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Modeling of Cotton Yields in the Amu Darya River Floodplains of Uzbekistan Integrating Multitemporal Remote Sensing and Minimum Field Data

Shi, Zhou and Rücker, Gerd and Müller, Marc and Conrad, Christopher and Ibragimov, Nazar and Lamers, John P.A. and Martius, Christopher and Strunz, Günter and Dech, Stefan and Vlek, Paul L.G. (2007) Modeling of Cotton Yields in the Amu Darya River Floodplains of Uzbekistan Integrating Multitemporal Remote Sensing and Minimum Field Data. Agronomy Journal, 99, pp. 1317-1326. The American Society of Agronomy. ISSN 0002-1962.

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Official URL: http://agron.scijournals.org/cgi/content/abstract/99/5/1317

Abstract

Increased knowledge about the spatial distribution of cotton (Gossypium hirsutum L.) yield in the Khorezm region in Uzbekistan supports the optimal allocation of resources. This research estimated the spatial distribution of cotton yields in Khorezm by integrating remote sensing, field data, and modeling. The agro-meteorological model used was based on Monteith’s biomass production model with multitemporal MODIS (Moderate Resolution Imaging Spectroradiometer)- derived parameters from 2002 as primary inputs. The photosynthetically active radiation (PAR) and environmental stress scalars on crop development were estimated with meteorological information. Using high-spatial-resolution Landsat 7 ETM+ images, the cotton area was extracted and the cotton fraction determined within the coarse spatial resolution MODIS pixels. The spatial resolution of the MODISFPARdata was improved by using an empirical relationship to the higher-resolution MODIS NDVI (Normalized Difference Vegetation Index) data. The estimated raw cotton yield ranged from 1.09 to 3.76 Mg ha<sup>-1</sup>. The modeling revealed a spatial trend of higher yields in upstream areas and in locations closer to the irrigation channels and lower yields in downstream areas and at sites more distant to the channels. The validated yield estimations showed a 10% deviation from official governmental statistics. The established agro-meteorological model based on freely available MODIS data and a minimum of field data input is a promising technique for economic and operational lateseason estimation of spatially distributed cotton yield over large regions on which management adjustments could be made.

Document Type:Article
Title:Modeling of Cotton Yields in the Amu Darya River Floodplains of Uzbekistan Integrating Multitemporal Remote Sensing and Minimum Field Data
Authors:
AuthorsInstitution or Email of Authors
Shi, ZhouInstitute of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou 310029, China
Rücker, Gerdgerd.ruecker@dlr.de
Müller, MarcInstitute for Prospective and Technological Studies, European Commission–Joint Research Center Edificio Expo C/Inca Garcilaso, 41092 Sevilla, Spain
Conrad, ChristopherChristopher.Conrad@uni-wuerzburg.de
Ibragimov, NazarUzbekistan National Cotton Growing Research Institute, P.O. Box Akkavak, 702133 Tashkent, Uzbekistan
Lamers, John P.A.Center for Development Research, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
Martius, ChristopherCenter for Development Research, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
Strunz, GünterGuenter.Strunz@dlr.de
Dech, Stefanstefan.dech@dlr.de
Vlek, Paul L.G.Center for Development Research, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
Date:11 September 2007
Journal or Publication Title:Agronomy Journal
Refereed publication:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:99
Page Range:pp. 1317-1326
Publisher:The American Society of Agronomy
ISSN:0002-1962
Status:Published
Keywords:MODIS, Cotton yield, Remote sensing, Light use efficiency, Photosynthetic active radiation, Crop model, Spatially distributed modeling, Aral Sea
HGF - Research field:Aeronautics, Space and Transport (old)
HGF - Program:Space (old)
HGF - Program Themes:W EO - Erdbeobachtung
DLR - Research area:Space
DLR - Program:W EO - Erdbeobachtung
DLR - Research theme (Project):W - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Dr.rer.nat. Gerd Rücker
Deposited On:27 Sep 2007
Last Modified:22 Apr 2013 14:54

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