Geography Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1641

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Start date: 2000End date: 2009

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Now showing 1 - 10 of 17
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    An Optimization Algorithm for Separating Land Surface Temperature and Emissivity from Multispectral Thermal Infrared Imagery
    (Institute of Electrical and Electronics Engineers, 2001-02) Liang, Shunlin
    Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with bands presents equations in + 1 unknowns ( spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER). The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way. The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5 , 70.2% within 1 and 84% within 1.5 ), although improvements are still needed.
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    Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods
    (Institute of Electrical and Electronics Engineers, 2001-11) Liang, Shunlin; Fang, Hongliang; Chen, Mingzhen
    To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. After reviewing historical development of atmospheric correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to atmospheric correction.
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    Atmospheric Correction of Landsat ETM+ Land Surface Imagery: II. Validation and Applications
    (Institute of Electrical and Electronics Engineers, 2002) Liang, Shunlin; Morisette, Jeffrey T.; Fang, Hongliang; Chen, Mingzhen; Shuey, Chad J.; Daughtry, Craig S. T.; Walthall, Charles L.
    This is the second paper of the series on atmospheric correction of ETM+ land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to 1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements; 2) apply this algorithm to correct MODIS and SeaWiFS imagery; and 3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.
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    Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation
    (Institute of Electrical and Electronics Engineers, 2003-09) Liang, Shunlin; Fang, Hongliang
    Leaf area index () is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimateLAIfromLandsat-7 Enhanced ThematicMapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, MD were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.
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    A Direct Algorithm for Estimating Land Surface Broadband Albedos From MODIS Imagery
    (Institute of Electrical and Electronics Engineers, 2003-01) Liang, Shunlin
    Land surface albedo is a critical variable needed in land surface modeling. The conventional methods for estimating broadband albedos rely on a series of steps in the processing chain, including atmospheric correction, surface angular modeling, and narrowband-to-broadband albedo conversions. Unfortunately, errors associated with each procedure may be accumulated and significantly impact the accuracy of the final albedo products. In an earlier study, we developed a new direct procedure that links the top-of-atmosphere spectral albedos with land surface broadband albedos without performing atmospheric correction and other processes. In this paper, this method is further improved in several aspects and implemented using actual Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Several case studies indicated that this new method can predict land surface broadband albedos very accurately and eliminate aerosol effects effectively. It is very promising for global applications and is particularly suitable for nonvegetated land surfaces. Note that a Lambertian surface has been assumed in the radiative transfer simulation in this paper as a first-order approximation; this assumption can be easily removed as long as a global bidirectional reflectance distribution function climatology is available,
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    Estimation and Validation of Land Surface Broadband Albedos and Leaf Area Index From EO-1 ALI Data
    (Institute of Electrical and Electronics Engineers, 2003-06) Liang, Shunlin; Fang, Hongliang; Kaul, Monisha; Van Niel, Tom G.; McVicar, Tim R.; Pearlman, Jay S.; Huemmrich, Karl Fred; Walthall, Charles L.; Daughtry, Craig S. T.
    The Advanced Land Imager (ALI) is a multispectral sensor onboard the National Aeronautics and Space Administration Earth Observing 1 (EO-1) satellite. It has similar spatial resolution to Landsat-7 Enhanced Thematic Mapper Plus (ETM+), with three additional spectral bands. We developed new algorithms for estimating both land surface broadband albedo and leaf area index (LAI) from ALI data. A recently developed atmospheric correction algorithm for ETM+ imagery was extended to retrieve surface spectral reflectance from ALI top-of-atmosphere observations. A feature common to these algorithms is the use of new multispectral information from ALI. The additional blue band of ALI is very useful in our atmospheric correction algorithm, and two additional ALI near-infrared bands are valuable for estimating both broadband albedo and LAI. Ground measurements at Beltsville, MD, and Coleambally, Australia, were used to validate the products generated by these algorithms.
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    Special Issue on Global Land Product Validation
    (Institute of Electrical and Electronics Engineers, 2006-07) Liang, Shunlin; Baret, Frédéric; Morisette, Jeffrey T.
    Overview of the Special Issue on Global Land Product Validation: In parallel with the recent bloom of sensors providing frequent medium-resolution observations (Fig. 1), global land products have been increasingly developed and released within the community. The raw data acquired by these sensors are transformed into higher level products that can be more easily exploited by the user community. In many cases, multiple products are developed from each sensor and similar products derived from different sensors. With this, users need access to quantitative information on product uncertainties to help them assess the most suitable product, or combination of products for their specific needs. As remote sensing observations are generally merged with other sources of information or assimilated within process models, evaluation of product accuracy is required. Making quantified accuracy information available to the user can ultimately provide developers the necessary feedback for improving the products, and can possibly provide methods for their fusion to construct a consistent long-term series of surface status.
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    An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery
    (Institute of Electrical and Electronics Engineers, 2004-04) Liang, Shunlin; Fang, Hongliang
    There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.
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    Estimation of Systematic Errors of MODIS Thermal Infrared Bands
    (Institute of Electrical and Electronics Engineers, 2006-10) Liang, Shunlin; Liu, Ronggao G.; Liu, Jiyuan Y.
    This letter reports a statistical method to estimate detector-dependent systematic error in Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) Bands 20–25 and 27–36. There exist scan-to-scan overlapped pixels in MODIS data. By analyzing a sufficiently large amount of those most overlapped pixels, the systematic error of each detector in the TIR bands can be estimated. The results show that the Aqua MODIS data are generally better than the Terra MODIS data in 160 MODIS TIR detectors. There are no detector-dependent systematic errors in Bands 31 and 32 for both Terra and Aqua MODIS data. The maximum detector errors are 3.00 K in Band 21 of Terra and −8.15 K in that of Aqua for brightness temperatures of more than 250 K.
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    Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation
    (American Geophysical Union, 2003-03-08) Liang, Shunlin; Lucht, Wolfgang; Jin, Yufang; Schaaf, Crystal B.; Woodcock, Curtis E.; Gao, Feng; Li, Xiaowen
    The evaluation of the first available satellite-based global albedo product at 1-km resolution is essential for its application in climate studies. We evaluate the accuracy of the Moderate-Resolution Imaging Spectroradiometer (MODIS) albedo product using available field measurements at Surface Radiation Budget Network (SURFRAD) and Cloud and Radiation Testbed–Southern Great Plains (CART/SGP) stations and examine the consistency between the MODIS surface albedos and the Clouds and Earth’s Radiant Energy System (CERES) top-of-the-atmosphere albedos as well as historical global albedos from advanced very high resolution radiometer (AVHRR) and Earth Radiation Budget Experiment (ERBE) observations. A comparison with the field measurements shows that the MODIS surface albedo generally meets an absolute accuracy requirement of 0.02 for our study sites during April–September 2001, with the root mean square errors less than 0.018. Larger differences appear in the winter season probably due to the increased heterogeneity of surface reflectivity in the presence of snow. To examine the effect of spatial heterogeneity on the validation of the MODIS albedos using fine resolution field measurements, we derive an intermediate albedo product from four Landsat Enhanced Thematic Mapper Plus (ETM+) images at 30-m spatial resolution as a surrogate for the distributed field measurements. The surface albedo is relatively homogeneous over the study stations in growing seasons, and therefore the validation during April–September is supported. A case study over three SURFRAD stations reveals that the MODIS bidirectional reflectance distribution function model is able to capture the solar zenith angle dependence of surface albedo as shown by the field measurements. We also find that the MODIS surface shortwave albedo is consistent with the contemporary and collocated CERES top-of atmosphere albedos derived directly from broadband observations. The MODIS albedo is also well correlated with historical surface albedos derived from AVHRR and ERBE observations, and a high bias of 0.016 and a low bias of 0.034 compared to those of the latter albedos are reasonable considering the differences in instruments and retrieval algorithms as well as environmental changes.