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11 October 2007 Validating the MODIS LAI product by scaling up LAI measurements at a VALERI alpine meadow site in China
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Abstract
The sampling protocol adopted during a field campaign at an Alpine meadow site (Shandan site), during July 2002 is based on the so-called "Valeri" protocol (VALERI). The field campaign LAI measurements in Shandan are scaled up to 30×30 m2 raster maps based on Landsat ETM+ imagery. Regression analysis is applied to construct empirical transfer functions for the determination of Leaf Area Index (LAI) raster imagery from ETM+ Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) data. Subsequently, the scaling up of the LAI raster maps is performed by the aggregation of the 30x30 m2 data into 1×1 km2 pixels by calculating the average LAI values for the low resolution pixels. The up-scaled data are used to validate the MODIS LAI product at the Shandan site. A power regression model (LAI=2.3758*NDVI3.5216, R2=0.66, P<0.01), established between field measured LAI and ETM+ NDVI, elicits a high statistical significance. A linear regression model (LAI=0.1798*SR-0.3574, R2=0.55, P<0.01) is established between field measured LAI and ETM+ SR. The MODIS LAI product correlates best with the ETM+ LAI transfer function obtained with NDVI data. Its R2 reaches 0.46, its slope 0.97, but the intercept is 0.7, which suggests that MODIS LAI is systematically underestimated. The results illustrate that LAI measured with a LAI-2000 instrument at the VALERI Shandan site leads to an underestimation of the MODIS LAI product. A plausible cause for the systematic underestimation related with the LAI field measurements is discussed.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mingguo Ma, Frank Veroustraete, Ling Lu, Xin Li, Reinhart Ceulemans, Jan Bogaert, Chunlin Huang, Tao Che, and Qinghan Dong "Validating the MODIS LAI product by scaling up LAI measurements at a VALERI alpine meadow site in China", Proc. SPIE 6742, Remote Sensing for Agriculture, Ecosystems, and Hydrology IX, 67420V (11 October 2007); doi: 10.1117/12.737560; https://doi.org/10.1117/12.737560
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