIS)for observing water vapor(Channel 27)and cloud top altitude(Channel 36)are selected for the assimilation.The process of heavy rainfall is simulated by the Weather Research and Forecasting(WRF)model.Our results show that the assimilated MODIS data can improve the distribution of water vapor and temperature in the first guess field and indirectly adjust the upper-level wind field.The tendency of adjustment agrees well with the satellite observations.The assimilation scheme has positive impacts on the short-range forecasting of rainstorm.

简析MODIS(Terra/Aqua)数据在福建省森林火灾监测中的应用

从目前福建省卫星林火监测应用实际出发,通过几种常用气象卫星功能介绍和对比,阐述MODIS(Terra/Aqua)数据在森林火灾监测中的应用优势,并简析MODIS(Terra/Aqua)数据在福建省林火监测中的应用现状。

利用MODIS数据和BP神经网络重构美国区域尺度大豆日光诱导叶绿素荧光

[目的和意义]原始星载日光诱导叶绿素荧光(Sunlight-induced Chlorophyll Fluorescence,SIF)数据存在足迹离散、时空分辨率低等缺陷,针对这些问题许多研究进行了SIF重构,但大多数重构后的新型SIF数据分辨率仍较低,难以应用到精细尺度农业领域,且部分高精度SIF重构数据并非基于原始卫星SIF数据重构。OCO-2 SIF原始数据空间分辨率高(1.29 km×2.25 km),植被异质性低,对区域尺度高分辨率作物SIF重构具备突出价值。[方法]选取美国区域尺度大豆为研究对象,利用原始OCO-2 SIF和MODIS产品进行高分辨率大豆SIF重构,通过组合多个卫星轨迹经过的大豆种植区,提高SIF样本总量,与增强植被指数(Enhanced Vegetation Index,EVI)、光合有效辐射分量(Fraction of Photosynthetically Active Radiation,FPAR)和土地表面温度(Land Surface Temperature,LST)等预测因子足迹匹配后构建多源遥感数据集,代入BP神经网络训练模型,进而生成区域尺度空间连续且具有较高时空分辨率(8 d、500 m)的重构SIF数据集(BPSIF)。[结果和讨论]加入EVI,FPAR和LST的SIF重构模型R^(2)达0.84,利用总初级生产力(Gross Primary Productivity,GPP)数据对BPSIF进行质量评价,OCO-2 SIF与GPP的Pearson相关系数为0.53,而BPSIF与GPP相关系数提升到0.8,表明本研究生成的BPSIF数据集更加可靠。[结论]研究成果有望为区域尺度大豆作物SIF研究提供理论依据和数据支撑。

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product （collection 6_L2） obtained from the Moderate Resolution Imaging Spectroradiorneter （MODIS） sensors on board the Terra （MOD06） and Aqua （MYD06） satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 （12 years） with a spatial resolution of 5 km × 5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.