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1.
反照率作为一种非常重要的地表能量平衡、全球变化研究的参数,在众多研究领域中得到了广泛的应用,到目前为止已经有多种全球范围的反照率产品进行业务化生产和发布,针对不同反照率产品质量评价的研究也变得愈加重要。MODLAND团队在MODIS V005反照率产品反演算法的基础上通过改进16天周期内观测数据加权的方法生产出新版本的反照率产品MODIS V006。本文针对MODIS两个版本V005及V006的反照率产品,利用FLUXNET地面站点数据,比较验证两个版本反照率的总体精度以及在不同地表类型条件下的精度差异,同时通过交叉验证的方法分析二者的差异及稳定性。验证结果表明,MODIS V006反照率产品虽然在全反演高质量的数据比例上较V005有所下降,但是在同等条件下V006在提高时间分辨率的同时其精度也有所提高,在不同的地表类型条件下精度也优于V005,且在时间序列分布上具有稳定比例的高质量数据,可以满足大多数应用的精度需求。 相似文献
2.
融合Landsat ETM+和MODIS数据估算高时空分辨率地表短波反照率 总被引：2，自引：0，他引：2
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。 相似文献
3.
利用BRDF原型和单方向反射率数据估算地表反照率 总被引：2，自引：2，他引：0
地表反照率是影响地表能量收支平衡的决定性参数之一,精确反演地表反照率需要考虑地表各向异性反射特征。本文尝试以双向反射分布函数BRDF原型为地表各向异性反射的先验知识,通过单方向反射率反演地表反照率。首先根据地面实测及MODIS多角度反射率数据对反演方法进行分析和精度评价,然后借助MODIS BRDF产品统计出研究区的主导BRDF原型,并联合环境一号卫星(HJ-1)单方向反射率数据反演30 m地表反照率,最终将结果与地表实测数据进行比较。结果表明:BRDF原型对BRDF的变化进行了约束,且能够适用于几十米尺度的遥感数据反照率的反演;不同级别的各向异性反射特征的分布是不均一的,借助于主导BRDF原型能够使大部分样本的地表反照率满足精度要求;利用研究区MODIS BRDF产品统计得到的主导BRDF原型为先验知识,通过HJ-1数据反演得到的地表反照率与地表实测反照率有较高的一致性,而朗伯假定条件下的反照率高于实测结果。本文算法简单高效,可为产生全国范围的中高分辨卫星反照率产品提供有价值的算法参考。 相似文献
4.
Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warming/cooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF/NBAR/albedo products and 30 m Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDF/Albedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30 m Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30 m albedos for the intervening daily time steps in this study. These enhanced daily 30 m spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of ±0.006. These synthetic time series provide much greater spatial detail than the 500 m gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 km by 14 km) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30 m resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces. 相似文献
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6.
The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters. 相似文献
7.
《International Journal of Digital Earth》2013,6(6):604-622
ABSTRACTOne of the essential steps for satellite albedo validation is upscaling in situ measurements to corresponding pixel scale over relatively heterogeneous land surfaces. Although the multi-scale validation strategy is applicable for heterogeneous surfaces, the calibration of the high-resolution imagery during upscaling process is never perfect, and thus the upscaling results suffer from errors. The regression-kriging (RK) technique can compensate the calibration part by applying kriging to upscale residuals and produce more accurate upscaling results. In this paper, in situ measurements and high spatial resolution albedo imagery combined with RK technique was proposed. This method is illustrated by upscaling surface albedo from in situ measurements scale to the coarse pixel scale in the core experimental area of HiWATER, where 17 WSN nodes were deployed at heterogeneous area. The upscaling results of this method were compared with the upscaling results from multi-scale strategy. The results show that the upscaling method based on in situ measurements and high-resolution imagery combined with RK technique can capture the spatial characteristics of surface albedo better. Further, an attempt was made to expand this method in time series. Finally, a preliminary validation of the Moderate Resolution Imaging Spectroradiometer albedo product was performed as the tentative application. 相似文献
8.
《International Journal of Digital Earth》2013,6(9):709-724
Six widely used coarse-resolution global land cover data-sets – Global Land Cover Characterization (GLCC), Global Land Cover 2000 (GLC2000), GlobCover land cover product (GlobCover), MODIS land cover product (MODIS LC), the University of Maryland land cover product (UMD LC), and the MODIS Vegetation Continuous Fields tree cover layer (MODIS VCF) disagree substantially in their estimates of forest cover. Employing a regression tree model trained on higher-resolution, Landsat-based data, these multisource multiresolution maps were integrated for an improved characterization of forest cover over North America. Evaluated using a withheld test sample, the integrated percent forest cover (IPFC) data-set has a root mean square error of 11.75% – substantially better than the 17.37% of GLCC, 17.61% of GLC2000, 17.96% of GlobCover, 15.23% of MODIS LC, 19.25% of MODIS VCF, and 15.15% of UMD LC, respectively. Although demonstrated for forest, this approach based on integration of multiple products has potential for improved characterization of other land cover types as well. 相似文献
9.
地表二向性反射分布函数(BRDF)是表征地物反射随太阳和观测方向变化的物理量。在统计意义上,BRDF表示均值统计量,BRVF(Bidirectional Reflectance Variance Function)表示方差统计量,它们对研究地表各向异性反射特征有着重要意义。本文首先采用误差传播理论,推导出基于MODIS BRDF模型的BRVF表达形式。研究结果表明,BRVF的空间分布模式主要由几何光学核Kgeo和体散射核Kvol的一次项和二次项权重和决定。然后利用EOS地面验证核心站点(EOS Land Validation Core Sites)的MODIS BRDF产品,对BRVF空间分布模式随地表类型、光谱波段和观测角度范围进行验证。验证结果表明,基于MODIS BRDF产品的验证与理论推导有较好的一致性。BRVF空间分布模式和地表类型有关,通常在热点处有一个峰值。在大观测天顶角(60°)下,BRVF随着角度的增大而增大。BRVF在近红外波段整体上大于红波段,表明其波段依赖性。最后,将上述理论成果初步应用于69组地表测量数据的模拟中。模拟结果表明,当大角度缺少观测数据时,模型外延所引起的方向反射方差显著增大,对地表反照率的反演精度和不确定性有较大影响。其中,红波段的白天空反照率的相对误差最大可达38.26%。本研究对利用小角度观测数据进行地表反照率反演的不确定性分析有指导意义;对大角度观测数据缺失情况下,先验知识在地表反照率的反演应用可提供有意义的理论支撑。 相似文献
10.
异质性地表反照率遥感产品真实性检验研究现状及挑战 总被引：1，自引：1，他引：0
地表反照率直接决定了地表能够吸收到的太阳辐射能量,是研究气候变化、能量平衡的一个关键参数。遥感为大尺度、连续获取地表反照率提供了一种有效的观测手段。但遥感数据本身的精度限制和反演模型的不确定性,使基于卫星数据反演的反照率产品存在误差,而这种误差的存在又会影响产品的进一步应用。正确的认识这种误差有助于提高产品的应用精度,深化其应用的深度和广度。真实性检验就是正确认识卫星反照率产品准确性、稳定性的重要手段,它是卫星产品从生产到应用的桥梁。虽然目前已经开展了大量的真实性检验工作,但即使是针对同一种卫星产品,真实性检验的结果往往并不一致。其根本原因在于验证中所采用的参考值能不能够准确地代表卫星像元尺度的地面真值。地面观测和卫星产品像元之间巨大的尺度差异以及广泛分布的地表异质性,使地面观测并不能直接作为像元尺度真值与卫星产品进行简单的对比。因此真实性检验过程并不是直接的,而是需要经过一系列严格、独立的过程得到像元尺度真值后与产品在一定的空间和时间范围内进行对比。针对目前真实性检验结果准确性和可信度不高等问题,本文尝试从地面实测数据、尺度转换、验证方式、评价方法及验证中存在的问题等几个方面来论述反照率产品的真实性检验现状及挑战。 相似文献
11.
Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed. 相似文献
12.
《International Journal of Digital Earth》2013,6(6):157-171
The Moderate Resolution Imaging Spectroradiometer (MODIS)-Terra surface reflectance product (MOD09A1), with bands 1 to 7, is a gridded, eight-day composite product derived from the MODIS-Terra top of atmosphere reflectance swaths. It performs cloud detection and corrects for the effects of atmospheric gases and aerosols. The cloud mask (CM) algorithms for MODIS are based on empirical thresholds on spectral reflectance and brightness temperature. Since the spatial resolution of the thermal band is 1000 m, while that of MOD09A1 is 500 m, many undetected and false clouds are observed in MOD09A1. These errors always result in temporal and spatial inconsistencies in higher-level products. In this paper, a cloud detection algorithm (TSCD) based on a MOD09A1 time series is introduced. Time series cloud detection (TSCD) algorithm is based on the relative stability of ground reflectance and the sudden variations in reflectance that result from cloud cover. The algorithm first searches the clear-sky reference data, and then discriminates clouded and unclouded pixels by detecting a sudden change of reflectance in the blue wavelength and spectral correlation coefficient at the pixel level. Compared with cloud cover assessments obtained from MODIS' original CM, TSCD provides similar or better discrimination in most situations when the land surface changes slowly. 相似文献
13.
陆表覆盖变化影响地表特征从而改变地表能量平衡是理解人类活动对全球气候变化影响的关键环节。选择国际气候谈判主要国家的美国、印度和巴西作为中国的对比国，对比分析不同国别、不同气候带典型陆表覆盖类型的地表反照率时空差异，进而模拟开垦和城市化等陆表覆盖变化对反照率的影响差异。结果表明：(1) 2000年—2015年，中国、美国的地表反照率年际变化存在明显的气候带空间分异特征，中国干旱半干旱区和美国中低纬湿润区表现出降低趋势，而中国亚热带湿润和美国高纬与中部干旱区则表现出明显的升高趋势，印度的地表反照率年际变化呈微弱下降趋势，而巴西为微弱上升趋势。(2)无雪覆盖时，耕地、林地、草地和人造地表反照率具有夏高、冬低的时间变化特征，干旱半干旱区反照率明显高于湿润区。4种类型的国别差异体现在，中国亚热带湿润区地表反照率均以上升为主，干旱半干旱区则相反；美国除耕地在干旱区呈较强的升高趋势外，其余类型基本为降低趋势；印度均表现为降低趋势；巴西则表现为略微升高趋势。(3)与无雪覆盖相比，有雪覆盖时不同陆表覆盖类型地表反照率均有所提高，林地提高幅度最小，约0.06—0.26，耕地提高最大，约为0.17—0.38，且中国林地反照率提高幅度略高于美国。(4)原陆表覆盖为林地时，开垦和城镇化均导致地表反照率升高，且干旱区升高幅度高于湿润区，湿润区的升高幅度随纬度降低而减弱；为草地时，开垦主要在巴西、印度和中、美亚热带湿润区引起地表反照率升高。而城镇化引起的反照率变化则受到原有地表覆盖、季节和气候背景影响存在较复杂的国别和气候带差异。 相似文献
14.
Yunqiao Shu Simon Stisen Karsten H. Jensen Inge Sandholt 《International Journal of Applied Earth Observation and Geoinformation》2011
Data from the first operational Chinese geostationary satellite Fengyun-2C (FY-2C) satellite are applied in combination with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products for the assessment of regional evapotranspiration over the North China Plain. The approach is based on the improved triangle method, where the temperature–vegetation index space includes thermal inertia. Two thermal infrared channels from FY-2C are used to estimate surface temperature (Ts) based on a split window algorithm originally proposed for the MSG-SEVIRI sensor. Subsequently the high temporal resolution of FY-2C data is exploited to give the morning rise in Ts. Combined with the 16 days composite MODIS vegetation indices product (MOD13) at a spatial resolution of 5 km, evaporative fraction (EF) is estimated by interpolation in the ΔTs–NDVI triangular-shaped scatter space. Finally, regional actual evapotranspiration (ET) is derived from the evaporative fraction and available energy estimated from MODIS surface albedo products MCD43. Spatial variations of estimated surface variables (Ts, EF and ET) corresponded well to land cover patterns and farmland management practices. Estimated ET and EF also compared well to lysimeter data collected for the period June 2005–September 2007. The improved triangle method was also applied to MODIS products for comparison. Estimates based on FY-2C products proved to provide slightly better results than those based on MODIS products. The consistency of the estimated spatial variation with other spatial data supports the use of FY-2C data for ET estimation using the improved triangle method. Of particular value is the high temporal frequency of image acquisitions from FY-2C which improves the likelihood of obtaining cloud free image acquisitions as compared to polar orbiting sensors like MODIS. 相似文献
15.
A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal. The lowest frequency component (last IMF) is the trend in the NDVI series. The trend in the series has been identified finding the Sen’s slope of last IMF, and the non-parametric seasonal Mann–Kendall test has been used to confirm the significance of the observed trend. The method has been applied on per–pixel basis to the SPOT Vegetation NDVI product covering Northeast India and surrounding regions for the time span of 1998–2009. Results show that the method has performed well in identifying the pixel clusters with significant trends. Hotspot regions with severe vegetation degeneration have been identified, and the relationship of the observed trends with the expected causative variables such as land use and land cover, topographic relief, and anthropogenic causes has been explored. The spatial locations of these critical regions closely matches with the findings of the previous studies carried out locally in the region, mainly indicating the shifting cultivation practice to be the main cause for land cover change. 相似文献
16.
Enrique L. Montaño 《International Journal of Digital Earth》2016,9(5):520-544
This research examines uncertainty in MODerate resolution Imaging Spectroradiometer (MODIS) observations, and demonstrates the direct influence of geometric distortions resulting from the standard practice of geolocating swath observations. MODIS observations vary dependent on the ground sample distance, which varies dependent on the view zenith angle that changes with every orbit. MODIS Level 2G (L2G) land products are generated by applying a geolocation algorithm that resamples the variable observation geometries to a consistent grid of fixed pixel size and location, a process which itself introduces variability associated with the changing observational footprint. For this study, broadband albedo was simulated for five validation sites, representing five distinct land cover types, exhibiting quantifiable variability, with additional seasonal variability exhibited in some sites. All site simulations exhibit compounded uncertainty attributable to the geometric distortion sufficient to influence climate models (i.e. ranges from 0.01 to 0.045 albedo). These results indicate there is a minimum level of uncertainty associated with the variable geometry that should be factored into L2G-based products, particularly for nominal 250?m band data. Aggregating the data to coarser resolutions and smoothing the data through average resampling can mitigate the uncertainty. 相似文献
17.
Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using primitive map layers. Primitive map layers are a suite of biophysical and end member maps, with land cover primitives representing the raw information needed to make decisions in a dichotomous key for land cover classification. We present best practices to create and assemble primitives from optical satellite using computing technologies, decision tree logic and Monte Carlo simulations to integrate their uncertainties. The concept is presented in the context of a regional land cover map based on a shared regional typology with 18 land cover classes agreed on by stakeholders from Cambodia, Laos PDR, Myanmar, Thailand, and Vietnam. We created annual map and uncertainty layers for the period 2000–2017. We found an overall accuracy of 94% when taking uncertainties into account. RLCMS produces consistent time series products using free long term historical Landsat and MODIS data. The customizable architecture can include a variety of sensors and machine learning algorithms to create primitives and the best suited smoothing can be applied on a primitive level. The system is transferable to all regions around the globe because of its use of publicly available global data (Landsat and MODIS) and easily adaptable architecture that allows for the incorporation of a customizable assembly logic to map different land cover typologies based on the user's landscape monitoring objectives 相似文献
18.
With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images. 相似文献
19.
及时获取有效的土地覆盖信息是地球系统模拟的基础。因此,中等空间分辨率传感器如MODIS或MERIS空前的通道设置与观测能力,使其具有快速更新土地覆盖图的能力。本文说明了如何结合MERIS的空间维（像元大小为300m）、光谱维（可见光与近红外范围内15个通道）和时间维（重返周期2—3d）,用于获取不同区域土地覆被组分的亚像元级组成权重。利用4月、7月和8月三期MERIS FR1b级数据得到荷兰主要土地覆被类型的组成权重。单一时相和多时相的数据都使用单个像元最优化的端元数进行线性光谱分解。利用一种形态偏离指数得到MERIS的空间维并用于端元的选择。应用荷兰土地利用数据库（LGN5）25m分辨率的栅格数据作为本文的参考数据。基于这种数据的高分辨率,因此可以从像元和亚像元的水平同时评价的分类精度。结果显示,结合4月和7月的影像可以获得最优的分类结果,精度约为58%。总的说来,亚像元和像元级的分类精度相似。通过几种组分类别和日期的光谱融合表明,物候状况对于数据获取时相最佳结合的选择以及正确识别土地覆盖类型的重要性。 相似文献
20.
Forward a spatio-temporal trend surface for long-term ground-measured albedo upscaling over heterogeneous land surface 总被引：1，自引：0，他引：1
Xiaodan Wu Qing Xiao Dongqin You Qiang Liu Xingwen Lin 《International Journal of Digital Earth》2018,11(5):470-484
Upscaling ground albedo is challenged by the serious discrepancy between the heterogeneity of land surfaces and the small number of ground-based measurements. Conventional ground-based measurements cannot provide sufficient information on the characteristics of surface albedo at the scale of coarse pixels over heterogeneous land surfaces. One method of overcoming this problem is to introduce high-resolution albedo imagery as ancillary information for upscaling. However, due to the low frequency of updating of high-resolution albedo maps, upscaling time series of ground-based albedo measurements is difficult. This paper proposes a method that is based on the idea of conceptual universal scaling methodology for establishing a spatiotemporal trend surface using very few high-resolution images and time series of ground-based measurements for spatial-temporal upscaling of albedo. The construction of the spatiotemporal trend surface incorporates the spatial information provided by auxiliary remote sensing images and the temporal information provided by long time series of ground observations. This approach was illustrated by upscaling ground-based fine-scale albedo measurements to a coarse scale over the core study area in HiWATER. The results indicate that this method can characterize the spatiotemporal variations in surface albedo well, and the overall correlation coefficient was 0.702 during the study period. 相似文献