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

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 MODIS FPAR data 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−1. 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.

Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling

“…In Uzbekistan, the land-use decisions largely remained unchanged during the study period given the area-based, state production targets for cotton and the prevalence of cotton and winter wheat in the cropland area (Djanibekov et al 2010; Shi et al 2007). Furthermore, summing the NDVI over the whole growing season, thus integrating vegetation peaks in the fields with different land uses, reduced the possibility of misinterpretations, particularly for the remaining land fraction with a variable cropping pattern.…”

“…The same crop often grows on the adjacent fields, exceeding the area of 250 ha (Conrad et al 2011). Most of the arable land is occupied by cotton (60–70 %) and winter wheat (20–30 %) (Shi et al 2007), cultivated under the state procurement system, where production goals are set for these strategic crops. Cotton has always been produced in Uzbekistan as a means of gaining export earnings, whereas wheat was introduced in the 1990s for national wheat self-sufficiency.…”

Relationships between high resolution RapidEye based fPAR and MODIS vegetation indices in a heterogeneous agricultural region

“…Another approach at the regional scale was investigated by Shi et al 8 , who applied a light-use efficiency approach for regional prediction of cotton yields. The method was based on MODIS fPAR data, downscaled to 250 m using MODIS NDVI, as the main input, and the results showed that yield could be accurately estimated.…”

Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield

“…Traditional satellite imagery, such as 30-m Landsat and 20-m SPOT, has been evaluated for some time for estimating crop yield (Barnett and Thompson 1982;Wiegand et al 1991;Precision Agric (2009) 10:292-303 293 Thenkabail et al 1995Lobell et al 2005;Shi et al 2007). Because of its coarse spatial resolution and large areal coverage, this type of imagery has been used more commonly for estimating yields over large geographic areas than for assessing within-field yield variation at fine spatial resolutions.…”