Methane emission modelling using MODIS thermal and optical data: A case study on Gujarat

Wetlands are one of the most important sources of atmospheric methane (CH₄) contributing about 22% to the global methane budget. But to improve estimates of CH₄ emission at regional and global scales there is a need to observe the sources such as wetlands frequently and develop process-based models. In this regard, wetland inventory using satellite remote sensing data has conventionally been carried out by analysis of optical data. Due to thermal inertia differences emittive thermal channels data has shown promise to provide highly critical information about wetlands such as water spread, aquatic vegetation and mud flats etc. Thermal channels data of MODIS (Moderate Resolution Imaging Spectroradiometer) sensor with a spatial resolution of 1km and swath of 2330 km is emerging as the key source of remote sensing data for global/ regional wetland estimation and assessment of green house gas emission. In the present study MODIS thermal channels (31 and 32) and optical channels (1,2, and 3) data have been used for evaluating methane emission from wetlands in Gujarat. An empirical model based on temperature and productivity has been used to investigate the response of methane emission from different sources. Model has the potential to estimate country level methane emission based on satellite remote sensing in conjunction with collateral data/information. In this study. MODIS data of two dates pertaining to Gujarat have been analyzed and results compared with respect to methane emission.     

3-Decadal Changes in Sea Ice Melting and Freezing Pattern in Antarctica Using SSM/I Data

The present study aims to estimate the long term changes in sea ice areal extent (SIAE) around Antarctica using Special Sensor Microwave Imager (SSM/I) satellite data (1988–2011). Daily, weekly and monthly brightness temperature (T_B) maps of Antarctica have been generated. Temporal variation in monthly averaged T_B values of sea ice have been analyzed and an increasing trend is observed in T_B. Sea ice concentration (SIC) values are estimated and the concentration class 0.85–0.95 is observed as the main contributor in sea ice areal extent during the Antarctic peak winter month i.e. September and October. SIC changes rapidly from October onwards and shows fast melting of sea ice, however, from April start of freezing has been observed. Continuous increase in SIAE is observed from February to September in all the analyzed years (1988–2011) data. In this observation period maximum SIAE is observed in September 1989 and minimum in February 1993. The Moderate Resolution Imaging Spectroradiometer (MODIS) data is used for the validation of SIAE values. SSM/I and MODIS estimated SIAE values are compared (R²?=?0.98) and a relation between them is established by performing regression analysis. This relation is further used for the estimation of corrected SIAE values. The monthly average sea ice extent maps of last three decades have been analyzed and a shift in onset of freezing of sea ice has been observed. The period of frozen sea has also been observed as decreased.     

Scrutinising MODIS and GIMMS Vegetation Indices for Extracting Growth Rhythm of Natural Vegetation in India

Satellite derived vegetation vigour has been successfully used for various environmental modeling since 1972. However, extraction of reliable annual growth information about natural vegetation (i.e., phenology) has been of recent interest due to their important role in many global models and free availability of time-series satellite data. In this study, usability of Moderate Resolution Imaging Spectro-radiometer (MODIS) and Global Inventory Modelling and Mapping Studies (GIMMS) based products in extracting phenology information about evergreen, semi-evergreen, moist deciduous and dry deciduous vegetation in India was explored. The MODIS NDVI and EVI time-series data (MOD13C1: 5.6 km spatial resolution with 16 day temporal resolution—2001 to 2010) and GIMMS NDVI time-series data(8 km spatial resolution with 15 day temporal resolution—2000 to 2006) were used. These three differently derived vegetation indices were analysed to extract and understand the vegetative growth rhythm over different regions of India. Algorithm was developed to derive onset of greenness and end of senescence automatically. The comparative analysis about differences in the results from these products was carried out. Due to dominant noise in the values of NDVI from GIMMS and MODIS during monsoon period the phenology rhythm were wrongly depicted, especially for evergreen and semi-evergreen vegetation in India. Hence, care is needed before using these data sets for understanding vegetative dynamics, biomass cestimation and carbon studies. MODIS EVI based results were truthful and comparable to ground reality. The study reveals spatio-temporal patterns of phenology, rate of greening, rate of senescence, and differences in results from these three products.     

Methane Emission Pattern of Indian Rice-Ecosystems

The rice land is linked to the climate change due to its methane emission potential. The systems of growing rice and associated soil and crop management practices that have evolved are varied and complex. However, from the methane emission point of view, water regime is a crucial parameter. According to IPCC guidelines the rice ecosystem need to be categorized into four strata for methane emission study. The remote sensing based stratification map previously developed was used for in-situ weekly/monthly measurements of methane emission from the representative ecosystems, samples were collected and analysed using gas chromatography following the IPCC standards for three consecutive years; 2003, 2004 and 2005. This paper highlights the results of methane emission measurement and pattern from rice lands of India based on in-situ measurements. The CH₄ emission pattern of irrigated crop in dry season showed a steady increase in the beginning which peaks during flowering stage, decreasing gradually thereafter. The results were consistent for different varieties and across the years. The emission pattern of irrigated wet season crop showed two peaks. The emission pattern also showed the influence of crop variety as well as year (of observation). The mean emission coefficient derived from all categories and all samples (n = 471) weighted for the Indian rice crop was 74.05 + 43.28 kg/ha.     

Snow and ice mask for the MODIS aerosol products

Atmospheric products have been derived operationally from multichannel imaging data collected with the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua spacecraft. Preliminary validations of the products were previously reported. Through analysis of more extensive time-series of MODIS aerosol products (Collection 4), we have found that the aerosol products over land areas are slightly contaminated by snow and ice during the springtime snow-melting season. We have developed an empirical technique using MODIS near-infrared channels centered near 0.86 and 1.24 /spl mu/m and a thermal emission channel near 11 /spl mu/m to mask out these snow-contaminated pixels over land. Improved aerosol retrievals over land have been obtained. Sample results from application of the technique to MODIS data acquired over North America, northern Europe, and northeastern Asia are presented. The technique has been implemented into the MODIS Collection 5 operational algorithm for retrieving aerosols over land from MODIS data.     

Characterisation of Residue Burning from Agricultural System in India using Space Based Observations

Biomass burning is a global phenomenon with agriculture residue burning having a sizeable share. Biomass burning is a major source of emission of green house gases (GHGs). Thus the space-based observations of global distribution of fire form a key component of climate change studies. This study is a step towards understanding the spatio-temporal occurrence of agricultural residues burning in Indo-Gangetic plains of India using fire products from space borne satellites. The 3 years daily active fire data of MODIS (Aqua/Terra) from August, 2006 to July, 2009 have been used in this study. The data analysis showed that out of total fire events, around 69% contribution comes from agricultural areas and remaining (31%) comes from non-agricultural areas. This is mainly due to the intensive cultivation in this belt. The characterisation analysis revealed that, 84% of agriculture residues burning is from Rice-Wheat system (RWS) alone and remaining 16% in other types of crop rotations. The fire incidents were very high in October–December (55%) compared to that in March–May (36%), further indicating that burning of rice residue is more prevalent than that of wheat.     

Hydrological modelling of a small watershed using satellite data and gis technique

A distributed parameter model Soil and Water Assessment Tool (SWAT) has been tested on daily and monthly basis for estimating surface runoff and sediment yield from a small watershed “Chhokeranala” in eastern India using satellite data and Geographical Information System (GIS). Several maps like watershed and sub-watershed boundaries, drainage network, landuse/cover and soil texture have been generated. The SWAT model has been verified for the initial phase of monsoon season in the year 2002 using daily rainfall and air temperature. Performance of the model has been also evaluated to simulate the surface runoff and sediment yield on sub-watershed basis for two months (July-August 2002). The results show a good agreement between observed and simulated runoff and sediment yield during the study period. Capability of the model for generating rainfall has been evaluated for 10 years (1992 - 2001) period. The model simulated daily rainfall shows close agreement with the observed rainfall. The present results show that the SWAT model can be used for satisfactory simulation of daily and monthly rainfall, runoff and sediment yield.     

Prototyping an artificial neural network for burned area mapping on a regional scale in Mediterranean areas using MODIS images

Each year thousands of ha of forest land are affected by forest fires in Southern European countries such as Spain. Burned area maps are a valuable instrument for designing prevention and recovery policies. Remote sensing has increasingly become the most widely used tool for this purpose on regional and global scales, where a large variety of techniques and data has been applied. This paper proposes a semiautomatic method for burned area mapping on a regional scale in Mediterranean areas (the Iberian Peninsula has been used as a study case). A Multi-layer Perceptron Network (MLPN) has been designed and applied to MODIS/Terra Surface Reflectance Daily L2G Global 500m SIN Grid multitemporal composite monthly images. The compositing criterion was based on maximum surface temperature. The research covered a six year period (2001–2006) from June to September, when most of the forest fires occur. The resulting burned area maps have been validated using official fire perimeters and compared with MODIS Collection 5 Burned Area Product (MCD45A1). The MLPN shown as an effective method, with a commission error of 29.1%, in the classification of the burned areas, while the omission error was of 14.9%. The results were compared with the MCD45A1 product, which had a slightly higher commission error (30.2%) and a considerably higher omission error (26.2%), indicating a high underestimation of the burned area.     

FY-3C VIRR大气可降水产品生成和检验

风云三号C星(FY-3C)可见光红外扫描辐射计(VIRR)两个红外分裂窗通道数据生成的晴空大气可降水(TPW)产品已投入业务使用。本文介绍了该产品的生成方法,并从产品精度和稳定性两个方面评价产品质量。与MODIS Terra TPW的月平均数据对比,FY-3C VIRR TPW能正确反应大气可降水的全球分布。与2015年3月—4月的全球探空数据对比,FY-3C VIRR TPW均方根误差为5.36 mm,相对误差在水汽值大于30 mm时在20%以内,并且夜间产品精度优于白天。相比于MODIS红外TPW产品与探空数据的误差,FY-3C TPW精度略好。计算2015年1月至2016年7月FY-3C VIRR TPW产品相对探空数据的月均方根误差,19个月均方根误差的标准差是0.54 mm,小于同期MODIS Terra TPW均方根误差的标准差,说明FY-3C VIRR TPW产品在检验时期内更稳定。FY-3C VIRR TPW产品精度较高且质量稳定,具备广泛应用能力。     

A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km² river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.     

多时相MODIS影像水田信息提取研究

水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。     

基于MODIS产品影像及陆面能量平衡系统(SEBS)的河北平原区域蒸散估算

地表蒸散是区域水文循环的重要组成部分之一,传统的地表蒸散估算一般基于点上的气象观测数据,当用于区域地表蒸散评价时,具有一定的局限性.随着遥感技术的发展,利用遥感影像能够对大区域进行观测的优势进行区域地表蒸散估算已成为可能.地表能量平衡系统(SEBS)是根据地表能量平衡估算地表蒸散量的一种方法,该方法由于提出了地表能量传输过程中关键制约因素热传导粗糙度的估算模型而在遥感蒸散计算方面具有较高的精度.本文在SEBS模型的基础上,以河北平原为例,采取中分辨率成像光谱辐射仪(MODIS)产品影像,根据研究区下垫面的实际情况进行了参数估算,进行了区域实际蒸发蒸腾量计算及模型精确度评价,并在SEBS模型的基础上,提出了新的"标准化温度差-反照率"特征空间分析方法,对研究区内地表土壤水分现状进行了评价;最后,对河北平原地表蒸散时空分布进行了分析.计算结果表明,在所选取的晴空乌云条件下,根据SEBS模型计算所得的地表蒸散与研究区内利用大型承重式蒸渗仪所测量的地表实际蒸散量具有很好的一致性,说明SEBS模型在遥感蒸散计算方面据有较高的可信度.然而,由于地表蒸散的遥感估算是以所获取的遥感影像单元为基础,计算误差不可避免,尤其是使用低分辨率遥感影像的时候,每个影像单元所反映的地表蒸散为单元内各种地表覆盖的综合反映,当与只反映一种地表覆盖蒸散的大型承重式蒸渗仪测量结果进行比较时,误差是显而易见的,因此,若着重考虑模型精度验证,尚需在以后的研究中考虑使用高分辨率的遥感影像.地表土壤水分或湿度状况是地表能量交换及蒸散发生的主要控制因素.利用地表温度与植被指数的关系对区域地表湿度状况进行监测在实践中被广泛利用,然而,由于不用地区地表属性的千差万别,当利用这种方法进行区域地表湿度评价时,"地表温度-植被指数"特征空间的边界很难确定.本文在SEBS模型的基础上,提出新的"标准化温度差-反照率"特征空间分析方法,对研究区内地表土壤水分现状进行评价,结果显示,由于在SEBS模型中考虑了干限和湿限两种极端情况下的能量平衡,"标准化温度差.反照率"特征空间的边界问题很容易被确定,利用修正的特征空间,可以对区域土壤水分或湿度状况进行客观的评价.下垫面的几何特征参数的精确反演是进行地表能量平衡模拟的基础,目前多根据有关经验公式利用遥感影像进行下垫面几何特征参数估算,由于已有的经验公式多基于不同研究区域获得,当应用到新的研究区时,其具体参数需要进一步调整,以获得对下垫面的几何特征的精确描述.由于缺乏相关实测资料,本研究中利用经验公式进行下垫面几何特征参数,误差不可避免,需要在以后的研究中进行深入探索.另外,地表蒸散的计算只是进行区域水资源评价、农业节水措施评价以及全球变化等研究的一个中间环节,需要在以后的研究中根据具体研究目的进行进一步研究.     

融合Landsat ETM+和MODIS数据估算高时空分辨率地表短波反照率

提出一种通过融合高空间低时间分辨率、低空间高时间分辨率地表短波反照率,来估算高时空分辨率地表短波反照率的方法。首先,利用Landsat ETM+数据,通过窄波段到宽波段的转换得到一景或多景空间分辨率较高的ETM+蓝天空短波反照率;然后,在MODIS短波反照率产品基础上,以天空光比例因子为权重,得到空间分辨率较低的MODIS蓝天空短波反照率;最后,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合ETM+短波反照率的空间变化信息和MODIS短波反照率的时间变化信息,得到高时空分辨率的地表短波反照率。针对STARFM模型在异质性区域估算精度降低的问题,通过以MODIS反照率影像各像元的端元(各地类)反照率取代MODIS像元反照率来提取时空变化等信息参与STARFM模型的融合过程,达到提高异质性区域估算精度的目的。结果显示,直接利用STARFM模型估算得到的高空间分辨率地表短波反照率处在合理的精度范围内(RMSE0.02),用改进后的STARFM模型估算得到的异质性区域短波反照率和真实ETM+短波反照率间的相关系数增大。     

Monitoring Horizontal and Vertical Cropping Pattern and Dynamics in Bihar over a Decade (2001–2012) Based on Time-Series Satellite Data

Increasing population and natural disasters like drought, flood, cyclone etc., has impacted global agriculture area and hence continuously modifying cropping pattern and associated statistics. The present study analysed agriculture dynamics over one of the densely populated and disaster prone state (Bihar) in India and derived vital statistics (single, double and triple cropping area, and monthly, seasonal, annual and long term status at the state and district level) for the years 2001–2012. The study used time-series MODIS vegetation index (EVI; MOD13A2, 1 km, 16 day, 2001–2012), MODIS annual Land Cover product (MCD12Q1, 500 m, 2001–2012) and Global Land Cover map (Scasia_V4, 1 km, 2000; Globcover_V2.2, 300 m, 2005/2006 and V2.3, 2009, 300 m), and extracted horizontal (i.e., area change) and vertical (i.e., cropping intensification) agriculture change pattern. The results were inter-compared, and validated using government reports as well as with high spatial resolution data (IRS-LISS III 23.5 m). From 2001–2006 to 2007–2012, the net horizontal and vertical change in agriculture area is +145.24 and +907.82 km², respectively, and net change in seasonal crop area (winter, summer and monsoon) is +959.21, +1009.84 and ?1061.64 km², respectively. The districts which are located along the eastern part of Ganges experienced maximum positive changes and the districts along Gandak river in the north-western part of the study area experienced maximum negative changes. Overall, the study has quantified and revealed interesting space–time agriculture change patterns over 12 years including impacts caused by droughts and floods in the study area.     

FY-3B/MWRI和Aqua/AMSR-E海冰密集度比较及印证

本文对2011-07-01—2011-09-30风云三号B星(FY-3B)搭载的微波成像仪MWRI(Microwave Radiometer Imager)和Aqua卫星搭载的微波扫描辐射计AMSR-E(Advanced Microwave Scanning Radiometer for Earth Observing System)观测数据获取的海冰密集度产品进行比较及印证。首先,逐日比较FY-3B/MWRI和Aqua/AMSR-E区域平均海冰密集度;其次,逐月比较FY-3B/MWRI和Aqua/AMSR-E月平均海冰密集度;最后,使用Aqua卫星搭载的中等分辨率成像光谱辐射计MODIS数据进行印证。MWRI和AMSR-E比较结果为(1)MWRI与AMSR-E逐日区域平均海冰密集度变化趋势一致,MWRI海冰密集度均高于AMSR-E,7—9月MWRI与AMSR-E逐日平均偏差月平均值分别为8.55%、7.67%、2.58%,逐日标准差月平均值分别为12.16%、12.08%、10.43%,二者差异逐月减小。(2)MWRI与AMSR-E月平均海冰密集度差呈现逐月递减趋势,7—9月MWRI与AMSR-E逐月平均偏差分别为7.37%、6.53%、1.51%,逐月标准差分别为4.61%、4.36%、3.64%,MWRI与AMSR-E差异逐月减小的原因是二者在密集度较低的边缘区域差异较大,而夏季随着边缘区域海冰的融化,二者差异逐渐减小。MWRI和AMSRE海冰密集度与MODIS印证结果为:(1)密集度小于95%情况下,MWRI与AMSR-E海冰密集度均比MODIS偏高,AMSR-E更接近MODIS,MWRI高估,误差较大。(2)密集度大于等于95%情况下,MWRI与AMSR-E海冰密集度均比MODIS偏低,AMSR-E偏低更多,MWRI结果更好。     

基于地面观测资料的MODIS云量产品订正

通过对MODIS云量数字产品与气象站云量观测资料的对比分析,发现两者间存在较大偏差。本文以气象站观测云量资料为基准,提出了差值订正、比值订正、差值混合订正、比值混合订正和归一化混合订正5种MODIS云量数字产品的订正方法。结果表明:5种订正方法均有效,其中比值订正法最简单易行,且效果最好,是基于地面观测资料MODIS云量数字产品的最优订正方法。订正后的MODIS云量与气象站观测云量在空间分布特征和数值上都非常吻合。加密站验证结果表明:各月绝对误差平均值均小于5%。本研究为利用地面观测资料订正相关卫星数字产品提供了借鉴方法,有效发挥了卫星空间连续观测的优势,对高原、荒漠和山地等地面测站稀缺地区的相关研究具有重要意义。     

A new method for retrieving band 6 of aqua MODIS

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key research instrument for the NASA Earth Observing System (EOS) mission. It was successfully launched onboard the Terra satellite in December 1999 and Aqua satellite in May 2002. Both MODIS instruments have been working well except that 15 of the 20 detectors in Aqua MODIS band 6 (1.628-1.652 /spl mu/m) are either nonfunctional or noisy. The striping in Aqua MODIS band 6 caused by its nonfunctional or noisy detectors has been a serious problem for MODIS snow products, which use band 6 primarily for snow detection. MODIS scientists have been using Aqua MODIS band 7 (2.105-2.155 /spl mu/m) instead of band 6 for computing the normalized difference snow index to continue detecting global snow coverage. The main objective of this letter is to retrieve Aqua MODIS band 6 using other bands based on their relationships in Terra MODIS. The band retrieval approach proposed in this letter is also very useful for the next generation of MODIS sensor-the Visible/Infrared Imager/Radiometer Suite (VIIRS) band M10 proxy data generation. Such proxy data can support the VIIRS prelaunch end-to-end testing, postlaunch calibration/validation, and data quality checking.     

Above Ground Forest Phytomass Assessment in Southern Gujarat

Spectral modeling of above ground biomass (AGB) with field data collected in 48 field sites representing moist deciduous forest in Surat district is reported. Models were generated using LISS-III and MODIS data. The plot-wise field data was aggregated to MODIS pixel (250 m) using area weightages of forest/vegetation. The study reports that above ground phytomass varied from 6.13 t/ha to 389.166 t/ha while AGB phytomass estimated using area-weights for sites of 250×250 m, ranged from 5.534 t/ha to 134.082 t/ha. The contribution of bamboo in AGB has been found very high. The analysis indicated that the highest correlation between AGB phytomass and red band (R) of MODIS satellite data of October was (R²=0.7823) and R²=0.6998 with both NDVI of October data as well as NDVI_max. High correlation (R²=0.402) with IR band of February month was also found. The phytomass range obtained by using MODIS data varies from 0.147 t/ha to 182.16 t/ha. The mean biomass is 40.50 t/ha. Total biomass is 31.44 Mt. The mean Carbon density is 19.44 tC/ha in forest areas. The study is validation of region-wise spectral modeling approach that will be adopted for mapping vegetation carbon pool of the India under National Carbon Project of ISRO-Geosphere Biosphere Programme.     

A simple surface energy balance to derive evapotranspiration from remote sensing and conventional meteorological observations

Monthly mean measurements of surface temperature, albedo and normalized difference vegetation index (NDVI) of NOAA AVHRR are processed for Maharashtra. These data are used in combination with monthly average surface meteorological observations in a surface energy balance model to estimate monthly mean actual evapotranspiration (AET) from different climatic zones of Maharasthra,. India. AET is estimated between April and December months for two contrasting monsoons in 1992 and 1995. Estimates reasonably agree with pan evaporation data during growing season and with AET estimated from water balance procedure. AET is low in semi-arid dry land areas of central Maharashtra and significantly high in the humid-perhumid western ghat region and subhumid eastern Maharashtra region. The modeled evapotranspiration show the influence of seasonal vegetation in different climatic zones from the region. The method can be used to obtain large-scale evapotranspiration with minimum ground observations.     

MISR和MODIS二向性反射数据产品的对比分析

利用4种地表类型共16个地面站点的MISR 9个角度反射率数据产品和MODIS反射率数据产品(MOD09A1), 对比两种传感器获取的地表方向反射观测数据和用BRDF模型参数产品计算的方向反射数据, 分析了这两种BRDF模型参数的外推能力。研究结果表明: (1) MISR和MODIS BRDF模型参数数据产品都具有一定的对观测数据以外方向反射的外推能力, 相比用MISR BRDF模型参数数据外推到相应MODIS观测角度方向反射的结果一致性较强。(2) 采用两种BRDF模型参数推算的方向反射率与观测数据的接近程度有随观测天顶角的增大而减弱的趋势, 在观测天顶角较小时一致性较好。(3) 所用数据处理结果显示, MODIS BRDF模型参数数据产品在近垂直主平面方向反射率推算结果相比近主平面方向的推算结果更接近观测数据。