Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary,USA

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible–Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20 000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R ²) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R ² = 0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite‐based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively.     

Sizing snow grains using backscattered solar light

In this article, we describe a technique to determine dry snow grain size from optical observations. The method is based on analysis of the snow reflectance in the near-infrared region, in particular, the Medium Resolution Imaging Spectrometer (MERIS) band at 865 nm, which is common to many spaceborne optical sensors, is used. In addition, the algorithm is applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) 1240 nm band. It is found that bands located at 1020 and 1240 nm are the most suitable for snow grain size remote-sensing applications. The developed method is validated using MODIS observations over flat snow deposited on a lake ice in Hokkaido, Japan.     

Satellite multi-sensor mapping of suspended particulate matter in turbid estuarine and coastal ocean,China

In this work, five ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer aboard the Terra satellite (Terra MODIS), Moderate Resolution Imaging Spectroradiometer aboard the Aqua satellite (Aqua MODIS), Medium Range Imaging Spectrometer aboard the Environmental Satellite (Envisat MERIS), Medium Resolution Spectral Imager aboard the FY-3 satellite (FY-3 MERSI), and Geostationary Ocean Colour Imager (GOCI), were selected to examine the compatibility of an algorithm proposed for suspended particulate matter (SPM) retrieval and concordance of satellite products retrieved from different ocean-colour sensors. The results could effectively increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off-orbit. Using in situ measurements from 17 cruise campaigns between 2004 and 2012, the SPM retrieval algorithm was recalibrated so as to be universal and adapted for multi-sensor retrievals. An inter-comparison of multi-sensor-derived products showed that GOCI-derived SPM and Envisat MERIS-derived SPM had the best fitting on a 1:1 scatterplot, with a statistic regression slope of 0.9617 and an intercept of 0.0041 (in units of g l^–1), respectively. SPM products derived from three sensors with nearly synchronous transit, Envisat MERIS, Terra MODIS, and FY-3 MERSI, exhibited excellent accordance with mean differences of 0.056, 0.057, and 0.013 g l^–1 in three field fixed stations, respectively, in the Yangtze estuary. Terra MODIS-derived SPM with GOCI-derived SPM, except in the high SPM waters of Hangzhou Bay, and Aqua MODIS-derived SPM with GOCI-derived SPM, except in the moderate SPM waters of the South Branch and south of the Subei Coast, showed a good correspondence. Meanwhile, synchronous multi-sensor-derived SPM with concurrent in situ SPM time series observed in fixed field stations mostly displayed a good correspondence. Results suggest that the algorithm is feasible and compatible for SPM retrieval by multiple sensors.     

The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

In this study we evaluated the potential of the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) for monitoring gross primary productivity (GPP) across fifteen eddy covariance towers encompassing a wide variation in North American vegetation composition. The across-site relationship between MTCI and tower GPP was stronger than that between either the MODIS GPP or EVI and tower GPP, suggesting that data from the MERIS sensor can be used as a valid alternative to MODIS for estimating carbon fluxes. Correlations between tower GPP and both vegetation indices (EVI and MTCI) were similar only for deciduous vegetation, indicating that physiologically driven spectral indices, such as the MTCI, may also complement existing structurally-based indices in satellite-based carbon flux modeling efforts.     

Assessment of SeaWiFS,MODIS, and MERIS ocean colour products in the South China Sea

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) remote-sensing radiometric and chlorophyll-a (chl-a) concentration products for the South China Sea (SCS) from October 2003 to May 2010 were assessed using in situ data. A strict spatiotemporal match-up method was used to minimize the temporal variability effects of atmosphere and seawater around the measurement site. A comparison of the remote-sensing reflectance (R_rs(λ)) of the three sensors with in situ values from the open waters of the SCS showed that the mean absolute percentage difference varied from 13% to 55% in the 412–560 nm spectral range. Generally, the MERIS radiometric products exhibited higher typical uncertainties and bias than the SeaWiFS and MODIS products. The R_rs(443) to R_rs(555/551/560) band ratios of the satellite data were in good agreement with in situ observations for these sensors. The SeaWiFS, MODIS, and MERIS chl-a products overestimated in situ values by 74%, 42%, and 120%, respectively. MODIS retrieval accuracy was better than those of the other sensors, with MERIS performing the worst. When the match-up criteria were relaxed, the assessment results degraded systematically. Therefore, strict spatiotemporal match-up is recommended to minimize the possible influences of small-scale variation in geophysical properties around the measurement site. Coastal and open-sea areas in the SCS should be assessed separately because their biooptical properties are different and the results suggest different atmospheric correction problems.     

Evaluation of RapidEye data for mapping algal blooms in inland waters

The high spatial resolution multispectral imaging sensor onboard RapidEye (RE) has a red-edge band centred at 710 nm, which can be used to produce a product equivalent to the Maximum Chlorophyll Index (MCI) that was developed to detect algal blooms with Medium Resolution Imaging Spectrometer (MERIS) data. The RapidEye system, with five satellites, offers a greater repeat frequency than other high-resolution satellites. In this study, we compared RapidEye and MERIS derived MCI products for the Harris Chain of Lakes in central Florida, USA, to determine if RapidEye can produce an equivalent product similar to MERIS. Data from two RapidEye satellites (RapidEye-2 and RapidEye-5) were used. Band-by-band matchups used RapidEye Top of the Atmosphere (TOA) reflectance and MERIS ρ_s (reflectance corrected only for Raleigh scattering and molecular absorption). The RapidEye TOA reflectance data differed from MERIS, but when the bands were calibrated to the MERIS, the MCI products matched between the two RapidEye satellites and the MERIS MCI. Estimated chlorophyll-a concentrations using a relationship established for Lake Erie matched in situ chlorophyll-a concentrations with a median error of 1.09 mg m^?3. The results indicate that RapidEye is useful for this purpose, which also suggests that other high-resolution satellites with similar red-edge bands may also provide MCI-type products that would allow estimation of chlorophyll-a. RapidEye provides a context for applying future constellation of small satellites for monitoring water quality issues. Lake water quality managers and environmental agencies could effectively use such high-resolution products to assess and manage algal bloom events.     

The inter-comparison of AATSR dual-view aerosol optical thickness retrievals with results from various algorithms and instruments

The Advanced Along-Track Scanning Radiometer (AATSR) dual-view (ATSR-DV) aerosol retrieval algorithm is evaluated for a single scene over Germany (49–53? N, 7–12? E) on 13 October 2005 by comparison of the aerosol optical thickness (AOT) at 550 nm with products from Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS), in addition to the Atmospheric Aerosol Retrieval using Dual-View Angle Reflectance Channels (AARDVARC) algorithm developed at Swansea University. The AOT was retrieved from the AATSR using the ATSR-DV algorithm, for the pixel size of 1 km × 1 km (at nadir). Then these values were meshed to be consistent with the sampling of the products from the other satellite instruments. The ATSR-DV results compare favourably with the products from orbiting optical instruments dedicated to aerosol retrieval, such as MODIS and MISR, which leads to the conclusion that AATSR is well suited for aerosol retrieval over land when the dual view is used with the ATSR-DV algorithm.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.     

A novel ocean color index to detect floating algae in the global oceans

Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.     

Use of satellite images of chlorophyll fluorescence to monitor the spring bloom in coastal waters

This article demonstrates a successful application of fluorescence line height (FLH) images from the Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagers and provides a strong argument for making more widespread use of FLH in monitoring surface phytoplankton in coastal waters. In the present example, MERIS and MODIS FLH images show the start of the spring bloom in coastal waters of the Strait of Georgia in British Columbia, Canada. The images clearly show a recurring pattern in five of the eight years from 2003 to 2010 covered by MERIS, which suggests seeding of the early spring bloom from narrow coastal inlets. Such seeding has been suggested before, but never observed. FLH images show the blooms more clearly than images of surface chlorophyll based on the ratios of water-leaving radiances in the blue and green spectral range (440–560 nm). FLH images used here have been derived with no atmospheric correction. Alternative products based on the blue/green ratio require atmospheric correction, which is difficult in coastal areas. Such products also tend to be more significantly confused with other constituents of coastal waters.     

Advanced InSAR atmospheric correction: MERIS/MODIS combination and stacked water vapour models

A major source of error for repeat‐pass Interferometric Synthetic Aperture Radar (InSAR) is the phase delay in radio signal propagation through the atmosphere (especially the part due to tropospheric water vapour). Based on experience with the Global Positioning System (GPS)/Moderate Resolution Imaging Spectroradiometer (MODIS) integrated model and the Medium Resolution Imaging Spectrometer (MERIS) correction model, two new advanced InSAR water vapour correction models are demonstrated using both MERIS and MODIS data: (1) the MERIS/MODIS combination correction model (MMCC); and (2) the MERIS/MODIS stacked correction model (MMSC). The applications of both the MMCC and MMSC models to ENVISAT Advanced Synthetic Aperture Radar (ASAR) data over the Southern California Integrated GPS Network (SCIGN) region showed a significant reduction in water vapour effects on ASAR interferograms, with the root mean square (RMS) differences between GPS‐ and InSAR‐derived range changes in the line‐of‐sight (LOS) direction decreasing from ～10 mm before correction to ～5 mm after correction, which is similar to the GPS/MODIS integrated and MERIS correction models. It is expected that these two advanced water vapour correction models can expand the application of MERIS and MODIS data for InSAR atmospheric correction. A simple but effective approach has been developed to destripe Terra MODIS images contaminated by radiometric calibration errors. Another two limiting factors on the MMCC and MMSC models have also been investigated in this paper: (1) the impact of the time difference between MODIS and SAR data; and (2) the frequency of cloud‐free conditions at the global scale.     

Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX

A sequence of five high-resolution satellite-based land surface temperature (T_s) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.     

Satellite data cloud detection using deep learning supported by hyperspectral data

ABSTRACT

Deep learning methods can play an important role in satellite data cloud detection. The number and quality of training samples directly affect the accuracy of cloud detection based on deep learning. Therefore, selecting a large number of representative and high-quality training samples is a key step in cloud detection based on deep learning. For different satellite data sources, choosing sufficient and high-quality training samples has become an important factor limiting the application of deep learning in cloud detection. This paper presents a fast method for obtaining high-quality learning samples, which can be used for cloud detection of different satellite data with deep learning methods. AVIRIS (Airborne Visible Infrared Imaging Spectrometer) data, which have 224 continuous bands in the spectral range from 400–2500 nm, are used to provide cloud detection samples for different types of satellite data. Through visual interpretation, a sufficient number of cloud and clear sky pixels are selected from the AVIRIS data to construct a hyperspectral data sample library, which is used to simulate different satellite data (such as data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Operational Land Imager (OLI) satellites) as training samples. This approach avoids selecting training samples for different satellite sensors. Based on the Keras deep learning framework platform, a backpropagation (BP) neural network is employed for cloud detection from Landsat 8 OLI, National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Terra MODIS data. The results are compared with cloud coverage results interpreted via artificial vision. The results demonstrate that the algorithm achieves good cloud detection results for the above data, and the overall accuracy is greater than 90%.     

遥感数据时空融合技术在农作物监测中的适应性研究

受卫星回访周期及云的影响,大范围研究区同一时期的Landsat卫星数据很难获取,因而国内外学者提出了遥感影像时空融合技术。以石河子为实验区,利用STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)模型融合生成了高时空分辨率TM影像,对不同作物类型真实反射率与融合影像反射率作相关性分析,分析了遥感数据时空融合技术在新疆农作物监测中的适用性。结果表明:利用STARFM模型模拟得到的融合影像与真实影像间的相关性较高,但当地物类型发生变化时,融合影像与真实影像间将存在明显的差异。地物类型变化作物融合影像反射率与真实影像反射率间的相关性较小。     

Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests

Accurate and precise detection of phenology events is needed to assess trends in seasonal vegetation development indicative of climate or other environmental change processes. In this research, detection accuracy of start of season (SOS) phenology for deciduous forest across Eastern Canada was assessed using satellite time series and in situ PlantWatch observations. Several aspects were evaluated regarding performance of phenology information extraction: 1) effect of compositing period, 2) individual performance of the Advanced Very High Resolution Radiometer (AVHRR) and the Medium Resolution Imaging Spectrometer (MERIS) sensors, and 3) performance for these sensors combined. The AVHRR and MERIS sensors were used as they are overlapping operational missions with planned future continuity. Three approaches to utilizing the multi-sensor data were tested: 1) inter-calibrating NDVI data between sensors and using the multi-sensor data stream to detect SOS, 2) combining independently derived SOS estimates from AVHRR and MERIS based on a weighted average, and 3) combining approaches 1 and 2. Comparison with in situ observations of leaf out and first bloom showed that combining independent SOS estimates from AVHRR and MERIS was better than using the inter-calibrated multi-sensor data. Combining SOS estimates from both sensors reduced error by 1-2 days compared to the individual sensor results. Composite periods from 7 to 11 days produced the best results for leaf out with a mean absolute error (MAE) of 5 days. Results for first bloom were not as good as those for leaf out, producing a MAE of 6.5 days. For first bloom, compositing periods greater than 11 days did not increase error at the same rate as seen for leaf out. However, the larger MAE observed for first bloom may have masked this effect.     

The 250 m global land cover change product from the Moderate Resolution Imaging Spectroradiometer of NASA's Earth Observing System

One of the products to be derived from data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) will identify locations where land cover changes attributable to human activities are occurring. The product aims to serve as an alarm where rapid land cover conversion can subsequently be analysed with higher resolution sensors such as Landsat 7. This paper describes the production procedure and change detection algorithms for the 250 m MODIS land cover change product. Multiple change detection algorithms, including three spectral methods and two texture methods, are utilized to generate the product from the two 250 m MODIS bands. The change detection methods are implemented with look-up tables (LUTs) which are initially generated using data from the Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic Mapper (TM) until MODIS data become available. The results from the five methods are combined to improve confidence in the product. We test the performance of each method against several sets of simulated MODIS data derived from Landsat TM image pairs. The test data represent tropical deforestation, agricultural expansion and urbanization. The commission errors of the five methods and the combination are approximately 10%, with reasonable omission errors less than 25%.     

Use of Landsat TM and EOS MODIS imaging technologies for estimation of winter wheat yield in the North China Plain

This study focuses on the methodologies of winter wheat yield prediction based on Land Satellite Thematic Map (TM) and Earth Observation System Moderate Resolution Imaging Spectroradiometer (MODIS) imaging technologies in the North China Plain. Routine field measurements were initiated during the periods when the Landsat satellite passed over the study region. Five Landsat TM images were acquired. Wheat yields of the experimental sites were recorded after harvest. Spectral vegetation indices were calculated from TM and MODIS images. The correlation analysis among wheat yield and spectral parameters revealed that TM renormalized difference water index (RDWI) and MODIS near-infrared reflectance had the highest correlation with yield at grain-filling stages. The models from the best-fitting method were used to estimate wheat yield based on TM and MODIS data. The average relative error of the root mean square error (RMSE) of the predicted yield was smaller from TM than from MODIS.     

Comparison of MERIS,MODIS, SeaWiFS-derived particulate organic carbon,and in situ measurements in the South China Sea

Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) particulate organic carbon (POC) concentration products for the South China Sea (SCS) were compared with in situ data collected from October 2007 to December 2013. Spectral remote-sensing reflectance (R_rs,λ) was also measured to help understand POC algorithm performance. A strict comparison of the satellite-derived POC and in situ measurements showed that MERIS, MODIS, and SeaWiFS underestimated in situ values by 29.1, 11.7, and 31.5%, respectively. Similar results were obtained with a relaxed matching criterion. Through analysis of the causes of product uncertainty, the results suggested that satellite retrieval of R_rs,λ and the global POC algorithm both have an impact on inversion accuracy. However, the formulation of the POC algorithm seems to be more critical. When a regional algorithm was developed to obtain satellite-derived POC, both the strict and relaxed comparison results showed significant improvement, but for coastal waters, both algorithms had larger errors. Other factors affecting the comparison are also discussed.     

A comparison of algorithms for Near‐Real Time water vapour retrieval from MODIS

When studying the Earth's surface from space it is important that the component of the signal measured by the satellite‐based sensor due to the atmosphere is accurately estimated and removed. Such atmospheric correction requires good knowledge of atmospheric parameters including precipitable water (PW), ozone concentration and aerosol optical depth. To make full use of the capabilities of satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) these parameters should be accurately estimated in Near‐Real Time (NRT) with complete global coverage approximately every two days. NRT retrieval of the required ancillary information facilitates the atmospheric correction of such direct broadcast data from the MODIS instrument in the operational environment. In this paper three Near Infrared (NIR) algorithms for PW retrieval from MODIS are compared to determine which is most suitable for use in an operational MODIS‐based process for the atmospheric correction of spectral reflectance data. Two of the algorithms estimate PW in NRT and gave RMS errors of approximately 0.48 g cm^?2 (23%) and 0.59 g cm^?2 (28%), respectively, when compared against radiosonde data and modelled PW fields over Western Australia. The third algorithm was the NIR PW product from MODIS (MOD05) archived by the Distributive Active Archive Centre (DAAC). For the same locations the MOD05 NIR PW dataset gave an RMS error of approximately 0.95 g cm^?2 (44%). In each of the cases the best results were obtained after optimal cloudmasking of the NIR data. In this paper, the accuracy and suitability of the three algorithms for use in the operational atmospheric correction of MODIS data are evaluated and the importance of an accurate cloudmask for atmospheric correction in NRT is discussed.     

Quality assessment of Landsat surface reflectance products using MODIS data

Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat-MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions.