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Climate change is likely to decrease surface water availability in Central Asia, thereby necessitating land use adaptations in irrigated regions. The introduction of trees to marginally productive croplands with shallow groundwater was suggested for irrigation water-saving and improving the land’s productivity. Considering the possible trade-offs with water availability in large-scale afforestation, our study predicted the impacts on water balance components in the lower reaches of the Amudarya River to facilitate afforestation planning using the Soil and Water Assessment Tool (SWAT). The land-use scenarios used for modeling analysis considered the afforestation of 62% and 100% of marginally productive croplands under average and low irrigation water supply identified from historical land-use maps. The results indicate a dramatic decrease in the examined water balance components in all afforestation scenarios based largely on the reduced irrigation demand of trees compared to the main crops. Specifically, replacing current crops (mostly cotton) with trees on all marginal land (approximately 663 km\(^2\)) in the study region with an average water availability would save 1037 mln m\(^3\) of gross irrigation input within the study region and lower the annual drainage discharge by 504 mln m\(^3\). These effects have a considerable potential to support irrigation water management and enhance drainage functions in adapting to future water supply limitations.
Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 %. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 % for cotton and 30.4 % for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 % to 23.8 % for cotton and from -0.4 % to -19.4 % for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.