Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain

Martin L. Gnyp, Georg Bareth, Fei Li, Victoria I.S. Lenz-Wiedemann, Wolfgang Koppe, Yuxin Miao, Simon D. Hennig, Liangliang Jia, Rainer Laudien, Xinping Chen, Fusuo Zhang

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


Crop monitoring during the growing season is important for regional management decisions and biomassprediction. The objectives of this study were to develop, improve and validate a scale independentbiomass model. Field studies were conducted in Huimin County, Shandong Province of China, duringthe 2006-2007 growing season of winter wheat (Triticum aestivum L.). The field design had a multiscaleset-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cul-tivars, to create different biomass conditions: small experimental fields (L1), large experimental fields(L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties,was managed according to farmers' practice while L1 through L3 represented controlled field experi-ments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled foreach spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1field data were used to develop biomass models based on the relation between the winter wheat spectraand biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were inves-tigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIRdomains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2through L4) were used stepwise to validate and improve the models of the lower levels, and to transferthe improved models to the next level. Lastly, the models were transferred and validated at the regionalscale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, whichwere based on the NIR and SWIR domains, performed best with R2> 0.74. All the other indices explainedless than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained81-89% of the biomass variability. These results highlighted that GnyLi and NRI can be used togetherwith hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additionalstudies and analyses are needed to test its replicability in other environmental conditions.

Original languageEnglish (US)
Pages (from-to)232-242
Number of pages11
JournalInternational Journal of Applied Earth Observation and Geoinformation
Issue number1
StatePublished - 2014
Externally publishedYes

Bibliographical note

Funding Information:
This study was financially supported by the International Bureau of the German Federal Ministry of Research and Technology (BMBF, Project number CHN 06/003), the Natural Foundation of China (Project number: 30571080), Program for Changjiang Scholars and Innovative Research Team in University (IRT 0511), and the GIS & RS Group of the Institute of Geography, University of Cologne . Continuous funding support for the field work was provided by the Agricultural Bureau of Huimin County (Director Kang). The authors also want to thank Dr. Fei Yuan from the Minnesota State University for her comments and edits.


  • Biomass
  • Hyperspectral
  • Model development
  • Multiscale
  • Vegetation index
  • Winter wheat


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