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Monitoring of vegetation dynamics in extensive irrigated croplands is essential for improving land and water
management, especially to understand the reaction of the system to water scarcity and degradation processes. This study focuses on the assessment of irrigated cropland dynamics in the western part of the Aral Sea Basin in Central Asia during the past decade. Extend of cropland and spatio-temporal cropping patters are analyzed based on phenological profiles extracted from 16day MODIS vegetation index time series at a spatial resolution of 250m. Knowledge-based classifications which needed to be adjusted for every single year were applied to distinguish between cropland and other major land cover types, the desert or sparsely vegetated steppes, settled areas, and water bodies. Interannual variability of the time series in the maximum cropland extend recorded between 2001 and 2010 was assessed by using Pearson’s cross correlation (PCC) coefficient. Shifts of maximum one month (+/-) were tested and the highest PCC coefficient was selected. Accuracy assessment using a multi-annual MODIS classification conducted for a representative irrigation system between 2004 and 2007 returned acceptable results for the cropland mask (<90%). Comparing the inter-annual cropland dynamics revealed using PCC with both, the MODIS classifications 2004-2007 and pure pixels of aggregated ASTER based maps showed that the PCC only permits differentiation between different modalities in the time series, i.e. years of a varying number of intra-annual crop cycles. However, simply overlaying the cropland extends 2001-2010 already exhibits areas of unreliable water supply. In this light, integration of both, PCC analysis of MODIS time series and annual maps of the cropland extent can be concluded as valuable next steps for better understanding the dynamics of the irrigated cropland at regional scale not only in the Aral Sea Basin of Central Asia, but also in other arid environments, where irrigation agriculture is essential for rural income generation and food security.
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