Sarah Schönbrodt‐Stitt

ORCID: 0000-0002-7279-6242
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About
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Research Areas
  • Soil erosion and sediment transport
  • Hydrology and Watershed Management Studies
  • Transboundary Water Resource Management
  • Soil Moisture and Remote Sensing
  • Hydrology and Sediment Transport Processes
  • Environmental and Agricultural Sciences
  • Remote Sensing in Agriculture
  • Agriculture and Rural Development Research
  • Precipitation Measurement and Analysis
  • Rangeland Management and Livestock Ecology
  • Water resources management and optimization
  • Landslides and related hazards
  • Hydropower, Displacement, Environmental Impact
  • Groundwater and Watershed Analysis
  • African Botany and Ecology Studies
  • Ecology, Conservation, and Geographical Studies
  • Aeolian processes and effects
  • Synthetic Aperture Radar (SAR) Applications and Techniques
  • Soil Geostatistics and Mapping
  • Computational Physics and Python Applications
  • Geological Modeling and Analysis
  • Fire effects on ecosystems
  • Climate change impacts on agriculture
  • Soil and Land Suitability Analysis
  • Land Rights and Reforms

University of Würzburg
2016-2025

University of Tübingen
2010-2017

Inadequate land management and agricultural activities have largely resulted in degradation Burkina Faso. The nationwide governmental institutional driven implementation adoption of soil water conservation measures (SWCM) since the early 1960s, however, is expected to successively slow down process increase output. Even though relevant been taken, only a few studies conducted quantify their effect, for instance, on erosion environmental restoration. In addition, comprehensive summary...

10.3390/su10093182 article EN Sustainability 2018-09-06

Abstract Most calibration sampling designs for Digital Soil Mapping (DSM) demarcate spatially distinct sample sites. In practical applications major challenges are often limited field accessibility and the question on how to integrate legacy soil samples cope with usually scarce resources laboratory analysis. The study focuses development application of an efficiency improved DSM design that (1) applies optimized set size, (2) compensates accessibility, (3) enables integration samples....

10.1002/jpln.201500313 article EN Journal of Plant Nutrition and Soil Science 2016-06-15

This study is aimed at a better understanding of how upstream runoff formation affected the cropping intensity (CI: number harvests) in Aral Sea Basin (ASB) between 2000 and 2012. MODIS 250 m NDVI time series knowledge-based pixel masking that included settlement layers topography features enabled to map irrigated cropland extent (iCE). Random forest models supported classification vegetation phenology (CVP: winter/summer crops, double cropping, etc.). CI percentage fallow (PF) were derived...

10.3390/rs8080630 article EN cc-by Remote Sensing 2016-07-29

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability agroforestry systems 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 together with in-situ measurements θ into random forest (RF) regression approach (10-fold...

10.3389/frwa.2021.655837 article EN cc-by Frontiers in Water 2021-07-14

Abstract Soil erosion by water outlines a major threat to the Three Gorges Reservoir Area in China. A detailed assessment of soil conservation measures requires tool that spatially identifies sediment reallocations due rainfall–runoff events catchments. We applied EROSION 3D as physically based and deposition model small mountainous catchment. Generally, we aim provide methodological frame facilitates parametrization data scarce environment identify sources deposits. used digital mapping...

10.1002/ldr.2503 article EN Land Degradation and Development 2016-01-20

Abstract The dramatic droughts in West Africa from the 1960s to 1990s compelled Burkina Faso implement soil and water conservation (SWC) measures late 1970s. purpose was combat land degradation (e.g., desertification) areas that experienced a decrease vegetation productivity. In this study, normalized difference index (NDVI) trends 2002 2016 were analyzed using Mann‐Kendall test explore spatio‐temporal variations of at SWC non‐SWC sites selected regions Faso. On average, NDVI increased by...

10.1002/ldr.3654 article EN Land Degradation and Development 2020-05-12

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) global drylands. They consist of poikilohydric organisms capable adjusting their metabolic activities depending on the water availability. However, they, with them, ecosystem functions, endangered by climate change land-use intensification. Remote sensing (RS)-based studies estimated BSC cover drylands...

10.3390/rs13163093 article EN cc-by Remote Sensing 2021-08-05

Spatially explicit near-surface soil moisture ($\theta$) patterns at high temporal resolution play a very important role in environmental modelling for improving risk assessment and quantifying the effects of climatic seasonality land use/land cover change on ecosystem services functions Mediterranean catchments. Remote sensing data from European Copernicus mission are highly acknowledged to serve as fast, reliable, available suppliers derivation area wide, grid-resolution information (20 m...

10.1109/metroagrifor.2019.8909226 article EN 2019-10-01

Accurate near-surface soil moisture (θ; ~ 5 cm) estimation is one of the most crucial challenges in agricultural management and hydrological studies. This study aims to map θ at high spatiotemporal resolution (17 m grid size, satellite overpass 6 days) a small-scale agroforestry experimental site (~ 30 ha) southern Italy. The observation period from November 2018 until March 2019. We employed an ensemble machine-learning method based on Random Forest (RF) θ. RF three input data types: i)...

10.1109/metroagrifor50201.2020.9277557 article EN 2020-11-04
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