Evaluating multispectral and hyperspectral satellite remote sensing data for estimating winter wheat growth parameters at regional scale in the North China plain

Wolfgang Koppe, Fei Li, Martin L. Gnyp, Yuxin Miao, Liangliang Jia, Xinping Chen, Fusuo Zhang, Georg Bareth

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Timely monitoring of crop growth status at large scale is crucial for improving regional crop management decisions. The main objective of the recent study is a model development to predict and estimate crop parameters, here biomass, plant N concentration and plant height, based on multi-and hyperspectral satellite data. In this contribution, the focus is on relating orbital multispectral (EO-1 ALI) and hyperspectral (EO-1 Hyperion) measurements to winter wheat parameters for regional level applications. The study was conducted in Huimin County, Shandong Province of China in the growing season of 2005/2006 involving three big winter wheat fields managed by different farmers. Winter wheat growth parameters including aboveground biomass, plant N concentration and plant height were collected at different growth stages. Three different predicting models were investigated: traditional vegetation indices calculated from broad and narrow bands, and Normalized Ratio Indices (NRI) calculated from all possible two-band combinations of Hyperion between 400 and 2,500 nm. The results indicated that TVI performed best among the tested vegetation indices using either broad (R2=0.69, 0.32 and 0.64 for biomass, N concentration and plant height, respectively) or narrow (R2=0.71, 0.33 and 0.65 for biomass, N concentration and plant height, respectively) bands. The best performing Normalized Ratio Index (NRI) selected through band combination analysis were significantly better than TVI, achieving R2 of 0.83, 0.81 and 0.79 for biomass, plant N concentration and plant height, respectively. The different NRI models use wavebands from the near infrared (NIR) (centered at 874, 732, and 763 nm) and short wave infrared (SWIR) (centered at 1,225 and 1,305 nm) spectrum with varying bandwidth between 10 and 190 nm. The result of this study suggest that vegetation indices derived from NIR- and SWIR-Hyperion spectrum are better predictors of plant aboveground biomass, nitrogen concentration and plant height than indices derived from only visible spectrum. More studies are needed to further evaluate the results using data from more diverse conditions.

Original languageEnglish (US)
Pages (from-to)167-178
Number of pages12
JournalPhotogrammetrie, Fernerkundung, Geoinformation
Volume2010
Issue number3
DOIs
StatePublished - Jul 1 2010

Keywords

  • ALI
  • Biomass
  • Hyperion
  • Hyperspectral
  • Imaging spectrometry
  • Multispectral
  • Nitrogen concentration
  • Vegetation indices
  • Winter wheat

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