@techreport{ConradMorperBuschNetzbandetal.2019,
  type      = {Working Paper},
  author    = {Conrad, Christopher and Morper-Busch, Lucia and Netzband, Maik and Teucher, Mike and Sch{\"o}nbrodt-Stitt, Sarah and Schorcht, Gunther and Dukhovny, Viktor},
  title     = {Инструмент для выработки обоснованных решений в вопросах земле- и водопользования},
  doi       = {10.25972/OPUS-19200},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-192006},
  pages     = {12},
  year      = {2019},
  abstract  = {WUEMoCA — научный инструмент веб-кар¬тографирования для мониторинга эф¬фек¬тивности земле- и водопользования на территориях орошаемого земледелия стран трансграничного бассейна Араль¬ского моря (Казахстана, Кыргызстана, Таджикистана, Туркменистана, Узбеки¬стана и Афганистана). Путём интеграции спутниковых данных по землепользованию, растениеводству и потреблению воды с гидрологическими и экономическими данными создаётся целый набор показателей. Инструмент полезен для выработки масштабных решений в вопросах распределения воды и землепользования, а также может применяться во многих практических сферах, в которых требуются независимые данные о конкретных обширных территориях.},
  language  = {ru}
}
@article{ConradSchoenbrodtStittLoewetal.2016,
  author    = {Conrad, Christopher and Sch{\"o}nbrodt-Stitt, Sarah and L{\"o}w, Fabian and Sorokin, Denis and Paeth, Heiko},
  title     = {Cropping Intensity in the Aral Sea Basin and Its Dependency from the Runoff Formation 2000-2012},
  series = {Remote Sensing},
  volume    = {8},
  journal   = {Remote Sensing},
  number    = {630},
  doi       = {10.3390/rs8080630},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147701},
  year      = {2016},
  abstract  = {This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number of harvests) in the Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series and knowledge-based pixel masking that included settlement layers and topography features enabled to map the irrigated cropland extent (iCE). Random forest models supported the classification of cropland vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI and the percentage of fallow cropland (PF) were derived from CVP. Spearman's rho was selected for assessing the statistical relation of CI and PF to runoff formation in the Amu Darya and Syr Darya catchments per hydrological year. Validation in 12 reference sites using multi-annual Landsat-7 ETM+ images revealed an average overall accuracy of 0.85 for the iCE maps. MODIS maps overestimated that based on Landsat by an average factor of ~1.15 (MODIS iCE/Landsat iCE). Exceptional overestimations occurred in case of inaccurate settlement layers. The CVP and CI maps achieved overall accuracies of 0.91 and 0.96, respectively. The Amu Darya catchment disclosed significant positive (negative) relations between upstream runoff with CI (PF) and a high pressure on the river water resources in 2000-2012. Along the Syr Darya, reduced dependencies could be observed, which is potentially linked to the high number of water constructions in that catchment. Intensified double cropping after drought years occurred in Uzbekistan. However, a 10 km × 10 km grid of Spearman's rho (CI and PF vs. upstream runoff) emphasized locations at different CI levels that are directly affected by runoff fluctuations in both river systems. The resulting maps may thus be supportive on the way to achieve long-term sustainability of crop production and to simultaneously protect the severely threatened environment in the ASB. The gained knowledge can be further used for investigating climatic impacts of irrigation in the region.},
  language  = {en}
}
@article{SchoenbrodtStittAhmadianKurtenbachetal.2021,
  author    = {Sch{\"o}nbrodt-Stitt, Sarah and Ahmadian, Nima and Kurtenbach, Markus and Conrad, Christopher and Romano, Nunzio and Bogena, Heye R. and Vereecken, Harry and Nasta, Paolo},
  title     = {Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem},
  series = {Frontiers in Water},
  volume    = {3},
  journal   = {Frontiers in Water},
  doi       = {10.3389/frwa.2021.655837},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259062},
  pages     = {655837},
  year      = {2021},
  abstract  = {Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the "Alento" hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{-3}\), and 89\% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{-3}\, and 86\% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.},
  language  = {en}
}
@techreport{ConradMorperBuschNetzbandetal.2019,
  type      = {Working Paper},
  author    = {Conrad, Christopher and Morper-Busch, Lucia and Netzband, Maik and Teucher, Mike and Sch{\"o}nbrodt-Stitt, Sarah and Schorcht, Gunther and Dukhovny, Viktor},
  title     = {WUEMoCA Water Use Efficiency Monitor in Central Asia Informed Decision-Making in Land and Water Resources Management},
  doi       = {10.25972/OPUS-19193},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-191934},
  pages     = {1-12},
  year      = {2019},
  abstract  = {WUEMoCA is an operational scientific webmapping tool for the regional monitoring of land and water use efficiency in the irrigated croplands of the transboundary Aral Sea Basin that is shared by Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and Afghanistan. Satellite data on land use, crop pro-duction and water consumption is integrated with hydrological and economic information to provide of a set indicators. The tool is useful for large-scale decisions on water distribution or land use, and may be seen as demonstrator for numerous applications in practice, that require independent area-wide spatial information.},
  subject      = {Zentralasien},
  language  = {en}
}