Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground‐based measurements at a semi‐desert site on the western Tibetan Plateau

Current MODerate‐resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground‐based measurements of LST at Gaize (32.30° N, 84.06° E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1‐km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split‐window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover‐based emissivities in bands 31 and 32 used by the split‐window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1–1.7 K (standard deviation around 0.6 K) in comparisons to the 5‐km grid aggregated values of the LSTs in the 1‐km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground‐based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May–June 2004.     

Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE

This article presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration Terra and Aqua Earth Observing System satellites using in-situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January–April) and 2014 (February–April). A total of 314 day-and-night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. CREST-SAFE is a synoptic ground station, located in the cold county of Caribou in Maine, USA, with a distinct advantage over most meteorological stations because it provides automated and continuous LST observations via an Apogee Model SI-111 Infrared Radiometer. This article also attempts to answer the question of whether a single pixel (1 km²) or several spatially averaged pixels should be used for satellite LST validation by increasing the MODIS window size to 5 × 5, 9 × 9, and 25 × 25 windows.

Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy (about 1°C), both suggesting that MODIS LST retrievals are reliable for similar land-cover classes and atmospheric conditions. Increasing the MODIS window size showed an overestimation of in-situ LST and some improvement in the daytime Terra and night-time Aqua biases, with the highest accuracy achieved with the 5 × 5 window. A comparison between MODIS emissivity from bands 31, 32, and in-situ emissivity showed that emissivity errors (relative error = ?0.30%) were insignificant.     

New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products

This paper discusses the lessons learned from analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-Surface Temperature/Emissivity (LST) products in the current (V4) and previous versions, and presents eight new refinements for V5 product generation executive code (PGE16) and the test results with real Terra and Aqua MODIS data. The major refinements include considering surface elevation when using the MODIS cloudmask product, removal of temporal averaging in the 1 km daily level-3 LST product, removal of cloud-contaminated LSTs in level-3 LST products, and the refinements for the day/night LST algorithm. These refinements significantly improved the spatial coverage of LSTs, especially in highland regions, and the accuracy and stability of the MODIS LST products. Comparisons between V5 LSTs and in-situ values in 47 clear-sky cases (in the LST range from − 10 °C to 58 °C and atmospheric column water vapor range from 0.4 to 3.5 cm) indicate that the accuracy of the MODIS LST product is better than 1 K in most cases (39 out of 47) and the root of mean squares of differences is less than 0.7 K for all 47 cases or 0.5 K for all but the 8 cases apparently with heavy aerosol loadings. Emissivities retrieved by the day/night algorithm are well compared to the surface emissivity spectra measured by a sun-shadow method in two field campaigns. The time series of V5 MODIS LST product over two sites (Lake Tahoe in California and Namco lake in Tibet) in 2003 are evaluated, showing that the quantity and quality of MODIS LST products depend on clear-sky conditions because of the inherent limitation of the thermal infrared remote sensing.     

Quality assessment and validation of the MODIS global land surface temperature

This paper presents an evaluation of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared bands and the status of land surface temperature (LST) version-3 standard products retrieved from Terra MODIS data. The accuracy of daily MODIS LST products has been validated in more than 20 clear-sky cases with in situ measurement data collected in field campaigns in 2000–2002. The MODIS LST accuracy is better than 1°C in the range from ?10 to 50°C. Refinements and improvements were made to the new version of MODIS LST product generation executive code. Using both Terra and Aqua MODIS data for LST retrieval improves the quality of the LST product and the diurnal feature in the product due to better temporal, spatial and angular coverage of clear-sky observations.     

Long-term high-frequency validation of MODIS LST products in a Mediterranean mountain environment

The accuracy of Moderate-resolution Imaging Spectroradiometer (MODIS) level 3 1 km land surface temperature (LST) products was assessed through long-term validation carried out in a mountainous site in Sierra Nevada, southeast Spain. A total of 1458 day and night thermal images, acquired by Terra and Aqua satellites during 2008, were processed and compared to ground-truth data recorded at the meteorological station of Robledal de Cañar with a frequency of one measurement every 10 min. The purpose of this investigation was to understand whether MODIS LST data can be used as input for climate models to be constructed for mountainous environments. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and the overestimation of night-time values. Although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation coefficients with ground measurements, only night values maintained a relatively high accuracy of approximately 2°C of annual average error. Factors that may cause errors in the MODIS LST data, like acquisition angle, cloud, and snow cover, were analysed without conclusive results. High accuracy levels, i.e. close to 1°C, similar to other validation studies carried out over simpler and much more homogenous land-cover types such as cultivated fields, have been achieved for night images acquired during the summer months, thus making these datasets reliable for their use in climatic models over mountainous regions.     

Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites

Cross‐sensor inter‐comparison is important to assess calibration quality and consistency and ensure continuity of observational datasets. This study conducts an inter‐comparison of Terra and Aqua MODIS (the MODerate Resolution Imaging Spectroradiometer) to examine the overall calibration consistency of the reflective solar bands. Observations obtained from AVHRR (the Advanced Very High Resolution Radiometer) onboard the NOAA‐KLM series of satellites are used as a transfer radiometer to examine three MODIS bands at 0.65 (visible), 0.85 (near‐IR) and 1.64 µm (far near‐IR) that match spectrally with AVHRR channels. Coincident events are sampled at a frequency of about once per month with each containing at least 3000 pixel‐by‐pixel matched data points. Multiple AVHRR sensors on‐board NOAA‐15 to 18 satellites are used to check the repeatability of the Terra/Aqua MODIS inter‐comparison results. The same approach applied in previous studies is used with defined criteria to generate coincident and co‐located near nadir MODIS and AVHRR pixel pairs matched in footprint. Terra and Aqua MODIS to AVHRR reflectance ratios are derived from matched pixel pairs with the same AVHRR used as a transfer radiometer. The ratio differences between Terra and Aqua MODIS/AVHRR give an indication of the calibration biases between the two MODIS instruments. Effects due to pixel footprint mismatch, band spectral differences and surface and atmospheric bi‐directional reflectance distributions (BRDFs) are discussed. Trending results from 2002 to 2006 show that Terra and Aqua MODIS reflectances agree with each other within 2% for the three reflective solar bands.     

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass,location and elevation information

The MODerate Resolution Imaging Spectroradiometer (MODIS) instrument on‐board the Terra and Aqua satellites is a critical tool for providing daily estimates of land surface temperature (LST). Terra launched in late 1999 has a morning (AM) overpass, whereas Aqua launched in early 2002 has an afternoon (PM) overpass. Generally, LST is expected, under cloudless conditions, to be warmer in the early afternoon than the morning due to the link between maximum skin temperature and solar insolation peak time, therefore the Aqua PM LST is likely to be closer to the maximum daily LST than that acquired from Terra. This letter investigated differences between the Aqua MODIS PM and Terra MODIS AM LST estimates over a range of land cover classes, locations, and dates, across Canada. The aim was to develop a simple adjustment which can be applied to Terra AM LST estimates to approximate a “synthetic” Aqua PM LST product from 2000 to mid‐2002, thereby providing a seamless afternoon MODIS LST product from 2000 to 2006. Results indicate that there are statistically significant differences between the AM and PM LST ranging from 0.3°C to 3.2°C depending on cover type, and between 1.2° and 5.0° depending on time of year. On average, over 90% of the variation observed in the PM record can be explained by the AM LST, land cover types and location.     

基于分组分裂窗算法的MTSAT-1R地表温度反演

以黑河流域上游和中游为研究区,针对MTSAT-1R卫星数据,运用MODTRAN 4.0及晴空状态下的TIGR大气廓线数据,发展了根据地表比辐射率、大气水汽含量、传感器观测角度分组模拟的分裂窗算法,进行地表温度反演。分析了传感器噪声、地表比辐射率和大气水汽含量3个参数对该算法的影响,并结合模拟数据、地面观测数据及MODIS地表温度产品,对反演结果进行分析评价。结果表明:当传感器垂直观测或大气水汽含量小于2.5g/cm2时,反演精度在1K以内;反演结果与地面观测数据对比差异较小,在阿柔站RMSE为3.7 K(日)/1.4 K(夜),在盈科站RMSE为2.4K(日)/2.0K(夜);与MODIS地表温度产品比较,空间分布呈现出一致性。总之,分组分裂窗算法能较好地用于MTSAT-1R卫星数据进行地表温度反演。     

Validating MODIS land surface temperature products using long-term nighttime ground measurements

The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, provides multiple land surface temperature (LST) products on a daily basis. However, these products have not been adequately validated. This paper presents preliminary results of validating two MODIS Terra daily LST products, MOD11_L2 (version 4) and MOD07_L2 (version 4), using the FLUXNET and Carbon Europe Integrated Project (CarboEurope-IP) long-term ground measurements over eight vegetated sites. Since ground-measured LSTs were only available over one fixed point in each validation site, the study was carefully designed to mitigate the scale mismatch issue by using nighttime ground measurements concurrent to more than 1800 MODIS Terra overpasses.The preliminary results show that MOD11_L2 LSTs have smaller absolute biases and root mean squared errors (RMSE) than those of MOD07_L2 LSTs in most cases. The match of MOD11_L2 LSTs with ground measurements in the Brookings, Audubon, Canaan Valley, and Black Hills sites is good, yielding absolute biases less than 0.8 °C and RMSEs less than 1.7 °C. In the Fort Peck, Hainich, Tharandt, and Bondville sites, MOD11_L2 LSTs were underestimated by 2-3 °C. Biases in MOD11_L2 LSTs correlate to those in MOD07_L2 LSTs. Since the MOD07_L2 LST product is one of the input parameters to the MOD11_L2 LST algorithm, biases in MOD11_L2 LSTs may be influenced by biases in MOD07_L2 LSTs. The errors in both products depend weakly on sensor view zenith angle but are independent of surface air temperature, humidity, wind speed, and soil moisture.     

A three-channel algorithm for retrieving night-time land surface temperature from MODIS data under thin cirrus clouds

Thin cirrus clouds are dominated by non-spherical ice crystals with an effective emissivity of less than 0.5. Until now, the influences of clouds were not commonly considered in the development of algorithms for retrieving land-surface temperature (LST). However, numerical simulations showed that the influence of thin cirrus clouds could lead to a maximum LST retrieval error of more than 14 K at night if the cirrus optical depth (COD) at 12 μm was equal to 0.7 (cirrus emissivity equivalent to 0.5). To obtain an accurate estimate of the LST under thin cirrus using satellite infrared data, a nonlinear three-channel LST retrieval algorithm was proposed based on a widely used two-channel algorithm for clear-sky conditions. The variations in the cloud top height, COD, and effective radius of cirrus clouds were considered in this three-channel LST retrieval algorithm. Using Moderate Resolution Imaging Spectroradiometer (MODIS) channels 20, 31, and 32 (centred at 3.8, 11.0, and 12.0 μm, respectively) and the corresponding land surface emissivities (LSEs), the simulated data showed that this algorithm could obtain LSTs with root mean square errors (RMSEs) of less than 2.8 K when the COD at 12 μm is less than 0.7 and the viewing zenith angle (VZA) is less than 60°. In addition, a sensitivity analysis of the proposed algorithm showed that the total LST errors, including errors from the uncertainties in input parameters and algorithm error, were nearly the same as the algorithm error itself. Some lake surface water temperatures measured in Lake Superior and Lake Erie were used to test the performance of the proposed LST retrieval algorithm. The results showed that the proposed nonlinear three-channel algorithm could be used for estimating LST under thin cirrus with an RMSE of less than 2.8 K.     

Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenland during summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporal variability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model to adequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenland indicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly better correlated with the in situ data than the Aqua retrievals (r = 0.79 versus r = 0.77). Comparisons were also made between the MODIS daily albedo product and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with better agreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua and Terra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day and therefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived using MODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparison suggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.     

Multi‐sensor data fusion and comparison of total column ozone

With many remote‐sensing instruments onboard satellites exploring the Earth's atmosphere, most data are processed to gridded daily maps. However, differences in the original spatial, temporal, and spectral resolution—as well as format, structure, and temporal and spatial coverage—make the data merging, or fusion, difficult. NASA Goddard Earth Sciences Data and Information Services Center (GES‐DISC) has archived several data products for various sensors in different formats, structures, and multi‐temporal and spatial scales for ocean, land, and atmosphere. In this investigation using Earth science data sets from multiple sources, an attempt was made to develop an optimal technique to merge the atmospheric products and provide interactive, online analysis tools for the user community. The merged/fused measurements provide a more comprehensive view of the atmosphere and improve coverage and accuracy, compared with a single instrument dataset. This paper describes ways of merging/fusing several NASA Earth Observing Systems (EOS) remote‐sensing datasets available at GES‐DISC. The applicability of various methods was investigated for merging total column ozone to implement these methods into Giovanni, the online interactive analysis tool developed by GES‐DISC. Ozone data fusion of MODerate resolution Imaging Spectrometer (MODIS) Terra and Aqua Level‐3 daily data sets was conducted, and the results were found to provide better coverage. Weighted averaging of Terra and Aqua data sets, with the consequent interpolation through the remaining gaps using Optimal Interpolation (OI), also was conducted and found to produce better results. Ozone Monitoring Instrument (OMI) total column ozone is reliable and provides better results than Atmospheric Infrared Sounder (AIRS) and MODIS. However, the agreement among these instruments is reasonable. The correlation is high (0.88) between OMI and AIRS total column ozone, while the correlation between OMI and MODIS Terra/Aqua fused total column ozone is 0.79.     

Classification-based emissivity for land surface temperature measurement from space

Classification-based global emissivity is needed for the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectrometer (NASA EOS/MODIS) satellite instrument land surface temperature (LST) algorithm. It is also useful for Landsat, the Advanced Very High Resolution Radiometer (AVHRR) and other thermal infrared instruments and studies. For our approach, a pixel is classified as one of fourteen 'emissivity classes' based on the conventional land cover classification and dynamic and seasonal factors, such as snow cover and vegetation index. The emissivity models we present provide a range of values for each emissivity class by combining various spectral component measurements with structural factors. Emissivity statistics are reported for the EOS/MODIS channels 31 and 32, which are the channels that will be used in the LST split-window algorithm.     

Determination of sea surface temperature at large observation angles using an angular and emissivity-dependent split-window equation

This paper proposes an angular and emissivity-dependent split-window equation that permits the determination of the sea surface temperature (SST) to a reasonable level of accuracy for any observation angle, including large viewing angles at the image edges of satellite sensors with wide swaths. This is the case of the MODIS radiometer both on EOS Terra/Aqua platforms, with observation angles of up to 65° at the surface, for which the split-window equation has been developed in this study. The algorithm takes into account the angular dependence of both the atmospheric correction (due to the increase of the atmospheric optical path with angle) and the emissivity correction (since sea surface emissivity (SSE) decreases with observation angle). Angular-dependent coefficients have been estimated for the atmospheric terms, and also an explicit dependence on the SSE has been included in the algorithm, as this parameter has values different to a blackbody surface for off-nadir angles, the SSEs also being dependent on surface wind speed. The proposed algorithm requires as input data at-sensor brightness temperatures for the split-window bands (31 and 32 of MODIS), the observation angle at each pixel, an estimate of the water vapor content (which is provided by the MODIS MOD07/MYD07 products) and accurate SSE values for both channels. The preliminary results show a good agreement between SSTs estimated by the proposed equation for off-nadir viewings of MODIS-Terra images and in situ SST measurements, with a root-mean square error (RMSE) of about ± 0.3 K, for which the MODIS SST product gives an RMSE larger than ± 0.7 K.     

Evaluation of 10 year AQUA/MODIS land surface temperature with SURFRAD observations

As the 10 year Moderate Resolution Imaging Spectroradiometer Land Surface Temperature MODIS LST becomes available, it is significant to perform a comprehensive evaluation on the long-term product before downstream users use it for climate studies and atmospheric models. In this study, a validation is carried out using observations from the US Surface Radiation budget (SURFRAD) network. Strict quality control removes cloud-contaminated samples from MODIS LST collection and decreases noise information from SURFRAD measurements, thereby making the validation more persuasive. With analysis on 19,735 valid samples, Aqua/MODIS LST from a split-window algorithm shows retrieval errors from –14 K to 17 K with a bias of –0.93 K, an RMSE of 2.65 K, and a standard deviation of 2.48 K. The errors also show strong seasonal signals. With correlation tests between LST errors and several other factors, it is disclosed that LST retrieval errors mainly come from atmospheric effects and surface emissivity uncertainties, which are closely related to relative air humidity, absolute air humidity, sensor zenith angle, wind speed, normalized difference vegetation index (NDVI), and soil moisture. In addition, the impacts from these factors may not be independent. These impact factors suggest a deficiency of the split-window algorithm in dealing with atmospheric and surface complexity and variety.     

FraRI: an algorithm to account for the discontinuity at 60° N in MODIS temperature products

The Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellites conducts continuous monitoring of the Earth's land surface and oceans. Recently, a sharp discontinuity (averaging 1.9°C) has been noticed at 60° N in both MODIS daytime and night-time land surface temperature (LST) products. This linear artefact arises because the CO₂ high cloud test in the operational code for the generation of the MODIS cloud mask product is used only in the non-polar region (between 60° N and 60° S). The resulting discontinuity clearly has negative implications for any statistical applications of these temperature data. In this technical note we present a new algorithm, which minimizes this discontinuity. The method uses edge detection and elimination based on a mixture of Sobel and non-linear Laplacian filters (edge detection and quantification), cubic splines (edge modelling), and a controllable power function for image restoration. The implementation of this algorithm is demonstrated on an image of average minimum night-time LST between 2001 and 2008.     

Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor

This study presents first results on Normalized Difference Vegetation Index (NDVI), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the geostationary satellite Meteosat Second Generation (MSG) covering the African continent. With a temporal resolution of 15 min MSG offers complementary information for NDVI monitoring compared to vegetation monitoring based on polar orbiting satellites. The improved temporal resolution has potential implications for accurate NDVI assessment of the African continent; e.g. the increased amount of available scenes are expected to help overcome problems related to cloud cover which makes the MSG data particularly well suited for early warning systems. Time series of 2004 MSG NDVI was compared to MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua NDVI for the Dahra site in the Senegalese Sahel, West Africa. It was found that NDVI was available for 82 days with multiple cloud free acquisitions per day during the growing season as compared to 47 days with information from either MODIS Terra or Aqua for that particular site. Differences in MSG SEVIRI and MODIS BRDF on a seasonal scale were found to influence the time series of NDVI for the test site; MSG NDVI being higher than MODIS in July-August and lower in October-November. Preliminary composite analysis suggests that the period of compositing to produce continent scale cloud free products can be reduced to ∼5 days using MSG NDVI as compared to polar orbiting data. With the availability of diurnal reflectance information the significance of differences between the red and near-infrared wavelengths due to anisotropy become evident, causing diurnal variations in observed NDVI. Diurnal MSG NDVI was compared to in situ measured MSG NDVI at the test site in Senegal and the same “bowl-shaped” diurnal curve was found for a medium dense cover of annual grasses. The range in observed NDVI and time of diurnal minimum was different due to different viewing geometry. Daily minimum of in situ measured NDVI was around solar noon whereas minimum MSG NDVI occurs one hour prior to noon due to the test site location 12° west of the satellite sensor. Diurnal variation in observed NDVI was studied for a number of pixels characterized by different sensor view zenith angles and vegetation types. This analysis illustrated the diurnal NDVI dependency of illumination conditions, view angle and vegetation intensity and pinpoints the importance of proper BRDF modeling to produce daily values of MSG NDVI normalized for acquisition time, which will be the subject of a forthcoming paper.     

Technical Note: Multi‐sensor data fusion of aerosol optical thickness

This paper investigates the applicability and limitations of combining multi‐sensor data through data fusion, to increase the usefulness of the datasets. This study focuses on merging daily mean aerosol optical thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood (ML) technique to merge the pixel values where available, and then the optimal interpolation method to fill the remaining gaps. The algorithm was applied to a regional AOT subset. The results illustrate that the fusion algorithm can produce complete AOT fields with reasonably good data values and acceptable errors. The cumulative semivariogram (CSV) was found to be sensitive to the spatial distribution and fraction of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.     

基于静止气象卫星数据的地表温度遥感估算

基于分裂窗算法和地表温度日周期变化模型,探讨了利用多时相热红外遥感数据反演地表温度的方法。首先,利用分裂窗算法及地表温度日周期变化形式,推导了多时相遥感数据反演地表温度的方法。其次,利用辐射传输模型(MODTRAN),以2006年夏季在禹城观测的3 d地表温度、气温及大气水汽数据做为输入参数、变化观测角及比辐射率,模拟了一日多个时刻与风云二号(F-2D)波谱响应函数一致的亮温数据,基于此,模拟数据库对所提算法进行了检验。最后,利用2010年9月30日FY-2D多时相热红外数据对新疆区域地表温度进行了反演,并与相应时刻的MODIS地表温度产品进行了比较。结果表明：利用模拟遥感数据反演地表温度,模拟值与估算值的相关系数达0.9,均方根误差在1.5 K以内;利用在轨FY-2D热红外数据反演得到的地表温度与MODIS温度产品趋势基本一致,两者的相关性达到了0.5,均方根误差为4.4 K。需要说明的是,此方法仅满足于晴朗无云的条件。