Uncertainties of LAI estimation from satellite imaging due to atmospheric correction

REMOTE SENSING OF ENVIRONMENT

卷:153

Article

Mannschatz, T.^1,2,4  Pflug, B.⁵  Borg, E.⁶  Feger, K. -H.⁴  Dietrich, P.^2,3 

[1] UN Univ, Inst Integrated Management Mat Fluxes & Resources, D-01067 Dresden, Germany

[2] UFZ Helmholtz Ctr Environm Res, Dept Monitoring & Explorat Technol, D-04318 Leipzig, Germany

[3] Univ Tubingen, Tubingen, Germany

[4] Tech Univ Dresden, Inst Soil Sci & Site Ecol, D-01737 Tharandt, Germany

[5] German Aerosp Ctr DLR, Remote Sensing Technol Inst, D-12489 Berlin, Germany

[6] German Aerosp Ctr DLR, German Remote Sensing Data Ctr, D-17235 Neustrelitz, Germany

关键词: LAI estimation;    Hydrological modelling;    Uncertainty analysis;    Sensitivity analysis;    Satellite imaging;    Atmospheric correction;    ATCOR;   

DOI  :  10.1016/j.rse.2014.07.020

来源: Elsevier

【 摘 要 】

Leaf area index (LAI) is a plant development indicator that as an input parameter strongly influences several relevant hydrological processes represented in Soil-Vegetation-Atmosphere-Transfer (SVAT) models. Generally, temporal measurement or monitoring of LAI is challenging or even impossible in remote areas. High-temporal resolution remote sensing imaging can be used to estimate LAI from vegetation indices calculated from band ratios. This paper shows the sensitivity of LAI estimation from satellite imaging to atmospheric correction (with ATCOR) and evaluates the effects of LAI uncertainty on water balance modelling. LAI as a SVAT model input parameter was estimated based on the empirical relationship between field measurements, and the vegetation indices NDVI (Normalized-Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and SARVI (Soil-Atmosphere Resistant Vegetation Index) for six RapidEye images obtained between 2011 and 2012. In summary, we found that the ATCOR parameter 'visibility' has the strongest influence on LAI estimation. Likewise, atmospherically corrected successive images gathered from around the same time period had low LAI differences (mean absolute difference of 0.09 +/- 0.08) on overlapping image areas. This uncertainty is negligible in SVAT modelling in most cases, thereby allowing mosaicked successive atmospherically corrected images to be used. We showed that LAI uncertainties arising from atmospheric correction (ATCOR 3) can translate into small (LAI +/- 0.1 approximate to evapotranspiration +/- 0.9%, interception +/- 2.5%, evaporation +/- 33%, transpiration +/- 0.7%) to moderate (LAI +/- 0.3 approximate to evapotranspiration +/- 4.1%, interception +/- 7.5%, evaporation +/- 9.9%, transpiration +/- 2.4%) SVAT model uncertainty. (C) 2014 Elsevier Inc. All rights reserved.

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