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Modeling of Cotton Yields in AmuDarya River Floodplains of Uzbekistan Integrating Multitemporal RS.pdf

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Modeling of Cotton Yields in AmuDarya River Floodplains of Uzbekistan Integrating Multitemporal RS

Modeling of Cotton Yields in the Amu Darya River Floodplains of Uzbekistan Integrating Multitemporal Remote Sensing and Minimum Field Data Zhou Shi, Gerd R. Ruecker,* Marc Mueller, Christopher Conrad, Nazar Ibragimov, John P. A. Lamers, Christopher Martius, Guenter Strunz, Stefan Dech, and Paul L. G. Vlek 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 Spectroradiom- eter)-derived parameters from 2002 as primary inputs. The photosyn- thetically active radiation (PAR) and environmental stress scalars on crop development were estimated with meteorological information. Using high-spatial-resolution Landsat 7 ETM1 images, the cotton area was extracted and the cotton fraction determined within the coarse spatial resolution MODIS pixels. The spatial resolution of the MODISFPARdatawas improved by using an empirical relationship to the higher-resolution MODIS NDVI (Normalized Difference Vegeta- tion Index) data. The estimated raw cotton yield ranged from 1.09 to 3.76 Mg ha21. 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 late- season estimation of spatially distributed cotton yield over large regions on which management adjustments could be made. ACHIEVING ECOLOGIC