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Semiarid rangelands are very sensitive to global climatic change; studies of their biophysical attributes are crucial to understanding the dynamics of rangeland ecosystems under human disturbance. In the Santa Rita Experimental Range, AZ, the vegetation has changed considerably, and there have been many management activities applied. This study calculates seven surface variables: the enhanced vegetation index, the normalized difference vegetation index (NDVI), surface albedos (total shortwave, visible, and near-infrared), leaf area index (LAI), and the fraction of photosynthetically active radiation (FPAR) absorbed by green vegetation from the Enhanced Thematic Mapper (ETM+) data. Comparison with the Moderate Resolution Imaging Spectroradiometer vegetation index and albedo products indicates they agree well with our estimates from ETM+, while their LAI and FPAR are larger than from ETM+. Human disturbance has significantly changed the cover types and biophysical conditions. Statistical tests indicate that surface albedos increased and FPAR decreased following tree-cutting disturbances. The recovery will require more than 67 years and is about 50% complete within 40 years at the higher elevation. Grass cover, vegetation indexes, albedos, and LAI recovered from cutting faster at the higher elevation. Woody plants, vegetation indexes, and LAI have recovered to their original characteristics after 65 years at the lower elevation. More studies are needed to examine the spectral characteristics of different ground components. Index Terms--Albedo, Enhanced Thematic Mapper (ETM+), fraction of photosynthetically active radiation (FPAR), leaf area index (LAI), Moderate Resolution Imaging Spectroradiometer (MODIS), rangeland, remote sensing.

Leaf area index (LAI) 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 estimate LAI from Landsat-7 Enhanced Thematic Mapper 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. Index Terms--Enhanced Thematic Mapper Plus (ETM+), leaf area index (LAI), neural networks (NNs), radiative transfer, soil reflectance index (SRI).

The Advanced Land Imager (ALI) is a multispectral sensor onboard the National Aeronautics and Space Administration Earth Observing I (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. (;round measurements at Beltsville, MD, and Coleambally, Australia, were used to validate the products generated by these algorithms. Index Terms--Atmospheric correction, Advanced Land Imager (ALI), broadband albedos, Earth Observing I (EO-I), leaf area index (LAI), validation.

This is the second paper of the series on atmospheric correction of Enhanced Thematic Mapper-Plus (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 Moderate-Resolution Imaging Spectroradiometer (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. Index Terms--Atmospheric correction, broadband albedos, change detection, classification, validation.

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. Index Terms--Adjacency effect, aerosol, atmospheric correction, land surface, reflectance.

Forest leaf area index (LAI) and clumping index (CI) are critical structural parameters to study the biomass, biogeochemical cycles, ecological simulation, and radiation transfer in a forest system. The ground-based measurements by traditional methods are costly, time-consuming, and are sensitive to variations in solar illumination conditions. Global Positioning System (GPS) satellites transfer L-band signals, and the presence of the forest canopy leads to an increase in noise. The GPS satellite's signal strength is generally described by the carrier-to-noise-density ratio (C/N0). This study explores the potential of using the GPS satellite C/N0 to estimate forest canopy structural parameters. We performed experiments at 16 forest plots in northeastern China. LAI, CI, and canopy closure were estimated by digital hemispherical photography (DHP) using the CAN_EYE software. The GPS satellite C/N0 was recorded with a modified smartphone application, GPSTest. The relationship between the forest canopy structural parameters and satellite C/N0 was analyzed for those observations with satellite elevation angle (SEA) > 30°. The results show that both the true LAI and the effective LAI have strong linear and polynomial correlation with C/N0 and the coefficient of determination (R2) is larger than 0.7. CI is linearly related to C/N0(R2 = 0.56). The linear relationship between the canopy closure and C/N0 is much lower (R2= 0.28). Accounting for the SEA effect, LAI can be estimated with R2 > 0.8, and CI with R2 = 0.6. LAI and CI can be well estimated from C/N0 when the SEA effect is accounted for. In general, the GPS C/N0 provides an alternative method to estimate forest canopy structural parameters. [ABSTRACT FROM AUTHOR]

Forest leaf area index (LAI) and clumping index (CI) are critical structural parameters to study the biomass, biogeochemical cycles, ecological simulation, and radiation transfer in a forest system. The ground-based measurements by traditional methods are costly, time-consuming, and are sensitive to variations in solar illumination conditions. Global Positioning System (GPS) satellites transfer L-band signals, and the presence of the forest canopy leads to an increase in noise. The GPS satellite's signal strength is generally described by the carrier-to-noise-density ratio (C/N0). This study explores the potential of using the GPS satellite C/N0 to estimate forest canopy structural parameters. We performed experiments at 16 forest plots in northeastern China. LAI, CI, and canopy closure were estimated by digital hemispherical photography (DHP) using the CAN_EYE software. The GPS satellite C/N0 was recorded with a modified smartphone application, GPSTest. The relationship between the forest canopy structural parameters and satellite C/N0 was analyzed for those observations with satellite elevation angle (SEA) > 30°. The results show that both the true LAI and the effective LAI have strong linear and polynomial correlation with C/N0 and the coefficient of determination (R2) is larger than 0.7. CI is linearly related to C/N0(R2 = 0.56). The linear relationship between the canopy closure and C/N0 is much lower (R2= 0.28). Accounting for the SEA effect, LAI can be estimated with R2 > 0.8, and CI with R2 = 0.6. LAI and CI can be well estimated from C/N0 when the SEA effect is accounted for. In general, the GPS C/N0 provides an alternative method to estimate forest canopy structural parameters. [ABSTRACT FROM AUTHOR]