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The Moderate Imaging Spectroradiometer (MODIS) provides operational products of the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the fraction of photosynthetic active radiation (fPAR). FPAR can be used in productivity models, but agricultural applications depend on sub-pixel heterogeneity. Examples for heterogeneous areas are the irrigation systems of the inner Aral Sea Basin, where the 1 km fPAR product proved less suited. An alternative can be to upscale fPAR to the 250 m scale, but there are few studies evaluating this approach. In this study, the use of MODIS 250 m NDVI and EVI for this approach was investigated in an irrigation system in western Uzbekistan. The analysis was based on high resolution fPAR maps and a crop map for the growing season 2009, derived from ground measurements and multitemporal RapidEye data. The data was used to explore statistical relationships between RapidEye fPAR and MODIS NDVI/EVI with respect to spatial heterogeneity. The correlations varied between products (daily NDVI, 8-day NDVI, 16-day NDVI/EVI), with results suggesting that 8-day NDVI performed best. The analyses and the compiled fPAR maps show that, compared to 1 km MODIS fPAR, the 250 m scale is more homogeneous, allows for crop-specific analyses, and better captures the spatial patterns in the study region.

In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

Remote sensing offers the opportunity to produce land cover classifications for large and remote areas on a yearly basis and is an important tool in regions that lack these information. However often training and validation data to generate annual land cover maps are not available in necessary quantity - being from one year only or covering only a small extent of the region of interest. This study was focused on land use classifications at regional scale with a special emphasize on annual updates under the constraint of limited sampling data. Often, sampling is reduced to one year or to an unrepresentative area extend within the region of interest. The investigations for the period between 2004 and 2009 were conducted in the irrigation systems of the Amu Darya Delta in Central Asia, where reliable information on crop rotations is required for sustainable land and water management. Annual training and validation data were extracted from high resolution land use classifications. For classification, statistical features based on MODIS time series of vegetation indices, reflectance and land surface temperature (LST) were calculated and a random forest algorithm was applied. By a combination of training data from different years, the accuracy could be enhanced from an overall accuracy of 70% to more than 90% for a focused subregion and also good consistency with high resolution images for the other parts of the delta, which has to be confirmed using quantitative validation. A combination of a different number of years was tested. Already two years can be sufficient to generate a robust and transferable random forest to produce yearly land use maps. The study shows the possibility to combine training data from different years for the annual classification of irrigated croplands on a regional scale.