@article{WamburaDietrichLischeid2017,
  author    = {Wambura, Frank Joseph and Dietrich, Ottfried and Lischeid, Gunnar},
  title     = {Evaluation of Spatio-Temporal Patterns of Remotely Sensed Evapotranspiration to Infer Information about Hydrological Behaviour in a Data-Scarce Region},
  series = {Water},
  volume    = {9},
  journal   = {Water},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2073-4441},
  doi       = {10.3390/w9050333},
  pages     = {297 -- 315},
  year      = {2017},
  abstract  = {Information about the hydrological behaviour of a river basin prior to setting up, calibrating and validating a distributed hydrological model requires extensive datasets that are hardly available for many parts of the world due to insufficient monitoring networks. In this study, the focus was on prevailing spatio-temporal patterns of remotely sensed evapotranspiration (ET) that enabled conclusions to be drawn about the hydrological behaviour and spatial peculiarities of a river basin at rather high spatial resolution. The prevailing spatio-temporal patterns of ET were identified using a principal component analysis of a time series of 644 images of MODIS ET covering the Wami River basin (Tanzania) between the years 2000 and 2013. The time series of the loadings on the principal components were analysed for seasonality and significant long-term trends. The spatial patterns of principal component scores were tested for significant correlation with elevations and slopes, and for differences between different soil texture and land use classes. The results inferred that the temporal and spatial patterns of ET were related to those of preceding rainfalls. At the end of the dry season, high ET was maintained only in areas of shallow groundwater and in cloud forest nature reserves. A region of clear reduction of ET in the long-term was related to massive land use change. The results also confirmed that most soil texture and land use classes differed significantly. Moreover, ET was exceptionally high in natural forests and loam soil, and very low in bushland and sandy-loam soil. Clearly, this approach has shown great potential of publicly available remote sensing data in providing a sound basis for water resources management as well as for distributed hydrological models in data-scarce river basins at lower latitudes.},
  language  = {en}
}
@article{RungeGrosse2019,
  author    = {Runge, Alexandra and Grosse, Guido},
  title     = {Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions},
  series = {Remote Sensing},
  volume    = {11},
  journal   = {Remote Sensing},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs11141730},
  pages     = {29},
  year      = {2019},
  abstract  = {The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.},
  language  = {en}
}