Universität zu Köln

Gnyp, Martin L. ORCID: 0000-0002-5702-4914, Bareth, Georg, Li, Fei, Lenz-Wiedemann, Victoria I. S., Koppe, Wolfgang, Miao, Yuxin ORCID: 0000-0001-8419-6511, Hennig, Simon D., Jia, Liangliang, Laudien, Rainer, Chen, Xinping and Zhang, Fusuo (2014). Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Int. J. Appl. Earth Obs. Geoinf., 33. S. 232 - 243. AMSTERDAM: ELSEVIER. ISSN 1872-826X

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Abstract

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006-2007 growing season of winter wheat (Triticum aestivum L). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, 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 Li through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. Li field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R-2 > 0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81-89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions. (C) 2014 Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Gnyp, Martin L. UNSPECIFIED orcid.org/0000-0002-5702-4914 UNSPECIFIED Bareth, Georg UNSPECIFIED UNSPECIFIED UNSPECIFIED Li, Fei UNSPECIFIED UNSPECIFIED UNSPECIFIED Lenz-Wiedemann, Victoria I. S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Koppe, Wolfgang UNSPECIFIED UNSPECIFIED UNSPECIFIED Miao, Yuxin UNSPECIFIED orcid.org/0000-0001-8419-6511 UNSPECIFIED Hennig, Simon D. UNSPECIFIED UNSPECIFIED UNSPECIFIED Jia, Liangliang UNSPECIFIED UNSPECIFIED UNSPECIFIED Laudien, Rainer UNSPECIFIED UNSPECIFIED UNSPECIFIED Chen, Xinping UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Fusuo UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-422959
DOI:	10.1016/j.jag.2014.05.006
Journal or Publication Title:	Int. J. Appl. Earth Obs. Geoinf.
Volume:	33
Page Range:	S. 232 - 243
Date:	2014
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	1872-826X
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Geosciences > Geographisches Institut
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ESTIMATING ABOVEGROUND BIOMASS; REFLECTANCE MEASUREMENTS; ABSORPTION FEATURES; YIELD; CANOPY; CHLOROPHYLL; GROWTH; RICE; LEAF; INFORMATION Multiple languages Remote Sensing Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/42295