Vegetation monitoring and yield prediction from NOAA-AVHRR GAC data in the Argentinean Pampa

Herve Kerdiles; G. Magrin; Cesar M. Rebella; B. Seguin

doi:10.1117/12.200754

31 January 1995 Vegetation monitoring and yield prediction from NOAA-AVHRR GAC data in the Argentinean Pampa

Herve Kerdiles, G. Magrin, Cesar M. Rebella, B. Seguin

Proceedings Volume 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources; (1995) https://doi.org/10.1117/12.200754
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

Ten years of NOAA GAC data over the Argentinean Pampa were analyzed in relation with climate and crop production. Correlations between crop yield and monthly NDVI (cumulated or not, weighted by the global radiation or not) reached 0.87 for wheat, 0.85 for soybean and 0.83 for corn, despite the classical limitations of AVHRR data (mixed response, atmospheric and directional noise, sensor calibration), the monthly frequency and the size of the test areas (10,000 km²). The quality of these results was partly due to the extensive character of the Pampa's cropping system since the correlation between final yield and NDVI relies on the following two hypothesis: NDVI can predict biomass and biomass is a good indicator of final grain yield. The best correlations were observed with the NDVI sensed at maximum green biomass, hence permitting yield estimations one to two months before harvest. Standard errors of regression were of 0.22, 0.17, and 0.63 t/ha for wheat, soybean, and maize respectively, for a mean yield around 1.7, 2.2, and 3.8 t/ha, respectively. Last, the complement between NDVI data and crop physiologically based models was examined. Despite the data related limitations, the relationship between CERES wheat predicted LAI and NOAA monthly GAC NDVI appeared as promising.

Citation Download Citation

Herve Kerdiles, G. Magrin, Cesar M. Rebella, and B. Seguin "Vegetation monitoring and yield prediction from NOAA-AVHRR GAC data in the Argentinean Pampa", Proc. SPIE 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources, (31 January 1995); https://doi.org/10.1117/12.200754

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