Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data

  • Devendra Singh
  • Published 2011 in
    Int. J. Applied Earth Observation and…

Abstract

Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r2 = 0.71, p≤0.01) compared to green band (mean r2 = 0.53; p≤0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r2 = 0.85, p≤0.01 and r2 = 0.86, p≤0.01) forwheat and sugarcane crops, respectively. Thedifference between theGPPderived fromsynthetic and observed ETM+ (prediction residual)was comparedwith the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97gC/m2 in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46gC/m2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values 2 (mean value of 16.53gC/m in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic gree ETM+ GPP showed good a

DOI: 10.1016/j.jag.2010.06.007

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Cite this paper

@article{Singh2011GenerationAE, title={Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data}, author={Devendra Singh}, journal={Int. J. Applied Earth Observation and Geoinformation}, year={2011}, volume={13}, pages={59-69} }