@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}
}
@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}
}
@article{NyamekyeThielSchoenbrodtStittetal.2018,
  author    = {Nyamekye, Clement and Thiel, Michael and Sch{\"o}nbrodt-Stitt, Sarah and Zoungrana, Benewinde J.-B. and Amekudzi, Leonard K.},
  title     = {Soil and water conservation in Burkina Faso, West Africa},
  series = {Sustainability},
  volume    = {10},
  journal   = {Sustainability},
  number    = {9},
  issn      = {2071-1050},
  doi       = {10.3390/su10093182},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197653},
  pages     = {3182},
  year      = {2018},
  abstract  = {Inadequate land management and agricultural activities have largely resulted in land degradation in Burkina Faso. The nationwide governmental and institutional driven implementation and adoption of soil and water conservation measures (SWCM) since the early 1960s, however, is expected to successively slow down the degradation process and to increase the agricultural output. Even though relevant measures have been taken, only a few studies have been conducted to quantify their effect, for instance, on soil erosion and environmental restoration. In addition, a comprehensive summary of initiatives, implementation strategies, and eventually region-specific requirements for adopting different SWCM is missing. The present study therefore aims to review the different SWCM in Burkina Faso and implementation programs, as well as to provide information on their effects on environmental restoration and agricultural productivity. This was achieved by considering over 143 studies focusing on Burkina Faso's experience and research progress in areas of SWCM and soil erosion. SWCM in Burkina Faso have largely resulted in an increase in agricultural productivity and improvement in food security. Finally, this study aims at supporting the country's informed decision-making for extending already existing SWCM and for deriving further implementation strategies.},
  language  = {en}
}
@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{RieserVesteThieletal.2021,
  author    = {Rieser, Jakob and Veste, Maik and Thiel, Michael and Sch{\"o}nbrodt-Stitt, Sarah},
  title     = {Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland},
  series = {Remote Sensing},
  volume    = {13},
  journal   = {Remote Sensing},
  number    = {16},
  issn      = {2072-4292},
  doi       = {10.3390/rs13163093},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245006},
  year      = {2021},
  abstract  = {Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs' activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the 'Lieberoser Heide' in northeastern Germany, is home to the country's largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9\%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands.},
  language  = {en}
}