Improving the disaggregation of MODIS land surface temperatures in an urban environment: a statistical downscaling approach using high-resolution emissivity

Spatially and temporally dense land surface temperature (LST) data are necessary to capture the high variability of the urban thermal environment. Sensors on board satellites with high revisit time cannot provide adequately detailed spatial information; thus, the downscaling of LST is recognized as being an important and inevitable intermediate process. In this paper, improvement in the downscaled LST accuracy is investigated, employing the statistical downscaling methodology in an urban setting. A new approach is proposed, where thermal radiances are disaggregated using multiple regression analysis and are then combined with emissivity values derived from a high-resolution image classification. Predictors include reflectance values, built-up and vegetation indices, and topographic data. Surface classification is performed utilizing machine learning techniques and fusing Sentinel-2 imagery with ancillary data. Thermal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor are downscaled from their original resolution to 100 m in the city of Athens, Greece. Validation of sharpened temperatures is performed using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) surface temperature product and in-situ measurements. It is demonstrated that the proposed downscaling framework using ridge regression has the potential to produce reliable, high temporal LST estimates with an average error of fewer than 2 K, while consistently having a better accuracy than the reference, single-predictor downscaling of the MODIS LST product.     

An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF

We present a new methodology to generate 30-m resolution land surface albedo using Landsat surface reflectance and anisotropy information from concurrent MODIS 500-m observations. Albedo information at fine spatial resolution is particularly useful for quantifying climate impacts associated with land use change and ecosystem disturbance. The derived white-sky and black-sky spectral albedos may be used to estimate actual spectral albedos by taking into account the proportion of direct and diffuse solar radiation arriving at the ground. A further spectral-to-broadband conversion based on extensive radiative transfer simulations is applied to produce the broadband albedos at visible, near infrared, and shortwave regimes. The accuracy of this approach has been evaluated using 270 Landsat scenes covering six field stations supported by the SURFace RADiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement Southern Great Plains (ARM/SGP) network. Comparison with field measurements shows that Landsat 30-m snow-free shortwave albedos from all seasons generally achieve an absolute accuracy of ±0.02-0.05 for these validation sites during available clear days in 2003-2005, with a root mean square error less than 0.03 and a bias less than 0.02. This level of accuracy has been regarded as sufficient for driving global and regional climate models. The Landsat-based retrievals have also been compared to the operational 16-day MODIS albedo produced every 8-days from MODIS on Terra and Aqua (MCD43A). The Landsat albedo provides more detailed landscape texture, and achieves better agreement (correlation and dynamic range) with in-situ data at the validation stations, particularly when the stations include a heterogeneous mix of surface covers.     

基于中国通量网的MODIS短波反照率验证与分析

遥感地表反照率产品的验证与分析是将其应用于环境研究的基础。采用中国通量网的地表实测短波反照率数据对MODIS反照率产品进行对比和分析,针对选取的8个地面站点,提取了2004年的MODIS反照率产品并进行验证。这些站点的植被覆盖情况涵盖了草地、森林和农业用地。结果显示MODIS在多数情况下能提供准确的地表反照率产品。针对各个站点的误差、均方根误差、相关系数分析都显示了这个结果,总体反演误差在0.002左右。较大的误差出现在有冰雪影响的时候,排除受积雪影响的数据,总体均方根误差可达0.028。分析了引起误差的原因并提出了改进意见。     

Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain

Canopy phenology is an important factor driving seasonal patterns of water and carbon exchange between land surface and atmosphere. Recent developments of real-time global satellite products (e.g., MODIS) provide the potential to assimilate dynamic canopy measurements with spatially distributed process-based ecohydrological models. However, global satellite products usually are provided with relatively coarse spatial resolutions, averaging out important spatial heterogeneity of both terrain and vegetation. Therefore, bias can result from lumped representation of ecological and hydrological processes especially in topographically complex terrain. Successful downscaling of canopy phenology to high spatial resolution would be indispensable for catchment-scale distributed ecohydrological modeling, aiming at understanding complex patterns of water, carbon and nutrient cycling in mountainous watersheds. Two downscaling approaches are developed in this study to overcome this issue by fusing multi-temporal MODIS and Landsat TM data in conjunction with topographic information to estimate high spatio-temporal resolution biophysical parameters over complex terrain. MODIS FPAR (fraction of absorbed photosynthetically active radiation) is used to provide medium spatial resolution phenology, while the variability of vegetation within a MODIS pixel is characterized by Landsat NDVI. The algorithms depend on the scale-invariant linear relationship between FPAR and NDVI, which is verified in this study. Downscaled vegetation dynamics are successfully validated both temporally and spatially with ground-based continuous FPAR and leaf area index measurements. Topographic correction during the downscaling process has a limited effect on downscaled FPAR products except for the period around the winter solstice in the study area.     

Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval

ABSTRACT

Linear kernel driven models of the surface Bidirectional Reflectance Distribution Function (BRDF) are valuable tools for exploiting Earth observation data acquired at different sun–sensor geometries. Here we present a method that efficiently determines linear BRDF model weights using Tikhonov smoothing where the smoothing parameter λ is determined via a Generalized Singular Value Decomposition with the root mean square error prescribed depending on the MODIS band. We applied this method to twenty-six different deciduous broadleaf sites across an entire year using the MODIS Terra and Aqua reflectance data products. Kernel weights and white sky albedo derived from this GSVD method were generally consistent with those provided by the MCD43 data products. The GSVD derived results had less sample variability compared to the MCD43 data products, attributable to the assumed smoothness between kernel weights in the Tikhonov smoothing method. The GSVD technique consistently outperforms MCD43 in the reconstruction of observed MODIS reflectance data, of which retrievals from this method will do a better job of estimating albedo and normalizing data to specified geometries.     

Spatial,seasonal, and topographical patterns of surface albedo in Norwegian forests and cropland

Land surface albedo is a key parameter of the Earth’s climate system. It has high variability in space, time, and land cover and it is among the most important variables in climate models. Extensive large-scale estimates can help model calibration and improvement to reduce uncertainties in quantifying the influence of surface albedo changes on the planetary radiation balance. Here, we use satellite retrievals of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo (MCD43A3), high-resolution land-cover maps, and meteorological records to characterize climatological albedo variations in Norway across latitude, seasons, land-cover type (deciduous forests, coniferous forests, and cropland), and topography. We also investigate the net changes in surface albedo and surface air temperature through site pair analysis to mimic the effects of land-use transitions between forests and cropland and among different tree species. We find that surface albedo increases at increasing latitude in the snow season, and cropland and deciduous forests generally have higher albedo values than coniferous forests, but for few days in spring. Topography has a large influence on MODIS albedo retrievals, with values that can change up to 100% for the same land-cover class (e.g. spruce in winter) under varying slopes and aspect of the terrain. Cropland sites have surface air temperature higher than adjacent forested sites, and deciduous forests are slightly colder than adjacent coniferous forests. By integrating satellite measurements and high-resolution vegetation maps, our results provide a large semi-empirical basis that can assist future studies to better predict changes in a fundamental climate-regulating service such as surface albedo.     

Validation of Narrow-band Surface Albedo Retrieved from FY-3C MERSI Satellite Data

Surface albedo is one of the driving factors in surface radiant energy balance and surface-atmosphere interaction.It is widely used in surface energy balance, medium and long-term weather forecasting and global change research.This study aims to validate the surface albedo retrieved from FY-3C MERSI. This paper selected four regions in Africa and North America as study areas to validate the retrieved albedo from the reflectance data and angle data of FY-3C MERSI at 250 m resolution in 2014. The semi-empirical kernel-driven BRDF(bidirectional reflectance distribution function) model RossThick-LiSparseR and least squares fitting method were used to calculate the parameter of BRDF. Then four narrow-band black-sky albedos and four narrow-band white-sky albedos can be obtained by angle integration. Finally, the cross-validation of FY-3C surface narrow-band albedo products with MODIS albedo products (MCD43A3) was carried out. The results show that theRoot Mean Square Error(RMSE) between the FY-3C narrow-band albedo and the corresponding MODIS narrow-band albedo is in the range of 0.01 ~ 0.04, and the Mean Bias (MBIAS) is 0.09. FY-3C narrow-band albedo has good consistency with the corresponding MODIS narrow-band albedo in the visible and near-infrared bands. So, the methodologyof using the BRDF model to invert the surface albedo of FY-3C medium resolution imaging spectrometer data is feasible and reliable. The further improvement of the inversion accuracy of FY3C-MERSI surface albedo also depends on the improvement of basic data processing quality, including image geometric correction, calibration, and strict data quality control.     

FY-3C中分辨率成像光谱仪数据的窄波段地表反照率验证研究

地表反照率数据对地表能量平衡和全球变化研究具有重要意义。基于2014年FY-3C卫星250 m分辨率的反射率数据和角度数据,选取非洲及北美洲的4个区域作为研究区,采用RossThick-LiSparseR模型作为BRDF(Bidirectional Reflectance Distribution Function)核模型反演了地表窄波段反照率,得到250 m分辨率的4个窄波段黑空、白空反照率。将反演得到的FY-3C地表窄波反照率产品与MODIS反照率产品（MCD43A3）数据进行了交叉验证,结果表明：FY-3C窄波段反照率与对应MODIS窄波段反照率对比的均方根误差在0.01~0.04,平均偏差(MBIAS)为0.09,FY-3C窄波段反照率与对应的MODIS窄波段反照率在可见光波段、近红外波段有较好的一致性。本研究提升了国产风云极轨卫星的应用范围,可为FY-3C地表反照率业务化产品提供算法支撑。     

A comparison study on empirical microwave soil moisture downscaling methods based on the integration of microwave-optical/IR data on the Tibetan Plateau

Soil moisture plays a vital role in land surface energy and the water cycle. Microwave remote sensing is widely used because of the physically based relationship between the land surface emission observed and soil moisture. However, the application of retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, downscaling methods should be developed to disaggregate coarse resolution microwave soil moisture data to fine resolution. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature, vegetation index, and surface albedo are key parameters. Five purely empirical methods based on the triangle feature are selected in this study. To evaluate their performance on downscaling microwave soil moisture, these methods are applied to the Zoige Plateau in China using the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product and Moderate Resolution Imaging Spectroradiometer (MODIS) optical/IR products. The coarse-resolution AMSR-E LPRM soil moisture data are disaggregated into the high resolution of the MODIS product, and the surface soil moisture measurements of the Maqu soil moisture observation network located in the plateau are used to validate the downscaling results. Results show that (1) the relationship models used in these methods can generally capture the variation in soil moisture, with R² around 0.6, but have a relatively high uncertainty under conditions of high soil moisture; (2) the methods can provide high-resolution soil moisture distribution, but the downscaled soil moisture presents a low level correlation with field measurements at different spatial and temporal scales. This comparative study provides insight into the performance of popular purely empirical downscaling methods on enhancing the spatial resolution of soil moisture on the Tibetan Plateau. Although synergistic methods can improve the spatial resolution of AMSR-E soil moisture data, additional studies are needed to exclude the uncertainty from AMSR-E soil moisture estimation, the low sensitivity of the relationship model under high soil moisture, and the spatial representativeness difference between coarse pixels and point measurement.     

Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat,Sentinel-2A and MODIS data

ABSTRACT

The leaf area index (LAI) is a key vegetation canopy structure parameter and is closely associated with vegetation photosynthesis, transpiration, and energy balance. Developing a landscape-scale LAI dataset with a high temporal resolution (daily) is essential for capturing rapidly changing vegetation structure at field scales and supporting regional biophysical modeling efforts. In this study, two daily 30 m LAI time series from 2014 to 2016 over a meadow steppe site in northern China were generated using a spatial and temporal adaptive re?ectance fusion model (STARFM) combined with an LAI retrieval radiative transfer model (PROSAIL). Gap-filled Landsat 7, Landsat 8 and Sentinel-2A surface reflectance (SR) images were used to generate fine-resolution LAI maps with the PROSAIL look-up table method. Two daily 500 m moderate-resolution imaging spectroradiometer (MODIS) LAI product-the existing MCD15A3H LAI product and one was generated from the MCD43A4 SR product and the PROSAIL model, were used to provide temporally continuous LAI variations. The STARFM model was then used to fuse the fine-resolution LAI maps with the two 500 m LAI products separately to generate two daily 30 m LAI time series. Both results were assessed for three types of pasture (mowed pasture, grazing pasture, and fenced pasture) using ground measurements from 2014–2015. The results showed that the PROSAIL-generated LAI maps all exhibited a high accuracy, and the root mean squared errors (RMSEs) for the Landsat 7 LAI and Landsat 8 LAI compared to the ground-measured LAI were 0.33 and 0.28 respectively. The Landsat LAI maps also showed good agreement and similar spatial patterns with the Sentinel-2A LAI with mean differences between ± 0.5. The MCD43A4_PROSPECT LAI product exhibited similar seasonal variability to the ground measurements and to the Landsat and Sentinel-2A LAIs, and these data are also smoother and contain fewer noisy points than the gap-filled MCD15A3H LAI product. Compared to the ground measurements, the daily 30 m LAI time series fused from the fine-resolution LAI maps and PROSPECT generated MODIS LAI product demonstrated better performance with an RMSE of 0.44 and a mean absolute error (MAE) of 0.34, which is an improvement from the LAI time series fused from the fine-resolution LAI maps and the existing MCD15A3H LAI product (RMSE of 0.56 and MAE of 0.42). The latter dataset also exhibited abnormal temporal fluctuations, which may have been caused by the interpolation method. The results also demonstrated the very good performance of the STARFM model in grazing and mowed pasture with homogeneous surfaces compared to fenced pasture with smaller patch sizes. The Sentinel-2A data offers increased landscape vegetation observation frequency and provides temporal information about canopy changes that occur between Landsat overpass dates. The scheme developed in this study can be used as a reference for regional vegetation dynamic studies and can be applied to larger areas to improve grassland modeling efforts.     

结合参量统计与时空融合的土壤水分降尺度方法

土壤水分的降尺度研究为解决被动微波产品的粗分辨率问题,更好地服务于流域小尺度应用提供了技术手段.以美国俄克拉荷马州为研究区域,基于SMAP土壤水分产品和MODIS产品等多种辅助数据,在地表分类数据的支持下,结合参量统计降尺度和时空融合降尺度发展了一种土壤水分混合降尺度方法,并利用SMAP 9 km产品和站点实测数据对降...     

基于机器学习算法的AMSR-2地表温度降尺度研究

MODIS日尺度的地表温度受到天气影响,有效像元信息严重缺失,这对数据稀缺区域尤为重要。以古尔班通古特沙漠为研究区,探索了采用AMSR-2的垂直极化亮度温度与植被指数对地表温度空间降尺度的方法,并用此方法填补了2018年MODIS的缺失像元。(1)通过十折交叉验证,对4种机器学习算法（Cubist、DBN、SVM、RF）、10个波段组合、2个空间尺度（5 km、10 km）下的训练模型进行了分析,表明RF算法精度明显高于其他3种算法,C09波段组合的验证精度高于其他波段组合。(2)构建了2个鲁棒性的随机森林算法地表温度降尺度模型（5 km|RF|09、10 km|RF|09）,将AMSR-2亮度温度降尺度到1km分辨率,表明5 km|RF|09模型反演结果更为合理,MODIS与站点验证的R2分别为0.971、0.930,RMSE分别为3.38 K、4.71 K,MAE分别为2.51 K、3.84 K。(3)降尺度结果填补MODIS地表温度缺失像元,将其应用到古尔班通古特沙漠长时间序列的陆表温度分析,可为数据稀缺区域数据获取提供科学参考。     

Evaluation of the MODIS Albedo product over a heterogeneous agricultural area

In this article, the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43) is evaluated over a heterogeneous agricultural area in the framework of the Earth Observation: Optical Data Calibration and Information Extraction (EODIX) project campaign, which was developed in Barrax (Spain) in June 2011. In this method, two models, the RossThick-LiSparse-Reciprocal (RTLSR) (which corresponds to the MODIS BRDF algorithm) and the RossThick-Maignan-LiSparse-Reciprocal (RTLSR-HS), were tested over airborne data by processing high-resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor. During the campaign, airborne images were retrieved with different view zenith angles along the principal and orthogonal planes. Comparing the results of applying the models to the airborne data with ground measurements, we obtained a root mean square error (RMSE) of 0.018 with both RTLSR and RTLSR-HS models. The evaluation of the MODIS BRDF/Albedo product (MCD43) was performed by comparing satellite images with AHS estimations. The results reported an RMSE of 0.04 with both models. Additionally, taking advantage of a homogeneous barley pixel, we compared in situ albedo data to satellite albedo data. In this case, the MODIS albedo estimation was (0.210 ± 0.003), while the in situ measurement was (0.204 ± 0.003). This result shows good agreement in regard to a homogeneous pixel.     

Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India

Thermal image downscaling algorithms use a unique relationship between land surface temperature (LST) and vegetation indices (e.g. normalized difference vegetation index (NDVI)). The LST–NDVI correlation and regression parameters vary in different seasons depending on land-use practices. Such relationships are dynamic in humid subtropical regions due to inter-seasonal changes in biophysical parameters. The present study evaluates three downscaling algorithms, namely disaggregation of radiometric surface temperature (DisTrad), sharpening thermal imagery (TsHARP), and local model using seasonal (25 February 2010, 14 April 2010, and 26 October 2011) thermal images. The aggregated Landsat LST of 960 m resolution is downscaled to 480, 360, 240, and 120 m using DisTrad, TsHARP, and the local model and validated with aggregated Landsat LSTs of a similar resolution. The results illustrate that the seasonal variability of the LST–NDVI relationship affects the accuracy of the downscaling model. For example, the accuracy of all algorithms is higher for the growing seasons (February and October) unlike the harvesting season (April). The root mean square error of the downscaled LST increases from 480 to 120 m spatial resolution in all seasons. The models are least suitable in water body and dry-river bed sand areas. However, the downscaling accuracy is higher for NDVI > 0.3. The present study is useful to understand the applicability of the downscaling models in seasonally varied landscapes and different NDVI ranges.     

Developing clear-sky,cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America

A new technology was developed at the Canada Centre for Remote Sensing (CCRS) for generating Canada-wide and North America continental scale clear-sky composites at 250 m spatial resolution for all seven MODIS land spectral bands (B1–B7). The MODIS Level 1B (MOD02) swath level data are used as input to circumvent the problems with image distortion in the mid latitude and polar regions inherent to the global sinusoidal (SIN) projection utilized for the standard MODIS data products. The MODIS 500 m land bands B3 to B7 are first downscaled to 250 m resolution using an adaptive regression and normalization scheme for compatibility with the 250 m bands B1 and B2. A new method has been developed to produce the mask of clear-sky, cloud and cloud shadow at 250 m resolution. It shows substantial advantages in comparison with the MODIS 250 m standard cloud masks. The testing of new cloud mask showed that it is in reasonable agreement with the MODIS 1-km standard product once it is aggregated to 1-km scale, while the cloud shadow detection looks more reliable with the new methodology. Nevertheless, more quantitative analyses of the presented scene identification technique are required to understand its performance over the range of input scenes in various seasons. The new clear-sky compositing scheme employs a scene-dependent decision matrix. It is demonstrated that this new scheme provides better results than any others based on a single compositing criterion, such as maximum NDVI or minimum visible reflectance. To account for surface bi-directional properties, two clear-sky composites for the same time period are produced by separating backward scattering and forward scattering geometries, which separate pixels with the sun-satellite relative azimuth angles within 90°–270° and outside of this range. Comparison with Landsat imagery and with MODIS standard composite products demonstrated the advantage of the new technique for screening cloud and cloud shadow, and generating high spatial resolution MODIS clear-sky composites. The new data products are mapped in the Lambert Conformal Conic (LCC) projection for Canada and the Lambert Azimuthal Equal-Area (LAEA) projection for North America. Presently this activity is limited to MODIS/TERRA due to known problems with band-to-band registration and noisy SWIR channels on MODIS/AQUA.     

A maximum entropy method to extract urban land by combining MODIS reflectance,MODIS NDVI,and DMSP-OLS data

Researchers often encounter difficulties in obtaining timely and detailed information on urban growth. Modern remote-sensing techniques can address such difficulties. With desirable spectral resolution and temporal resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) products have significant advantages in tackling land-use and land-cover change issues at regional and global scales. However, simply based on spectral information, traditional methods of remote-sensing image classification are barely satisfactory. For example, it is quite difficult to distinguish urban and bare lands. Moreover, training samples of all land-cover types are needed, which means that traditional classification methods are inefficient in one-class classification. Even support vector machine, a current state-of-the-art method, still has several drawbacks. To address the aforementioned problems, this study proposes extracting urban land by combining MODIS surface reflectance, MODIS normalized difference vegetation index (NDVI), and Defense Meteorological Satellite Program Operational Linescan System data based on the maximum entropy model (MAXENT). This model has been proved successful in solving one-class problems in many other fields. But the application of MAXENT in remote sensing remains rare. A combination of NDVI and Defense Meteorological Satellite Program Operational Linescan System data can provide more information to facilitate the one-class classification of MODIS images. A multi-temporal case study of China in 2000, 2005, and 2010 shows that this novel method performs effectively. Several validations demonstrate that the urban land extraction results are comparable to classified Landsat TM (Thematic Mapper) images. These results are also more reliable than those of MODIS land-cover type product (MCD12Q1). Thus, this study presents an innovative and practical method to extract urban land at large scale using multiple source data, which can be further applied to other periods and regions.     

Validating MODIS land surface reflectance products using ground-measured reflectance spectra – a case study in semi-arid grassland in Inner Mongolia,China

Validation of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance products is important to effective utilization of such products for earth systems science. Ground-based measurements are normally utilized for such validation. However, the major scale mismatch between the ground ‘point’ measurement and MODIS resolution (500 m and 1 km) makes direct comparison infeasible over many land surface types. In this paper, an indirect comparison between ground ‘point’ measurements and MODIS land surface products via high-resolution remotely sensed imagery (Landsat Thematic Mapper/TM) was utilized in semi-arid grassland of Inner Mongolia in summer 2005, where ground measurements are relatively sparse in comparison with other locations around the world. Within the validation, the TM reflectance imagery was first calibrated by the ground ‘point’ measurements, and then aggregated to MODIS data resolution for determination of their accuracy. Besides common direct spectral band comparison of reflectance between TM and MODIS, empirical/indirect comparison between TM and MODIS was also implemented. Both types of validation showed that the absolute error of bidirectional reflectance from atmospheric correction (MOD09) is less than 9.4%, and for nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (MOD43B4) it is less than 3.1%, in which the error of visible bands of two data sets is less than 1.35% and 0.95%, respectively. This validation will help improve the accuracy of MODIS products used in this area.     

基于劈窗算法的Landsat 8影像地表温度反演

陆地表面温度（LST）是表征地表能量交换和地面特征的重要指标,目前遥感技术逐渐成为区域和全球尺度上LST反演的一种便捷工具,而采样不同算法及不同影像的热红外遥感LST反演研究层出不穷,其中基于Landsat数据的反演成果尤为突出。文章利用劈窗算法对Landsat 8遥感影像进行地表温度反演,对比探讨了根据经验值与借助MODIS热红外数据两种不同方式的LST反演结果,并进行北京市热红外波段辐射亮度温度比较,针对地表温度分级进行统计,分析了当地地表温度分布趋势。结果表明：劈窗算法下Landsat 8数据的反演温度更接近实际温度,精度较高且优于MODIS产品;北京市地表温度空间分布格局受地物结构与反射率所制约,高温区主要集中分布于中东部,中低温区分布与林地及水体分布结构较为吻合。     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

Generating global surface albedo products from multiple geostationary satellites

Operational weather geostationary satellites have acquired data for more than two decades and offer thus the possibility to generate long time series of Essential Climate Variables like surface albedo as suggested by GCOS. This paper investigates the possibility to generate consistent global, to the exception of the polar regions, surface broadband albedo product from these satellites. In this context, the paper addresses two specific issues. Firstly, the spatial consistency of surface albedo derived from five different geostationary satellites is examined in detail. Secondly, this product is compared with the equivalent MODIS one to define the temporal consistency between surface albedo derived with old geostationary instruments and technologically advanced radiometers like MODIS. The analysis of the surface albedo products has revealed a good agreement between the products derived from the various geostationary satellites. Comparison of this global product with the one routinely derived from MODIS shows that on the average, the mean relative difference between these two data sets agree within 10%. These first encouraging results open therefore a new avenue for the exploitation of the archived data for the generation of long time series, covering the last 25 years or so, of global surface albedo from geostationary weather satellites.