@phdthesis{Kirschke2008,
  author    = {Kirschke, Stefanie},
  title     = {Bilanzierung des Methanaustauschs zwischen Biosph{\"a}re und Atmosph{\"a}re in Periglazialr{\"a}umen mit Hilfe von Fernerkundung und Modellen am Beispiel des Lena Deltas},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-29024},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2008},
  abstract  = {Verbleibende Unsicherheiten im Kohlenstoffhaushalt in {\"O}kosystemen der hohen n{\"o}rdlichen Breiten k{\"o}nnen teilweise auf die Schwierigkeiten bei der Erfassung der r{\"a}umlich und zeitlich hoch variablen Methanemissionsraten von Permafrostb{\"o}den zur{\"u}ckgef{\"u}hrt werden. Methan ist ein global abundantes atmosph{\"a}risches Spurengas, welches signifikant zur Erw{\"a}rmung der Atmosph{\"a}re beitr{\"a}gt. Aufgrund der hohen Sensibilit{\"a}t des arktischen Bodenkohlenstoffreservoirs sowie der großen von Permafrost unterlagerten Landfl{\"a}chen sind arktische Gebiete am kritischsten von einem globalen Klimawandel betroffen. Diese Dissertation adressiert den Bedarf an Modellierungsans{\"a}tzen f{\"u}r die Bestimmung der Quellst{\"a}rke nordsibirischer permafrostbeeinflusster {\"O}kosysteme der nassen polygonalen Tundra mit Hinblick auf die Methanemissionen auf regionalem Maßstab. Die Arbeit pr{\"a}sentiert eine methodische Struktur in welcher zwei prozessbasierte Modelle herangezogen werden, um die komplexen Wechselwirkungen zwischen den Kompartimenten Pedosph{\"a}re, Biosph{\"a}re und Atmosph{\"a}re, welche zu Methanemissionen aus Permafrostb{\"o}den f{\"u}hren, zu erfassen. Es wird ein Upscaling der Gesamtmethanfl{\"u}sse auf ein gr{\"o}ßeres, von Permafrost unterlagertes Untersuchungsgebiet auf Basis eines prozessbasierten Modells durchgef{\"u}hrt. Das prozessbasierte Vegetationsmodell Biosphere Energy Hydrology Transfer Model (BETHY/DLR) wird f{\"u}r die Berechnung der Nettoprim{\"a}rproduktion (NPP) arktischer Tundravegetation herangezogen. Die NPP ist ein Maß f{\"u}r die Substratverf{\"u}gbarkeit der Methanproduktion und daher ein wichtiger Eingangsparameter f{\"u}r das zweite Modell: Das prozessbasierte Methanemissionsmodell wird anschließend verwendet, um die Methanfl{\"u}sse einer gegebenen Bodens{\"a}ule explizit zu berechnen. Dabei werden die Prozesse der Methanogenese, Methanotrophie sowie drei verschiedene Transportmechanismen - molekulare Diffusion, Gasblasenbildung und pflanzengebundener Transport durch vaskul{\"a}re Pflanzen - ber{\"u}cksichtigt. Das Methanemissionsmodell ist f{\"u}r Permafrostbedingungen modifiziert, indem das t{\"a}gliche Auftauen des Permafrostbodens in der kurzen arktischen Vegetationsperiode ber{\"u}cksichtigt wird. Der Modellantrieb besteht aus meteorologischen Datens{\"a}tzen des European Center for Medium-Range Weather Forecasts (ECMWF). Die Eingangsdatens{\"a}tze werden mit Hilfe von in situ Messdaten validiert. Zus{\"a}tzliche Eingangsdaten f{\"u}r beide Modelle werden aus Fernerkundungsdaten abgeleitet, welche mit Feldspektralmessungen validiert werden. Eine modifizierte Landklassifikation auf der Basis von Landsat-7 Enhanced Thematic Mapper Plus (ETM+) Daten wird f{\"u}r die Ableitung von Informationen zu Feuchtgebietsverteilung und Vegetationsbedeckung herangezogen. Zeitserien der Auftautiefe werden zur Beschreibung des Auftauens bzw. R{\"u}ckfrierens des Bodens verwendet. Diese Faktoren sind die Haupteinflussgr{\"o}ßen f{\"u}r die Modellierung von Methanemissionen aus permafrostbeeinflussten Tundra{\"o}kosystemen. Die vorgestellten Modellergebnisse werden mittels Eddy-Kovarianz-Messungen der Methanfl{\"u}sse validiert, welche w{\"a}hrend der Vegetationsperioden der Jahre 2003-2006 im s{\"u}dlichen Teil des Lena Deltas (72°N, 126°E) vom Alfred Wegener Institut f{\"u}r Polar- und Meeresforschung (AWI) durchgef{\"u}hrt wurden. Das Untersuchungsgebiet Lena Delta liegt an der Laptewsee in Nordostsibirien und ist durch {\"O}kosysteme der arktischen nassen polygonalen Tundra sowie kalten kontinuierlichen Permafrost charakterisiert. Zeitlich integrierte Werte der modellierten Methanfl{\"u}sse sowie der in situ Messungen zeigen gute {\"U}bereinstimmungen und weisen auf eine leichte Modelluntersch{\"a}tzung von etwa 10\%.},
  subject      = {Methanemission},
  language  = {de}
}
@article{KoehlerKuenzer2020,
  author    = {Koehler, Jonas and Kuenzer, Claudia},
  title     = {Forecasting spatio-temporal dynamics on the land surface using Earth Observation data — a review},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {21},
  issn      = {2072-4292},
  doi       = {10.3390/rs12213513},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-216285},
  year      = {2020},
  abstract  = {Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.},
  language  = {en}
}
@phdthesis{Fritsch2013,
  author    = {Fritsch, Sebastian},
  title     = {Spatial and temporal patterns of crop yield and marginal land in the Aral Sea Basin: derivation by combining multi-scale and multi-temporal remote sensing data with alight use efficiency model},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-87939},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {Irrigated agriculture in the Khorezm region in the arid inner Aral Sea Basin faces enormous challenges due to a legacy of cotton monoculture and non-sustainable water use. Regional crop growth monitoring and yield estimation continuously gain in importance, especially with regard to climate change and food security issues. Remote sensing is the ideal tool for regional-scale analysis, especially in regions where ground-truth data collection is difficult and data availability is scarce. New satellite systems promise higher spatial and temporal resolutions. So-called light use efficiency (LUE) models are based on the fraction of photosynthetic active radiation absorbed by vegetation (FPAR), a biophysical parameter that can be derived from satellite measurements. The general objective of this thesis was to use satellite data, in conjunction with an adapted LUE model, for inferring crop yield of cotton and rice at field (6.5 m) and regional (250 m) scale for multiple years (2003-2009), in order to assess crop yield variations in the study area. Intensive field measurements of FPAR were conducted in the Khorezm region during the growing season 2009. RapidEye imagery was acquired approximately bi-weekly during this time. The normalized difference vegetation index (NDVI) was calculated for all images. Linear regression between image-based NDVI and field-based FPAR was conducted. The analyses resulted in high correlations, and the resulting regression equations were used to generate time series of FPAR at the RapidEye level. RapidEye-based FPAR was subsequently aggregated to the MODIS scale and used to validate the existing MODIS FPAR product. This step was carried out to evaluate the applicability of MODIS FPAR for regional vegetation monitoring. The validation revealed that the MODIS product generally overestimates RapidEye FPAR by about 6 to 15 \%. Mixture of crop types was found to be a problem at the 1 km scale, but less severe at the 250 m scale. Consequently, high resolution FPAR was used to calibrate 8-day, 250 m MODIS NDVI data, this time by linear regression of RapidEye-based FPAR against MODIS-based NDVI. The established FPAR datasets, for both RapidEye and MODIS, were subsequently assimilated into a LUE model as the driving variable. This model operated at both satellite scales, and both required an estimation of further parameters like the photosynthetic active radiation (PAR) or the actual light use efficiency (LUEact). The latter is influenced by crop stress factors like temperature or water stress, which were taken account of in the model. Water stress was especially important, and calculated via the ratio of the actual (ETact) to the potential, crop-specific evapotranspiration (ETc). Results showed that water stress typically occurred between the beginning of May and mid-September and beginning of May and end of July for cotton and rice crops, respectively. The mean water stress showed only minor differences between years. Exceptions occurred in 2008 and 2009, where the mean water stress was higher and lower, respectively. In 2008, this was likely caused by generally reduced water availability in the whole region. Model estimations were evaluated using field-based harvest information (RapidEye) and statistical information at district level (MODIS). The results showed that the model at both the RapidEye and the MODIS scale can estimate regional crop yield with acceptable accuracy. The RMSE for the RapidEye scale amounted to 29.1 \% for cotton and 30.4 \% for rice, respectively. At the MODIS scale, depending on the year and evaluated at Oblast level, the RMSE ranged from 10.5 \% to 23.8 \% for cotton and from -0.4 \% to -19.4 \% for rice. Altogether, the RapidEye scale model slightly underestimated cotton (bias = 0.22) and rice yield (bias = 0.11). The MODIS-scale model, on the other hand, also underestimated official rice yield (bias from 0.01 to 0.87), but overestimated official cotton yield (bias from -0.28 to -0.6). Evaluation of the MODIS scale revealed that predictions were very accurate for some districts, but less for others. The produced crop yield maps indicated that crop yield generally decreases with distance to the river. The lowest yields can be found in the southern districts, close to the desert. From a temporal point of view, there were areas characterized by low crop yields over the span of the seven years investigated. The study at hand showed that light use efficiency-based modeling, based on remote sensing data, is a viable way for regional crop yield prediction. The found accuracies were good within the boundaries of related research. From a methodological viewpoint, the work carried out made several improvements to the existing LUE models reported in the literature, e.g. the calibration of FPAR for the study region using in situ and high resolution RapidEye imagery and the incorporation of crop-specific water stress in the calculation.},
  subject      = {Fernerkundung},
  language  = {en}
}