Peak experience, peak performance, and flow: A comparative analysis of positive human experiences.

Compares peak experience (intense joy), peak performance (superior functioning), and flow (intrinsically rewarding experience). Peak experience and peak performance are models of optimal human experiencing and, therefore, are important in personality study. Flow, although not always at a high level, shares many qualities with both constructs. Important attributes shared by all 3 include absorption, valuing, joy, spontaneity, a sense of power, and personal identity and involvement. The topologies also reveal distinguishing characteristics. Peak experience, for example, is mystic and transpersonal; peak performance is transactive, clearly focusing on self as well as the valued object; and flow is fun. Differences among the constructs concerning sense of self and motivation are also noted. (27 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Directional effects in a daily AVHRR land surface temperature dataset over Africa

Land surface temperature (LST) is a key indicator of the land surface state and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. Split-window algorithms have been used with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to estimate instantaneous LST for nearly 20 years. However, the low accuracy of LST retrievals associated with intractable variability has often hindered its wide use. In this study, we developed a six-year daily (day and night) NOAA-14 AVHRR LST dataset over continental Africa. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that some of the AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including "hot spot" effects when no shadows are observed. For example, a woodland case showed that LST measurements within the "hot-spot" geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch). In general, effects are most pronounced over relatively sparse canopies (tree cover <60%), at wet season sun-view angle geometries (principal plane viewing) and early in the satellite lifetime. These results suggest that noise in LST time series may be strongly reduced for some locations and years, and that long-term LST climate data records should be normalized to a single sun-view geometry, if possible. However, much work remains before these can be accomplished.     

Inversion of a physically based bidirectional reflectance model ofvegetation

The challenge in using remote sensing data for the characterization of vegetation is to exploit reflectance measurements to infer biophysical and structural properties of the medium under study. In the present paper, the resolution of this problem was achieved, in the case of an homogeneous canopy, through a physically based bidirectional reflectance factor model. In a first validation phase, some inversions were performed against actual data obtained during the FIFE'89 experiment, and the retrieved parameters were found to be in good agreement with the corresponding in situ measurements. A sensitivity study was conducted afterward with synthetic data sets in order to identify and address some of the difficulties encountered while inverting such models, with a view to improving the accuracy of the retrievals and evaluating the inversion costs. The tackled points concerned not only the influence of the initial estimate values or the repercussions of errors made on the estimation of one of the variables, but also the angular sampling and the effect of simplifying assumptions made at the modeling step     

Developments in the 'validation' of satellite sensor products for the study of the land surface

Increased availability of global satellite sensor data is resulting in an increase in satellite sensor products for global change research and environmental monitoring. The ensuing research and policy directives that will utilize these satellite products puts a high priority on providing statements of their accuracy. The process of quantifying the accuracy of these geophysical products is herein termed 'validation'. This Letter provides examples of international land product 'validation' research and describes a new international forum for coordination within the Committee on Earth Observation Satellites (CEOS) Calibration and Validation Working Group (CVWG).     

Assessment of biases in MODIS surface reflectance due to Lambertian approximation

Using MODIS data and the AERONET-based Surface Reflectance Validation Network (ASRVN), this work studies errors of MODIS atmospheric correction caused by the Lambertian approximation. On one hand, this approximation greatly simplifies the radiative transfer model, reduces the size of the look-up tables, and makes operational algorithm faster. On the other hand, uncompensated atmospheric scattering caused by Lambertian model systematically biases the results. For example, for a typical bowl-shaped bidirectional reflectance distribution function (BRDF), the derived reflectance is underestimated at high solar or view zenith angles, where BRDF is high, and is overestimated at low zenith angles where BRDF is low. The magnitude of biases grows with the amount of scattering in the atmosphere, i.e., at shorter wavelengths and at higher aerosol concentration. The slope of regression of Lambertian surface reflectance vs. ASRVN bidirectional reflectance factor (BRF) is about 0.85 in the red and 0.6 in the green bands. This error propagates into the MODIS BRDF/albedo algorithm, slightly reducing the magnitude of overall reflectance and anisotropy of BRDF. This results in a small negative bias of spectral surface albedo. An assessment for the GSFC (Greenbelt, USA) validation site shows the albedo reduction by 0.004 in the near infrared, 0.005 in the red, and 0.008 in the green MODIS bands.     

The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes

A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.