Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia

The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non‐supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km² of land surface were burnt in Acre State. Of this, 3700 km² corresponded to the previously deforested areas and 2800 km² corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Radiometric homogenisation of aerial images by calibrating with satellite data

The use of very high resolution (VHR) aerial imagery for quantitative remote sensing has been limited by unwanted radiometric variation over temporal and spatial extents. In this paper we propose a simple yet effective technique for the radiometric homogenisation of the digital numbers of aerial images. The technique requires a collocated and concurrent, well-calibrated satellite image as surface reflectance reference to which the aerial images are calibrated. The bands of the reference satellite sensor should be spectrally similar to those of the aerial sensor. Using radiative transfer theory, we show that a spatially varying local linear model can be used to approximate the relationship between the surface reflectance of the reference image and the digital numbers of the aerial images. The model parameters for each satellite pixel location are estimated using least squares regression inside a small sliding window. The technique was applied to a set of aerial images captured over multiple days with an Intergraph Digital Mapping Camera (DMC) system. A near-concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance image was used as the reflectance reference dataset. The resulting DMC mosaic was compared to a near-concurrent Satellite Pour l’Observation de la Terre (SPOT) 5 reflectance image of a portion of the same area, omitting the blue channel from the DMC mosaic due to its absence in the SPOT 5 data. The mean absolute reflectance difference was found to be 3.43% and the mean coefficient of determination (R²) over the bands was 0.84. The technique allows the production of seamless mosaics corrected for coarse scale atmospheric and BRDF effects and does not require the manual acquisition (or provision) of ground reflectance references. The accuracy of corrections is limited by the resolution of the reference image, which is generally significantly coarser than VHR imagery. The method cannot correct for small scale BRDF or other variations not captured at the reference resolution. Nevertheless, results show a significant improvement in homogeneity and correlation with SPOT 5 reflectance.     

Satellite‐based assessment of the dynamics of new lakes in southern Egypt

Lakes in arid landscapes are indicators of environmental change and important sources of water for human use. In regions without in situ hydrologic measurements, remote sensing may provide the only means to monitor long‐term changes in water storage. We used a synergistic combination of multiple satellite remote‐sensing methods to provide the first comprehensive assessment of the dynamics of a newly formed chain of large lakes in the hyper‐arid Toshka Depression of southern Egypt. A total of 145 MODIS and AVHRR satellite images were used to monitor changes in lake surface area, which increased to a maximum of 1740 km² before declining to 900 km². Two methods were tested for satellite‐based measurement of lake levels and volumes, one based on analysis of a digital elevation model and one using data from the ICESat GLAS laser altimeter. This study shows the power of satellite remote sensing for long‐term monitoring of regional‐scale hydrologic transformations.     

A method for monitoring the on-orbit performance of a satellite sensor infrared window band using oceanic drifters

In this study, we propose a method to monitor the on-orbit performance of a satellite sensor thermal emissive band (TEB) using oceanic drifters and National Centers for Environmental Prediction (NCEP) data. Based on the radiance simulated using oceanic drifter data, NCEP data, and a radiative transfer model, we primarily aim to develop a method for the real-time monitoring of the on-orbit performance of a satellite sensor thermal emissive band. One month of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) L1b radiance data from Channels 31 and 32 are used as proxy data to test this method. In this paper, we propose some improvements to the cloud test algorithm for this monitoring method, thereby significantly enhancing the quality of this method. We describe the sensitivity tests conducted to identify the important sources of errors. Our improvements eliminate the cases contaminated by clouds and influenced by non-uniform distribution of seawater temperature. Comparisons with Aqua MODIS data reveal systematic underestimations (0.48 ± 0.97 K for Channel 31, 0.36 ± 1.05 K for Channel 32) of this method. Our sensitivity analysis also reveals that surface emissivity and drifter temperature are likely to make the greatest contributions to the total uncertainty of this method. We should therefore correct systematical errors in the real-time monitoring algorithm for other satellite TEB sensors.     

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km². AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.     

Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia - implications for MODIS LST based permafrost monitoring

The surface temperature of permafrost soils in remote arctic areas is accessible by satellite land surface temperature (LST) detection. However, the spatial resolution of satellite measurements such as the MODIS LST products is limited and does not detect the heterogeneities of the wet polygonal tundra landscape where surface wetness varies over distances of several meters. This paper examines the spatial and temporal variability of summer surface temperatures of a polygonal tundra site in northern Siberia using a ground based high resolution thermal imaging system. Thermal infrared images were taken of a 1000 m² polygonal tundra area in 10 min intervals from July to September 2008. Under clear sky conditions, the individual measurements indicate temperature differences of up to 6 K between dry and wet tundra surfaces and which can exceed 12 K when dry tundra and water surfaces are compared. These differences disappear when temperature averages are considered for intervals longer than the diurnal cycle; for weekly averages the spatial temperature variability decreases below 1 K. The exception is the free water surface of a shallow polygonal pond where weekly averaged temperature differences of 2.5 K are sustained compared to the tundra surface.The ground based thermal infrared images are upscaled to MODIS sized pixels and compared to available MODIS LST data for individual measurements and weekly averages. The comparisons show generally good agreement for the individual measurements under clear sky conditions, which exist during 20% of the studied time period. However, several erroneous measurements and large data gaps occur in the MODIS LST data during cloudy conditions, leading to biased weekly temperature averages inferred from the satellite observations. Based on these results the following recommendations are given for future permafrost temperature monitoring based on MODIS LST products: (i) high resolution surface water masks for the quality assessment in landscapes where lakes and ponds are frequent and (ii) reliable cloud cover detection in conjunction with a gap filling procedure for accurate temporal averages.     

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

Phenological records constructed from global mapping satellite platforms (e.g. AVHRR and MODIS) hold the potential to be valuable tools for monitoring vegetation response to global climate change. However, most satellite phenology products are not validated, and field checking coarse scale (≥ 500 m) data with confidence is a difficult endeavor. In this research, we compare phenology from Landsat (field scale, 30 m) to MODIS (500 m), and compare datasets derived from each instrument. Landsat and MODIS yield similar estimates of the start of greenness (r² = 0.60), although we find that a high degree of spatial phenological variability within coarser-scale MODIS pixels may be the cause of the remaining uncertainty. In addition, spatial variability is smoothed in MODIS, a potential source of error when comparing in situ or climate data to satellite phenology. We show that our method for deriving phenology from satellite data generates spatially coherent interannual phenology departures in MODIS data. We test these estimates from 2000 to 2005 against long-term records from Harvard Forest (Massachusetts) and Hubbard Brook (New Hampshire) Experimental Forests. MODIS successfully predicts 86% of the variance at Harvard forest and 70% of the variance at Hubbard Brook; the more extreme topography of the later is inferred to be a significant source of error. In both analyses, the satellite estimate is significantly dampened from the ground-based observations, suggesting systematic error (slopes of 0.56 and 0.63, respectively). The satellite data effectively estimates interannual phenology at two relatively simple deciduous forest sites and is internally consistent, even with changing spatial scale. We propose that continued analyses of interannual phenology will be an effective tool for monitoring native forest responses to global-scale climate variability.     

Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea

Estimating the evapotranspiration (ET) is a requirement for water resource management and agricultural productions to understand the interaction between the land surface and the atmosphere. Most remote-sensing-based ET is estimated from polar orbiting satellites having low frequencies of observation. However, observing the continuous spatio-temporal variation of ET from a geostationary satellite to determine water management usage is essential. In this study, we utilized the revised remote-sensing-based Penman–Monteith (revised RS-PM) model to estimate ET in three different timescales (instantaneous, daily, and monthly). The data from a polar orbiting satellite, the Moderate Resolution Imaging Spectroradiometer (MODIS), and a geostationary satellite, the Communication, Ocean, and Meteorological Satellite (COMS), were collected from April to December 2011 to force the revised RS-PM model. The estimated ET from COMS and MODIS was compared with measured ET obtained from two different flux tower sites having different land surface characteristics in Korea, i.e. Sulma (SMC) with mixed forest and Cheongmi (CFC) with rice paddy as dominant vegetation. Compared with flux tower measurements, the estimated ET on instantaneous and daily timescales from both satellites was highly overestimated at SMC when compared with the flux tower ET (Bias of 41.19–145.10 W m^?2 and RMSE of 69.61–188.78 W m^?2), while estimated ET results were slightly better at the CFC site (Bias of –27.28–13.24 W m^?2 and RMSE of 45.19–71.82 W m^?2, respectively). These errors in results were primarily caused due to the overestimated leaf area index that was obtained from satellite products. Nevertheless, the satellite-based ET indicated reasonable agreement with flux tower ET. Monthly average ET from both satellites showed nearly similar patterns during the entire study periods, except for the summer season. The difference between COMS and MODIS estimations during the summer season was mainly propagated due to the difference in the number of acquired satellite images. This study showed that the higher frequency of COMS than MODIS observations makes it more ideal to continuously monitor ET as a geostationary satellite with high spatio-temporal coverage of a geostationary satellite.     

Environment contribution to the atmospheric correction for Landsat-MSS images

The signal returning from the Earth's surface to the satellite is modified by the atmospheric effect, which has two components. The first one is solar radiation which, due to backscattering, is deviated in the direction of the sensor without reaching the Earth's surface. The second component is produced by the energy reflected in areas close to the pixel observed which, owing to collisions with atmospheric constituents, is deviated from its path in the sensor direction. This is called the adjacency effect and this paper presents a numerical method to estimate this effect under the assumption of a heterogeneous flat Lambertian surface. From this estimation it is possible to apply the atmospheric correction for the calculation of reflectance images based on data obtained by the optical channels of high resolution satellite systems such as Landsat-MSS, Landsat-TM and SPOT/H RV. In particular, in this paper the method is applied to Landsat-5 MSS images over urban regions. However, its application to any of the sensors mentioned is easily implemented considering the changes in spectral response and pixel size. Differences obtained in the results for reflectance at Earth's surface in winter and summer images were in the order of 10^?3 for bands 1 and 2, 10^?2 for band 3, and 10^?1 for band 4.     

基于局部空间自相关分析的时空数据融合

由于受到16d重访周期与云等对数据质量的影响,具有时间与空间连续性的Landsat 8OLI观测数据难以直接获取。考虑地物分布的空间自相关性,提出一种基于STARFM模型改进的局部自相关时空数据融合模型(LASTARFM),以新疆维吾尔族自治区喀什地区叶城县为研究区,利用Landsat 8OLI数据和MODIS数据的红光波段和近红外波段进行融合方法测试。结果表明:利用LASTARFM模型得到的融合影像,与真实影像NDVI相关系数达到0.92;在局部空间自相关性低的区域比STARFM模型影像反映出更多地物细节,具有更高的融合精度;在土地利用类型发生显著变化的区域与真实影像具有一定差异。     

Correction of bottom influence in ocean colour satellite images of shallow water areas of the Baltic Sea

Ocean colour satellite measurements are mainly disturbed by the atmosphere, the sea surface and the sea bottom in shallow water areas. In such areas special features of bottom topography can be recognized in satellite images of the visible spectrum and the derived concentrations of water constituents are often misinterpreted. The influence of the sea bottom depends on the water depth, the transmission of the water column, the reflectance of the water, the reflectivity of the bottom materials and the used spectral channels of satellite sensors. The influence of the sea bottom on the spectral reflectance at the sea surface is discussed here on the basis of model computations. The calculations are realized for examples of shallow water areas of the Baltic Sea. Furthermore, a technique for the identification of sea bottom disturbed pixels in satellite images and for the elimination of bottom effects is presented. A linear regression analysis between the bottom depth and the spectral reflectance in the different satellite sensor channels is used in order to test the correlation between these two variables. If a relationship exists, the reflectances have to be corrected. This procedure and the elimination of the bottom influence will be explained for specific satellite systems.     

Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation

A massive floating green macroalgae bloom (GMB) has occurred for several years consecutively in the Yellow Sea since 2007. In view of the rapid growth of green macroalgae, early detection of its patches at first appearance by satellite imagery is of importance, and the central issue is the selection of appropriate satellite data. As a first step towards this goal, based on quasi-synchronous satellite images of HJ-1A/B (China Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting) charge-coupled devices (CCDs), Environmental Satellite (ENVISAT) Advanced Synthetic Aperture Radar (ASAR) and TERRA Moderate Resolution Imaging Spectroradiometer (MODIS), GMB monitoring abilities by these data were compared. The average percentage difference (APD) of the GMB areas derived by ASAR and CCD was less than 15%, which may be partly attributed to the inability of synthetic aperture radar (SAR) data to detect macroalgae suspended beneath the sea surface. The macroalgae area extracted by MODIS was over two times of that extracted by CCD, which was mainly explained by the difference in their spatial resolutions (250 vs 30 m). The effects of the configuration of sensor bands and the aerosol optical properties on the comparison result were found to be negligible, and the underlying reason is analysed by atmosphere radiative transfer modelling. With satellite images, the drifting velocity of macroalgae patches was estimated to be about 0.21 m s^–1, which was in agreement with the surface current field numerically simulated by the Hybrid Coordinate Ocean Model (HYCOM). It indicates that numerical modelling can aid in deduction of the situation of the patches when satellite data are not available, and on the other hand, satellite data can be used to estimate sea-surface currents through monitoring the movement of green algae. By a comprehensive comparison of available satellite data in operation, for the early detection of macroalgae patches and warning of a massive bloom, CCD data from the HJ-1A/B constellation was preferred, with 30 m spatial resolution, 700 km swath width and 2 day revisiting period. SAR data may be an effective supplement, which can avoid the effects of bad weather (cloud, fog and haze) on optical satellite monitoring.     

Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province,Indonesia

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination (R ²) of 0.97490 and 0.92298, whereas the root mean square errors (RMSEs) were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.     

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.     

Downscaling MODIS-derived water maps with high-precision topographic data in a shallow lake

ABSTRACT

Remotely sensed imagery is the most efficient and widely used data source to monitor the water area changes. However, a trade-off always exists between temporal resolution and spatial resolution for satellite images. Taking the southern Dongting Lake as an example, this study was conducted to develop a method of downscaling the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived coarse spatial resolution water maps in shallow lakes with high-precision digital elevation model. The main principle of the method is to identify and adjust the horizontal location errors of the waterlines extracted from coarse-resolution data by analysing and modifying the elevation leaps using finer-scale topography information. Moving average filter was used to smooth the errors of waterlines caused by the geometric inaccuracies and classification uncertainties of the coarse data. The optimal local window size of the moving average filter was selected automatically using an exponential decay function model and a curvature algorithm for each pixel in the waterlines. In reference to Landsat Thematic Mapper data, the accuracy of the downscaling result is distinctly higher than that of the original MODIS normalized difference water index-derived water maps. The presented method is proved to be an effective tool for acquiring water maps of shallow lake with high spatio-temporal resolution using coarse- or moderate-resolution satellite imagery and high-precision topographic data.     

Mapping surface temperature in large lakes with MODIS data

Satellite sensor MODIS on two platforms can produce Sea Surface Temperature over certain regions about three to four times per day. Our objective was to test if the MODIS SST product can be applied for lakes whose surface areas are large enough to be observed at the MODIS spatial resolution and to compare the satellite-derived lake surface temperatures with in situ measurements. Surface temperatures for Lakes Vänern and Vättern in Sweden, two of the largest European lakes, are extracted from the MODIS/Terra images for period 2001-2003. The results are analyzed on different quality levels, as all MODIS L2 products are equipped with an additional quality flag. We present temperature development over 2001-2003, and show the capability of the MODIS SST product to couple the known thermodynamical features in the lakes under study, where temperature varies greatly with space and time. These results can complement lake monitoring programs anywhere.     

A physical explanation of the variation in threshold for delineating terrestrial water surfaces from multi-temporal images: effects of radiometric correction

Multi-temporal satellite images are widely used to delineate objects of interest for monitoring surface changes. Threshold value(s) are often determined from a histogram of a delineation index. However, the threshold determined may vary and be case-dependent, with images taken at different times. Although the variation is well known, its cause remains unclear, and this raises doubts about the reliability of the classification results. This study selects three widely used indices, the near-infrared (NIR) band, the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), all of which can be used to delineate water surfaces. Our theoretical analysis reveals that sensor calibration, the Sun–target–satellite geometry and the atmospheric optical properties create synthetic effects on the satellite's digital number (DN) and, subsequently, on the thresholds for delineation. The DN-based threshold has a significant dependence on the reflectance-based counterpart, which has been proved with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the Poyang Lake region of China. Our results show that a DN-based threshold is generally higher than a reflectance-based one, and ～90% of the difference is accounted for by temporal influences. A quantification of the temporal influences provides a physical explanation to the variation in thresholds, and the findings should be valuable for improving the reliability of long-term studies using multi-temporal images.     

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.     

Validating remotely sensed land surface fluxes in heterogeneous terrain with large aperture scintillometry

The Large Aperture Scintillometer (LAS) has emerged as one of the best tools for quantifying areal averaged fluxes over heterogeneous land surfaces. This is particularly useful as a validation of surface energy fluxes derived from satellite sources. We examine how changes in surface source area contributing to the scintillometer and eddy covariance measurements relate to satellite derived estimates of sensible heat flux. Field data were collected on the Konza Prairie in Northeastern Kansas, included data from two eddy covariance towers: one located on an upland, relatively flat homogeneous area, and the second located in a lowland area with generally higher biomass and moisture conditions. The large aperture scintillometer spanned both the upland and lowland areas and operated with a path length of approximately 1 km specifically to compare to Moderate Resolution Imaging Spectroradiometer (MODIS) derived estimates of surface fluxes. The upland station compares well with the LAS (correlation of 0.96), with the lowland station being slightly worse (correlation of 0.84). Data from the MODIS sensor was used to compute surface fluxes using the ‘triangle’ method which combines the remotely sensed data with a soil-vegetation-atmosphere-transfer scheme and a fully developed atmospheric boundary layer model. The relative contribution to the surface observations is estimated using a simple footprint model. As wind direction varies, the relative contribution of upland and lowland sources contributing to the LAS measurements varies while the MODIS pixel contribution remains relatively constant. With the footprint model, we were able to evaluate the relationship between the LAS observations and the remotely sensed estimates of the surface energy balance. The MODIS derived sensible heat flux values correspond better to the LAS measurements (percentage error: 0.04) when there was a larger footprint compared to a time with a smaller footprint (percentage error:??0.13). Results indicate that the larger the footprint, the better the agreement between satellite and surface observations.