Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data

Abstract

Leaf area index (LAI) is a fundamental land surface parameter. Recently, several agencies have produced 1 km gridded LAI estimates over Canada. These products are difficult to validate because of their coarse-resolution mapping units and large spatial extent. A Canada-wide sampling of Landsat-5 thematic mapper (TM) and Landsat-7 enhanced thematic mapper plus (ETM+) images was used to produce fine-resolution (30 m) LAI estimates using a uniform methodology traceable to in situ measurements. Residuals between coarse-scale LAI estimates based on data from the Satellite pour l'observation de la terre (SPOT) VEGETATION (VGT) sensor, and Landsat LAI estimates were quantified. Furthermore, a number of scaling treatments were performed on the Landsat scenes to isolate the relative contributions of land cover and reflectance scaling versus atmospheric correction and bidirectional distribution function (BRDF) normalization on coarse-scale LAI errors. Uncertainties because of atmospheric correction and acquisition geometry normalization were identified as the largest source of scene-wide bias errors. Scaling to 25 km resulted in a reduction of absolute and relative error on the order of 30%, suggesting only limited error reduction is achieved by aggregation. Scene average residuals between Landsat and VGT LAI on the order of ±1 LAI unit were observed. Larger residuals are noted over mountainous areas where shading and BRDF normalization are problematic and over agricultural regions where a vegetation index sensitive to atmospheric effects was used.