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Titolo:
Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region
Autore:
Qi, J; Kerr, YH; Moran, MS; Weltz, M; Huete, AR; Sorooshian, S; Bryant, R;
Indirizzi:
ARS, USDA, Water Conservat Lab, Phoenix, AZ USA ARS Phoenix AZ USAARS, USDA, Water Conservat Lab, Phoenix, AZ USA CNES, CESBIO, Toulouse, France CNES Toulouse FranceCNES, CESBIO, Toulouse, France ARS, USDA, Ft Collins, CO USA ARS Ft Collins CO USAARS, USDA, Ft Collins, CO USA Univ Arizona, Dept Soil Water & Environm Sci, Tucson, AZ USA Univ ArizonaTucson AZ USA ept Soil Water & Environm Sci, Tucson, AZ USA Univ Arizona, Dept Hydrol & Water Resources, Tucson, AZ 85721 USA Univ Arizona Tucson AZ USA 85721 & Water Resources, Tucson, AZ 85721 USA
Titolo Testata:
REMOTE SENSING OF ENVIRONMENT
fascicolo: 1, volume: 73, anno: 2000,
pagine: 18 - 30
SICI:
0034-4257(200007)73:1<18:LAIEUR>2.0.ZU;2-8
Fonte:
ISI
Lingua:
ENG
Soggetto:
ESTIMATING AGRONOMIC VARIABLES; CANOPY REFLECTANCE MODELS; ADJUSTED VEGETATION INDEX; BIDIRECTIONAL REFLECTANCE; SURFACE REFLECTANCE; HAPEX-SAHEL; SAIL MODEL; INVERSION; ANGLE; PROSPECT;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
43
Recensione:
Indirizzi per estratti:
Indirizzo: Qi, J Michigan State Univ, Dept Geog, E Lansing, MI 48824 USA Michigan State Univ E Lansing MI USA 48824 E Lansing, MI 48824 USA
Citazione:
J. Qi et al., "Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region", REMOT SEN E, 73(1), 2000, pp. 18-30

Abstract

The amount and spatial and temporal dynamics of vegetation are important information in environmental studies and agricultural practices. There has been a great deal of interest in estimating vegetation parameters and their spatial and temporal extent using remotely sensed imagery. There are primarily two approaches to estimating vegetation parameters such as leaf area index (LAI). The first one is associated with computation of spectral vegetation indices (SVI) from radiometric measurements. This approach uses an empirical or modeled LAI-SVI relation between remotely sensed variables such asSVI and biophysical variables such as LAI. The major limitation of this empirical approach is that there is no single LAI-SVI equation (with a set ofcoefficients) that can be applied to remote-sensing images of different surface types. The second approach involves using bidirectional reflectance distribution function (BRDF) models. It inverts a BRDF model with radiometric measurements to estimate LAI wing an optimization procedure. Although this approach has a theoretical basis and is potentially applicable to varyingsurface types, its primary limitation is the lengthy computation time and difficulty of obtaining the required input parameters by the model. In thisstudy, we present ct strategy that combines BRDF models and conventional LAI-SVI approaches to circumvent these limitations. The proposed strategy runs implemented in three sequential steps. In the first step, a BRDF model was inverted with a limited number of dam points or pixels to produce a training data set consisting of leaf area index and associated pixel values. Inthe second step, the training data set passed through a quality control procedure to remove outliers from the inversion procedure. In the final step,the training data set was used either to fit an LAI-SVI equation or to train a neural fuzzy system. The best fit equation or the trained fuzzy systemwas then applied to large-scale remote-sensing imagery to map spatial LAI distribution. This approach was applied to Landsat TM imagery acquired in the semiarid southeast Arizona and AVHRR imagery over the Hapex-Sahel experimental sites near Niamy, Niger. The results were compared with limited ground-based LAI measurements and suggested that the proposed approach producedreasonable estimates of leaf area index over large areas in semiarid regions. This study was not intended to show accuracy improvement of LAI estimation from remotely sensed data. Rather, it provides an alternative that is simple and requires little knowledge of study target and few ground measurements. (C) Elsevier Science Inc., 2000.

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Documento generato il 29/11/20 alle ore 08:40:56