König, M.; Birnbaum, G.; Oppelt, N. (2020):
Mapping the Bathymetry of Melt Ponds on Arctic Sea Ice Using Hyperspectral Imagery. Remote Sens. 2020, 12(16), 2623; https://doi.org/10.3390/rs12162623.

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Wagner, M.; Oppelt, N. (2020):
Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction. Remote Sens. 2020, 12(12), 1990; https://doi.org/10.3390/rs12121990

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Huth, J.; Gessner, U., Klein, I.; Yesou, H.; Lai, X.; Oppelt, N.; Kuenzer, C. (2020):
Analyzing Water Dynamics Based on Sentinel-1 Time Series—a Study at the Dongting Lake Wetlands in China. Remote Sensing 12(11),1761,  https://doi.org/10.3390/rs12111761

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König, M., Oppelt, N. (2020):
A linear model to retrieve melt pond depth from hyperspectral data. The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-2019-261

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Wagner, M.; Oppelt, N. (2020):
Extracting Agricultural Fields from Remote Sensing Imagery Using Graph-Based Growing Contours. Remote Sens, 12,1205, doi:10.3390/rs12071205.

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Pahlevan, N., Smitha, B., Schallesc, J., Binding, C., Cao, Z., Mae, R., Alikas, R., Kangro, K., Gurling, D., Nguyễnh, H., Matsushita, B., Moses, W., Greb, S., Lehmanm, M., Hann, T.-L., Ondrusek, M., Oppelt, N., Stumpf, R. (2020):
Seamless retrievals of chlorophyll-a from Sentinel-2/3 in coastal and inland waters: A machine learning approach. Remote Sens. Environ. 240, 111604, doi.org/10.1016/j.rse.2019.111604.

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Wagner, M.; Slawig, T.; Taravat, A.; Oppelt, N. (2020):
Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. Int. J. Geo-Inf, 9, 105, doi:10.3390/ijgi9020105.

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Tsai, Y.-L., Dietz, A., Oppelt, N., Kuenzer, C. (2019):
Combination of PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas. Remote Sens. 2019, 11(16), 1904; https://doi.org/10.3390/rs11161904

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Tsai, Y.-L.; Dietz, A.; Oppelt, N.; Kuenzer, C. (2019):
Wet and dry snow detection using Sentinel-1 SAR data for mountaneous areas with a machine learning technique. Remote Sens. 11/2019, doi.org/10.3390/rs11080895.

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Taravat, A.; Wagner, M.; Oppelt, N. (2019):
Automatic grassland cutting date detection in the context of spatiotemporal SAR imagery analysis and artificial neural networks. Remote Sens. 26, doi.org/10.3390/rs11060711.

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Addae, B.; Oppelt, N. (2019):
Land-use/Land-cover Change Analysis and Urban Growth Modelling in the Greater Accra Metropolitan Area (GAMA), Ghana. Urban Sci. 3(1),  doi.org/10.3390/urbansci3010026.

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König, M.; Hieronymi, M.; Oppelt, N. (2019):
Application of Sentinel-2 MSI in Arctic research: evaluating the performance of atmospheric correction approaches over Arctic sea ice. Frontiers in Earth Science - Cryosphere;  https://doi.org/10.3389/feart.2019.00022.

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Fritz, C.; Kuhwald, K.; Schneider, T.; Geist, J.; Oppelt, N. (2019):
Sentinel-2 for mapping the spatio-temporal development of submerged aquatic vegetation at Lake Starnberg (Germany). Journal of Limnology, DOI: 10.4081/jlimnol.2019.1824. 

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Baschek, B.; Dörnhöfer, K.; Fricke, K.; Oppelt, N. (2018):
Grundlagen und Möglichkeiten der passiven Fernerkundung von Binnengewässern. In: Handbuch Angewandte Limnologie 34.Erg.Lfg. 1/18  III-1.1.7: 28 Seiten.

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Kuhwald, M.; Dörnhöfer, K.; Oppelt, N.; Duttmann, R. (2018):
Spatially Explicit Soil Compaction Risk Assessment of Arable Soils at Regional Scale: The SaSCiA-Model. Sustainability. 2018. doi.org/10.3390/su10051618

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Dörnhöfer, K.; Scholze, J.;  Stelzer, K.; Oppelt, N. (2018):
Water Colour Analysis of Lake Kummerow Using Time Series of Remote Sensing and In Situ Data. Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2018. doi.org/10.1007/s41064-018-0046-3.

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Da Ponte, E.; Mack, B.; Wohlfahrt, C.; Rodas, O.; Fleckenstein, M.; Oppelt, N.; Dech, S.; Kuenzer, C. (2017):
Assessing forest cover dynamics and forest perception in the Atlantic forest of Paraguay, combining remote sensing and household level data. Forests 8(389), doi:10.3390/f8100389.

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Dörnhöfer, K.; Klinger, P.; Heege, T.; Oppelt, N. (2017):
Multi-sensor astellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake. Science of the Total Environment, 612: 1200-1214. doi.org/10.1016/j.scitotenv.2017.08.219

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Fritz, C.; Doernhoefer, K.; Schneider, T.; Geist, J.; Oppelt, N. (2017):
Mapping submerged aquatic vegetation using RapidEye satellite data: the example Lake Kummerow (Germany). Water 9(510), special issue "Water quality monitoring and modeling in lakes", doi:10.3390/w9070510.

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Da Ponte, E.; Kuenzer, C.; Parker, A.; Rodas, O.; Oppelt, N.; Fleckenstein, M. (2017):
Forest cover loss in Paraguay and perception of ecosystem services: a case study in the Upper Parana Forest. Ecosystem Services 24: 200-2012. 

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Dörnhöfer, K.; Göritz, A.; Gege, P.; Pflug, B.; Oppelt, N. (2016):
Water constituents and water depth retrieval from Setinel-2A – a first evaluation in an oligotrophic lake. Remote Sensing 2016/7/941, doi:10.3390/rs8110941.

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Peronaci, S.; Taravat, A.; del Frate, F.; Oppelt, N. (2016):
Use of NARX neural networks for Meteosat Second Generation SEVIRI very short-term cloud mask forecasting. International Journal of Remote Sensing 37/24: 6205-6215, dx.doi.org/10.1080/2150704X.2016.1249296.

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Uhl, F.; Bartsch, I.; Oppelt, N. (2016):
Submerged kelp detection with hyperspectral data. Remote Sensing, special issue on Coastal Remote Sensing 8/487; doi:10.3390/rs8060487.

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Oppelt, N. (2016):
Fernerkundung in der Hydrologie. In: Hydrologie (Eds. Fohrer, N. et al.), UTB. 

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Dörnhöfer, K.; Oppelt, N. (2016)
Remote sensing for lake research and monitoring - recent advances. Ecological Indicators 64, pp.105-122.

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Oppelt, N.; Scheiber, R.; Wegmann, M.; Taubenboeck, H.; Gege, P.; Berger, M. (2015)
Fundamentals of remote sensing for terrestrial applications: evolution, current state-of-art, and future possibilities. In: Thenkabail, P.S. (Ed.). Remote Sensing Handbook, Vol I/Data Characterisation, Classification, and Accuracies. Chapter 2, pp. 61-83. Taylor and Francis, ISBN 9781482217865.

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Leinenkugel, P.; Wolters, M.L.; Oppelt, N.; Kuenzer, C. (2015)
Tree cover and forest cover dynamics in the Mekong Basin from 2001 to 2011. Remote Sensing of Environment 158(1), pp. 376–392.

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Taravat, A.; Proud, D.; Peronaci, S.; del Frate, F.; Oppelt, N. (2015)
Multilayer percetron neural networks model for Meteosat Second Generation SEVIRI daytime cloud masking. Remote Sensing 7(2), pp. 1529-1539, dx.doi.org/10.3390/rs70201529.

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Rathjens, H.; Oppelt, N.; Bosch, D.D.; Arnold, J.; Volk, M. (2014)
Development of a grid-based version of the SWAT landscape model. Hydrological Processes 29(6), pp. 900-914, dx.doi.org/10.1002/hyp.10197.

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Rathjens, H.; Doernhofer, K.; Oppelt, N. (2014)
An interpolation and improvement approach for remotely sensed land cover data. International Journal of Applied Earth Observation and Geoinformation 31, pp. 1-12.

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Kandziora, M.; Dörnhöfer, K.; Oppelt, N.; Müller, F. (2014)
Detecting land use and land cover changes in northern German agricultural landscapes to assess ecosystem service dynamics. Landscape Online 35, pp. 1-24, dx.doi.org/10.3097/LO.201435.

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Leinenkugel, P.; Oppelt, N.; Kuenzer, C. (2014)
A new land cover map for the Mekong: Southeast Asia´s largest transboundary river basin. Pacific Geographies 41, pp. 10-14.

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Leinenkugel, P.; Wolters, M.; Kuenzer, C.; Oppelt, N.; Dech, S. (2014)
Sensitivity analysis for predicting continuous fields of tree-cover and fractional land-cover distributions in cloud-prone areas. International Journal of Remote Sensing 35(8), pp. 2799-2821.

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Taravat, A.; Oppelt, N. (2014)
Weilbull multiplicative model and multilayer perceptron neural networks for dark-spot detection from SAR imagery. Sensors 14(12), Special issue on Modern Technologies for Sensing Pollution in Air, Water, and Soils, pp. 22798-22810, dx.doi.org/10.3390/s141222798.

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Uhl, F.; Oppelt, N.; Bartsch, I. (2013)
Spectral mixture of intertidal marine macroalgae around the island of Helgoland (Germany, North Sea). Aquatic Botany 111, pp. 112-124, dx.doi.org//10.1016/j.aquabot.2013.06.001.

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Vo, T.Q.; Oppelt, N.; Kuenzer, C.; Leinenkugel, P. (2013)
Remote sensing in mapping ecosystem services - an object-based approach. Remote Sensing 5(1), pp.183-201, dx.doi.org/10.3390/rs5010183.

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Leinenkugel, P.; Kuenzer, C.; Oppelt, N.; Dech, S. (2013)
Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas – a novel product for the complete Mekong Basin. Remote Sensing of Environment 136, pp. 180-198.

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Oppelt, N. (2012)
Remote Sensing of Photosynthetic Parameters. In: Najafpour, M.M. (Ed.). Applied Photosynthesis. InTech Publisher, Rijeka (CRO), pp. 141-164, ISBN: 978-953-51-0061-4.

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Vo, Q.T.; Kuenzer, C.; Vo, Q,M.; Moder, F.; Oppelt, N. (2012)
Review of valuation methods of mangrove ecosystem services. Ecological Indicators 23(1), pp. 431-446, dx.doi.org/10.1016/j.ecolind.2012.04.022.

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Rathjens, H.; Oppelt, N. (2012)
SWAT model calibration of a grid-based setup. Advances in Geosciences 32, pp. 55-61, dx.doi.org/10.5194/adgeo-32-55-2012.

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Oppelt, N.; Schulze, F.; Bartsch, I.; Doernhoefer, K.; Eisenhardt, I. (2012)
Hyperspectral Classification Approaches for Intertidal Macroalgae Habitat Mapping: a Case Study in Heligoland. Optical Engineering 51(11), 111703, dx.doi.org/10.1117/1.OE.51.11.111703.

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Rathjens, H.; Oppelt, N. (2011)
SWATgrid: An interface for setting up SWAT in a grid-based discretization scheme. Computers & Geosciences 45, pp. 161-167, dx.doi.org/10.1016/j.cageo.2011.11.004.

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Oppelt, N. (2010)
The use of remote sensing data to assist crop modelling under climate change conditions. Journal of Applied Remote Sensing 4(1), 041896, dx.doi.org/10.1117/1.3491191.

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Oppelt, N. (2010)
Monitoring of the biophysical status of vegetation using multi-angular, hyperspectral remote sensing for the optimization of a physically-based SVAT model. Kieler Geographische Schriften 121, CAU Kiel (Germany), ISBN 978-3-923887-63-7.

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Oppelt, N.; Hank, T. (2009)
Improved modeling of maize growth by combining a biophysical model of photosynthesis with hyperspectral remote sensing. In: Henten, E.J.; Goense, D.; Lokhorst, C. (Eds.). Precision Agriculture '09. Wageningen Academic Publishers, pp. 133-140, dx.doi.org/10.3920/978-90-8686-664-9.

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Oppelt, N. (2008)
Vertical profiling of vegetation canopies using multi-angular remote sensing data. Canadian Journal of Remote Sensing 34(2), pp. 314-325, dx.doi.org/10.5589/m08-038.

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Oppelt, N.; Hank, T.; Mauser, W. (2007)
Assessment of vertical variation of chlorophyll using hyperspectral, multiangular imagery. In: Stafford, J. (Ed.). Precision Agriculture '07. Wageningen Academic Publishers, pp. 181-188, ISBN: 978-90-8686-024-1 (reviewed).

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Hank, T.; Oppelt, N.; Mauser, W. (2007)
Physically based modelling of photosynthetic processes. In: Stafford, J. (Ed.). Precision Agriculture '07. Wageningen Academic Publishers, pp. 165-172, ISBN: 978-90-8686-024-1 (reviewed).

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Oppelt, N.; Mauser, W. (2007)
Airborne Visible /Infrared Imaging Spectrometer AVIS: Design, Characterization and Calibration. Sensors 7(9), pp. 1934-1953, dx.doi.org/10.3390/s7091934.

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Oppelt, N.; Mauser, W. (2004)
Hyperspectral Monitoring of Physiological Parameters of Wheat during a Vegetation Period Using AVIS Data. International Journal of Remote Sensing 25(1), pp. 145-160.

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Oppelt, N.; Mauser, W. (2003)
Hyperspectral Remote Sensing - a Tool for the Derivation of Plant Nitrogen and its Spatial Variability. In: Stafford, J.; Werner, A. (Eds.). Precision Agriculture '03. Wageningen Academic Publishers, pp. 493-498 (reviewed).

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Oppelt, N. (2002)
Monitoring of Plant Chlorophyll and Nitrogen Status Using the Airborne Imaging Spectrometer AVIS. PhD thesis. Ludwig-Maximilians Universität München. edoc.ub.uni-muenchen.de/354/1/Oppelt_Natascha.pdf.