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Item Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods(Institute of Electrical and Electronics Engineers, 2001-11) Liang, Shunlin; Fang, Hongliang; Chen, MingzhenTo 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.Item 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.Item Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation(Institute of Electrical and Electronics Engineers, 2003-09) Liang, Shunlin; Fang, HongliangLeaf 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.Item 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.Item An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery(Institute of Electrical and Electronics Engineers, 2004-04) Liang, Shunlin; Fang, HongliangThere 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.Item Estimation of incident Photosynthetically Active Radiation from MODIS Data(American Geophysical Union, 2006-08-08) Liang, Shunlin; Zheng, Tao; Liu, Ronggao; Fang, Hongliang; Tsay, Si-Chee; Running, StevenIncident photosynthetically active radiation (PAR) is a key variable needed by almost all terrestrial ecosystem models. Unfortunately, the current incident PAR products estimated from remotely sensed data at spatial and temporal resolutions are not sufficient for carbon cycle modeling and various applications. In this study, the authors develop a new method based on the look-up table approach for estimating instantaneous incident PAR from the polar-orbiting Moderate Resolution Imaging Spectrometer (MODIS) data. Since the top-of-atmosphere (TOA) radiance depends on both surface reflectance and atmospheric properties that largely determine the incident PAR, our first step is to estimate surface reflectance. The approach assumes known aerosol properties for the observations with minimum blue reflectance from a temporal window of each pixel. Their inverted surface reflectance is then interpolated to determine the surface reflectance of other observations. The second step is to calculate PAR by matching the computed TOA reflectance from the look-up table with the TOA values of the satellite observations. Both the direct and diffuse PAR components, as well as the total shortwave radiation, are determined in exactly the same fashion. The calculation of a daily average PAR value from one or two instantaneous PAR values is also explored. Ground measurements from seven FLUXNET sites are used for validating the algorithm. The results indicate that this approach can produce reasonable PAR product at 1 km resolution and is suitable for global applications, although more quantitative validation activities are still needed.Item Improving the Estimation of Leaf Area Index from Multispectral Remotely Sensed Data(2003-10-27) Fang, Hongliang; Liang, Shunlin; Prince, Stephen D.; Townshend, John R.; Weismiller, Richard; GeographyLeaf Area Index (LAI) is an important structural property of surface vegetation. Many algorithms use LAI in regional and global biogeochemical, ecological, and meteorological applications. This dissertation reports several new, improved methods to estimate LAI from remotely sensed data. To improve LAI estimation, a new atmospheric correction algorithm was developed for the Enhanced Thematic Mapper Plus (ETM+) imagery. It can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance under general atmospheric and surface conditions. This method was validated using ground measurements at Beltsville, Maryland. Several examples are given to correct AVIRIS (Airborne Visible/Infrared Imaging Spectrometer), MODIS (Moderate Resolution Imaging Spectroradiometer) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor) data using the new algorithm. Next, a genetic algorithm (GA) was incorporated into the optimization process of radiative transfer (RT) model inversion for LAI retrieval. Different ETM+ band combinations and the number of "genes" employed in the GA were examined to evaluate their effectiveness. The LAI estimates from ETM+ using this method were reasonably accurate when compared with field measured LAI. A new hybrid method, which integrates both the RT model simulation and the non-parametric statistical methods, was developed to estimate LAI. Two non-parametric methods were applied, the neural network ((NN) algorithms and the projection pursuit regression (PPR) algorithms. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Both atmospherically corrected surface reflectances and raw top-of-atmosphere (TOA) radiances from ETM+ were tested. It was found that the best way to estimate LAI was to use the red and near infrared band combination of surface reflectance. In an application of this hybrid method to MODIS, the PPR and NN methods were compared. MODIS LAI standard products (MOD15) were found to have larger values than my results in the study area.