Multi-temporal hyperspectral and radar remote sensing for estimating winter wheat biomass in the North China Plain

Wolfgang Koppe, Martin L. Gnyp, Simon D. Hennig, Fei Li, Yuxin Miao, Xinping Chen, Liangliang Jia, Georg Bareth

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

This paper illustrates the results obtained in the frame of experimental campaigns carried out on winter wheat fields in the North China Plain from March 2006 to June 2007. Investigations focused on the methodology of estimating biomass on a regional scale with hyperspectral (EO-1 Hyperion) and microwave data (Envisat ASAR). Special importance is drawn to the combined analysis of microwave and optical satellite data for crop monitoring. Since hyperspectral and synthetic aperture radar (SAR) sensors respond to crop characteristics differently, their complementary information content can support the estimation of crop conditions. During the regular field measurements, satellite data from jointing to ripening stages were acquired. Linear regression models between measured surface reflection as well as surface backscatter and wheat's standing biomass were established. For hyperspectral data, the normalized ratio index (NRI) based on 825 nm and 1225 nm wavebands was calculated from 2006 data as input for the regression model. In addition, Envisat ASAR VV polarization data were related to winter wheat crop parameters. Bivariate correlation results from this study indicate that both multi-temporal EO-1 Hyperion as well as Envisat ASAR data provide notable relationships with crop conditions. As expected, linear correlation of hyperspectral data performed slightly better for biomass estimation (R 2 = 0.83) than microwave data (R 2 = 0.75) for the 2006 field survey. Based on the results, hyperspectral Hyperion data seem to be more sensitive to crop conditions. Improvements for crop parameter estimation were achieved by combining hyperspectral indices and microwave backscatter into a multiple regression analysis as a function of crop parameters. Combined analysis was performed for biomass estimation (R 2 = 0.90) with notable improvements in prediction power.

Original languageEnglish (US)
Pages (from-to)281-298
Number of pages18
JournalPhotogrammetrie, Fernerkundung, Geoinformation
Volume2012
Issue number3
DOIs
StatePublished - Jun 1 2012

Keywords

  • Biomass
  • Hyperspectral imaging
  • Multi-spectral
  • SAR
  • Vegetation indices

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