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Remote Sensing

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Springer Handbook of Geographic Information

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Abstract

Remote sensing is an essential method of collecting the geoinformation needed for investigations of the status or dynamics of environmental and social systems and processes. In this context, the multifunctional concept underlying remote sensing plays an important role. In particular, the removal of obstacles such as inadequate data availability or excessively long processing times when gathering information established remote sensing as a stable data source within the framework of environmental monitoring. The term remote sensing covers a multitude of different sensors, measuring principles, recording modes, and carrier platforms as well as combinations thereof. This makes it possible to generate data with various geometric, spectral, radiometric, and temporal resolutions to solve different problems. In the following, the physical basics of remote sensing (such as the theoretical basics and laws of electromagnetic radiation and the interaction of that radiation with natural materials), the technical basics of sensors, the properties of data (including aspects of data processing, processors, and processing chains as well as information extraction from remote sensing data), and selected areas of application of the results from remote sensing are explained.

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References

  • Mulders, M.A.: Remote sensing in soil science. In: Mulders, M.A. (ed.) Developments in Soil Science, vol. 15, p. 370. Elsevier, Amsterdam (1987)

    Google Scholar 

  • Jensen, J.R.: Remote Sensing of the Environment. An Earth Resource Perspective. Prentice Hall, Upper Saddle River, p. 592 (2000)

    Google Scholar 

  • Albertz, J.: Einführung in die Fernerkundung. Grundlagen der Interpretation von Luft- und Satellitenbildern. Wissenschaftliche Buchges., Darmstadt, p. 254 (2009)

    Google Scholar 

  • Liang, S., Strahler, A.: Retrieval of surface BRDF from multiangle remotely sensed data. Remote Sens. Environ. 50, 18–30 (1994)

    Article  Google Scholar 

  • Ulaby, F.T., Moore, R.K., Fung, A.K.: Microwave Remote Sensing, Vol. I: Microwave Remote Sensing Fundamentals and Radiometry. Addison-Wesley, London, p. 456 (1981)

    Google Scholar 

  • Gupta, R.P.: Remote Sensing Geology. Springer, Berlin (2018)

    Book  Google Scholar 

  • Wooster, M.J., Roberts, G., Smith, A.M.S., Johnston, J., Freeborn, P., Amici, S., Hudak, A.T.: Thermal remote sensing of active vegetation fires and biomass burning events. In: Thermal Infrared Remote Sensing, pp. 347–390. Springer, Dordrecht (2013)

    Chapter  Google Scholar 

  • EUMeTrain: Forest Fires (2012). http://www.eumetrain.org/data/3/30/print_5.htm#page_1.1.0, Accessed 10 Dec 2018

  • Rohde, R.A.: Radiation Transmitted by the Atmosphere (Wikipedia 2007) (2007). https://en.wikipedia.org/wiki/File:Atmospheric_Transmission.png, Accessed 23 Nov 2018

  • Boyd, D.S.: Physical basis of remote sensing. In: Atkinson, P.M. (ed.) Geoinformatics, Vol. I Encyclopedia of Life Support. pp. 184–208. EOLSS Publishers, Oxford (2008)

    Google Scholar 

  • Kraus, K., Schneider, W.: Fernerkundung – Physikalische Grundlagen und Aufnahmetechniken vol. 1. Dümmler, Bonn, p. 300 (1988)

    Google Scholar 

  • Long, M.W.: Radar Reflectivity of Land and Sea. Lexington Books, London, p. 306 (1975)

    Google Scholar 

  • Barrett, E.C., Curtis, L.F.: Introduction to Environmental Remote Sensing. Chapman Hall, London, p. 352 (1982)

    Google Scholar 

  • Combs, A.C., Weickmann, H.K., Mader, C., Tebo, A.: Application of infrared radiometers to meteorology. J. Appl. Meteorol. 4, 253–262 (1965)

    Article  Google Scholar 

  • Sahu, K.C.: Textbook of Remote Sensing and Geographical Information Systems. Atlantic, New Delhi, p. 503 (2008)

    Google Scholar 

  • van de Hulst, H.C.: Light Scattering by Small Particles. Wiley, New York, p. 470 (1957)

    Google Scholar 

  • Mishchenko, M.I., Travis, L.D., Lacis, A.A.: Scattering, Absorption, and Emission of Light by Small Particles. Cambridge Univ. Press, Cambridge, p. 445 (2002)

    Google Scholar 

  • Mo, T., Schmugge, T.J., Jachson, T.J.: Calculations of radar backscattering coefficient of vegetation-covered soils. Remote Sens. Environ. 15, 119–133 (1984)

    Article  Google Scholar 

  • Ulaby, F.T., Moore, R.K., Fung, A.K.: Microwave Remote Sensing, Vol. II: Active and Passive, Radar Remote Sensing and Surface Scattering and Emission Theory. Addison-Wesley, London, p. 609 (1982)

    Google Scholar 

  • von Hippel, A.R.: Dielectric Materials and Applications. MIT Technology Press, New York, p. 438 (1954)

    Google Scholar 

  • Nelson, S., D. Lindroth, Blake, R.: Dielectric properties of selected minerals at 1 to 22 GHz, Geophysics, 54(4), 1334–1349 (1989)

    Google Scholar 

  • Ulriksen, C.P.F.: Application of Impulse Radar to Civil Engineering. Dissertation. Univ. Lund, Lund, p. 175 (1982)

    Google Scholar 

  • Myllys, M., Simojoki, A.: Calibration of time domain reflectometry (TDR) for soil moisture measurements in cultivated peat soils. Suo 47(1), 1–6 (1996)

    Google Scholar 

  • de Vries, D.A.: Thermal properties of soils. In: van Wijk, W.R. (ed.) Physics of Plant Environment, pp. 210–235. North Holland, Amsterdam (1963)

    Google Scholar 

  • Arenson, L.U., Colgan, W., Marshall, H.-P.: Physical, thermal, and mechanical properties of snow, ice, and permafrost. In: Snow and Ice-Related Hazards, Risks, and Disasters, pp. 35–75. Academic Press, New York (2015) https://doi.org/10.1016/B978-0-12-394849-6.00002-0. Chap. 2

    Chapter  Google Scholar 

  • Mackowski, D.W.: MSTM Version 3.0 (2013). http://www.eng.auburn.edu/users/dmckwski/scatcodes/

  • Chandrasekhar, S.: Radiative Transfer. Dover, New York, p. 416 (1960)

    Google Scholar 

  • Popp, T.: Korrektur der atmosphärischen Maskierung zur Bestimmung der spektralen Albedo von Landoberflächen aus Satellitenmessungen. Dissertation. Univ. München, München, p. 137 (1993)

    Google Scholar 

  • Vermote, E.F., Vermeulen, A.: Atmospheric Correction Algorithm: Spectral Reflectances (MOD09). MODIS Algorithm Technical Background Document. Version 4.0, April 1999 (1999). https://eospso.nasa.gov/sites/default/files/atbd/atbd_mod08.pdf, Accessed 11 Feb 2019. NASA contract NAS5-96062

  • Erbertseder, T.: Quantifizierung von atmosphärischen Einflüssen auf NOAA-AVHRR NDVI-Daten und deren Korrektur. Diploma thesis. Ludwig-Maximilians-Univ. München, München (1998)

    Google Scholar 

  • Holzer-Popp, T., Bittner, M., Borg, E., Dech, S., Erbertseder, T., Fichtelmann, B., Schroedter, M.: Das Automatische Atmosphärenkorrekturverfahren ‘‘DurchBlick''. In: Blaschke, T. (ed.) Fernerkundung und GIS: Neue Sensoren – Innovative Methoden, pp. 78–87. Wichmann, Heidelberg (2001)

    Google Scholar 

  • Holzer-Popp, T., Bittner, M., Borg, E., Dech, S., Erbertseder, T., Fichtelmann, B., Schroedter, M. (2004): Process for Correcting Atmospheric Influences in Multispectral Optical Remote Sensing, Eur. Patent 1091188 B1

    Google Scholar 

  • Truckenbrodt, S.C., Baade, J.: Gebesee Database for the Enhancement of Crop Monitoring Applications: Evolution of Plant Physiology, Soil Moisture, Surface Reflectance and Atmospheric Conditions on the Agricultural Gebesee Test Site (Central Germany) in 2013 and 2014 (2017) https://doi.org/10.1594/PANGAEA.874251

    Book  Google Scholar 

  • Truckenbrodt, S.C., Schmullius, C.C.: Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: A database for the set-up and validation of EO-LDAS and satellite-aided retrieval models. Earth Syst. Sci. Data 10(1), 525–548 (2018)

    Article  Google Scholar 

  • Robinson, I., MacArthur, A.: The Field Spectroscopy Facility Post Processing Toolbox User Guide, Post Processing Spectral Data in MATLAB. Univ. Edinburgh, Edinburgh, p. 24 (2011)

    Google Scholar 

  • Vermote, E.F., Tanré, D., Deuzé, J.L., Herman, M., Morcrette, J.J.: Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans. Geosci. Remote Sens. 35(3), 675–686 (1997)

    Article  Google Scholar 

  • Blasch, G., Spengler, D., Itzerott, S., Wessolek, G.: Organic matter modeling at the landscape scale based on multitemporal soil pattern analysis using RapidEye data. Remote Sens. 7(9), 11125–11150 (2015)

    Article  Google Scholar 

  • Ehlers, M.: Neue Sensoren und Methoden in der Fernerkundung: Zwischen hochauflösend und hyperspektral. In: Pixel und Profile – Fernerkundung in den Bodenwissenschaften, pp. 7–16. Fachhochschule Osnabrück, Osnabrück (2005). https://www.mabga.de/fileadmin/HSOS/Studium/Studienangebot/Studiengaenge/Masterstudiengaenge/AuL/Boden__Gewaesser__Altlasten/Dokumente/2005_Tagungsband_Pixel_Profile.pdf (last access: 2 October 2019)

    Google Scholar 

  • Kramer, H.-J.: Observation of the Earth and Its Environment. Survey of Missions and Sensors, 4th edn. Springer, Berlin, p. 1510 (2002)

    Book  Google Scholar 

  • Campbell, J.B.: Introduction to Remote Sensing. Taylor Francis, London, p. 654 (2002)

    Google Scholar 

  • Westin, T.: Geometric modelling of imagery from the MSU-SK conical scanner. Bull. Soc. Franç. Photogramm. Télédétect. 159(3), 55–58 (2000)

    Google Scholar 

  • Ulaby, F.T., Long, D.G.: Microwave Radar and Radiometric Remote Sensing. Univ. Michigan Press, Ann Arbor, p. 984 (2014)

    Google Scholar 

  • Cracknell, A.P., Hayes, L.: Introduction to Remote Sensing. CRC, Boca Raton, p. 335 (2007)

    Book  Google Scholar 

  • de Loor, G.P.: Introduction and some general aspects of image formation in radar. Remote Sens. Rev. 1(1), 3–18 (1983)

    Article  Google Scholar 

  • Wise, P.: Spaceborne Remote Imagery – Its acquisition, processing and cartographic applications. Cartography 18(1), 9–20 (1989)

    Article  Google Scholar 

  • Kalkuhl, M.: Modulare Simulation komplexer SAR-Szenarien: Signalgenerierung, Positionsschätzung und Missionsplanung. Dissertation. Univ. Siegen, Siegen, p. 283 (2009)

    Google Scholar 

  • Hein, A.: Verarbeitung von SAR-Daten unter besonderer Berücksichtigung interferometrischer Anwendungen. Dissertation. Univ. Siegen, Siegen (1998)

    Google Scholar 

  • Goodman, J.W.: Some fundamental properties of speckle. J. Opt. Soc. Am. 66(11), 1145–1150 (1976)

    Article  Google Scholar 

  • Lee, J.S., Jurkevich, I., Dewaele, P., Wambacq, P., Oosterlinck, A.: Speckle filtering of synthetic aperture radar images: A review. Remote Sens. Rev. 8, 313–340 (1994)

    Article  Google Scholar 

  • van Zyl, J.J.: Radar Remote Sensing for Earth and Planetary Sciences. Jet Propulsion Laboratory, Pasadena (2007). http://www.its.caltech.edu/~ee157/lecture_note/Radar.pdf (last access: 10 February 2019)

    Google Scholar 

  • COAPS (Center for Ocean-Atmospheric Prediction Studies): Scatterometry & Ocean Vector Winds. Satellite Studies (2018). https://coaps.fsu.edu/scatterometry/about/overview.php, Accessed 28 Nov 2018

  • Winker, D.M., Vaughan, M.A., Omar, A., Hu, Y., Powell, K.A., Liu, Z., Hunt, W.H., Young, S.A.: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Ocean. Technol. 26(11), 2310–2323 (2009)

    Article  Google Scholar 

  • Winker, D.M., Pelon, J., Coakley Jr., J.A., Ackerman, S.A., Charlson, R.J., Colarco, P.R., Flamant, P., Fu, Q., Hoff, R.M., Kittaka, C., Kubar, T.L., Le Treut, H., Mccormick, M.P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M.A., Wielicki, B.A.: The CALIPSO mission: A global 3D view of aerosols and clouds. Bull. Am. Meteorol. Soc. 91(9), 1211–1229 (2010)

    Article  Google Scholar 

  • Illingworth, A.J., Barker, H.W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D.P., Fukuda, S., Hirakata, M., Hogan, R.J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T.Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M.W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., van Zadelhoff, G.-J.: The EarthCARE satellite: The next step forward in global measurements of clouds, aerosols, precipitation, and radiation. Bull. Am. Meteorol. Soc. 96(8), 1311–1332 (2015)

    Article  Google Scholar 

  • Stoffelen, A., Pailleux, J., Källén, E., Vaughan, J.M., Isaksen, L., Flamant, P., Wergen, W., Andersson, E., Schyberg, H., Culoma, A., Meynart, R., Endemann, M., Ingmann, P.: The atmospheric dynamics mission for global wind field measurement. Bull. Am. Meteorol. Soc. 86(1), 73–87 (2005)

    Article  Google Scholar 

  • Ehret, G., Flamant, P., Amediek, A., Ciais, P., Gibert, F., Fix, A., Kiemle, C., Quatrevalet, M., Wirth, M.: The French-German climate monitoring initiative on global observations of atmospheric methane. In: 25th Int. Laser Radar Conf. pp. 1348–1351. (2010)

    Google Scholar 

  • Konecny, G.: Small satellites – A tool for Earth observation? – The classification of satellites according to mass is usually as follows. ISPRS Archives 35(B4), 580–582 (2004)

    Google Scholar 

  • Allain, R.: What’s so Special About Low Earth Orbit? (2015). https://www.wired.com/2015/09/whats-special-low-earth-orbit/, Accessed 13 Nov 2018

  • Hillhouse, J.D.: Sun Synchronous Orbits for the Earth Solar Power Satellite System (1999). http://design.ae.utexas.edu/mission_planning/mission_resources/orbital_mechanics/Sun_Synchronous_Orbits.pdf, Accessed 30 Jan 2019

  • Hoffmann, E.J.: Space communications. In: Pisacane, V.L. (ed.) Fundamentals of Space Systems, pp. 565–599. Oxford Univ. Press, Oxford (2005)

    Google Scholar 

  • Holdrige, M.E.: Space mission operations. In: Pisacane, V.L. (ed.) Fundamentals of Space Systems, pp. 754–771. Oxford University Press, Oxford (2005)

    Google Scholar 

  • Montenbruck, O., Gill, E.: Satellite Orbits: Models, Methods and Applications. Springer, Berlin, p. 369 (2012)

    MATH  Google Scholar 

  • NASA: NASA Earth Observatory. Three Classes of Orbit (2009). https://earthobservatory.nasa.gov/Features/OrbitsCatalog/page2.php, Accessed 13 Nov 2018

  • Qiao, L., Rizos, C., Dempster, A.G.: Satellites orbit design for the Australian Garada Project. In: Proc. 24th Int. Tech. Meet. (ION GNSS 2011), pp. 1075–1081. (2011)

    Google Scholar 

  • Reiniger, K., Diedrich, E., Mikusch, E.: Aspects of ground segment design for Earth observation missions (1 August 2006). In: Summer School Alpbach 2016 ‘‘Monitoring of Natural Hazards from Space'', p. 53. (2006)

    Google Scholar 

  • Baret, F., Buis, S.: Estimating canopy characteristics from remote sensing observations: Review of methods and associated problems. In: Liang, S. (ed.) Advances in Land Remote Sensing, pp. 173–201. Springer, Dordrecht (2008)

    Chapter  Google Scholar 

  • Clark, P.E., Rilee, M.L.: Remote Sensing Tools for Exploration – Observing and Interpreting the Electromagnetic Spectrum. Springer, New York, p. 346 (2010)

    Book  Google Scholar 

  • USGS: Landsat 8 (L8) Data Users Handbook, LSDS-1574, Version 3.0, October 2018 (2018). https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/LSDS-1574_L8_Data_Users_Handbook.pdf, Accessed 31 Jan 2019

  • Fichtelmann, B., Borg, E.: A new approach of geo-rectification for time series satellite data based on a graph-network. Lect. Notes Comput. Sci. 9158, 217–232 (2015)

    Article  Google Scholar 

  • Fichtelmann, B., Borg, E., Günther, A., Guder, H.-G., Reimer, R., Wolff, E., Damerow, H.: Chlorophyll map of the Baltic Sea – The results of an automatic processing chain from data reception up to archiving and delivery of value-added products. In: Bruzzone, L., Smith, P. (eds.) European Commission – Joint Research Centre – Special Publications I.01.122, p. 50. (2001)

    Google Scholar 

  • Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G.: High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013)

    Article  Google Scholar 

  • Fichtelmann, B., Günther, K.P., Borg, E.: Adaption of a Self-Learning Algorithm for Dynamic Classification of Water Bodies to SPOT VEGETATION Data. Lect. Notes Comput. Sci. 9158, 177–192 (2015)

    Article  Google Scholar 

  • Liang, S. (ed.): Quantitative Remote Sensing of Land Surfaces. Wiley, Hoboken (2004)

    Google Scholar 

  • Schwarz, J., Maass, H., Bettac, H.-D.: Data reception of small satellites in a multimission scenario. Acta Astron. 46(2/6), 221–228 (2000)

    Article  Google Scholar 

  • Schwarz, J.: Highrate data acquisition system for Earth observation satellites. In: Proc. Eur. Telem. Conf. Exhib. Data Rec. Storage, pp. 122–131. (2002)

    Google Scholar 

  • Böhme, C., Bouwer, P.J., Prinsloo, M.: Real-time stream processing for active fire monitoring on Landsat 8 direct reception data. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40(7), 765–770 (2015)

    Article  Google Scholar 

  • Krause, D., Schwarz, E., Voinov, S., Damerow, H., Tomecki, D.: Sentinel-1 near real-time application for maritime situational awareness. CEAS Space J. 11(1), 45–53 (2019)

    Article  Google Scholar 

  • Haralick, R.M.: Automatic remote sensor image processing. In: Rosenfield, A. (ed.) Digital Picture Analysis, pp. 5–63. Springer, New York (1976)

    Chapter  Google Scholar 

  • Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T.-K.: A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci. Remote Sens. Lett. 3(1), 68–72 (2006)

    Article  Google Scholar 

  • Irish, R.R.: LANDSAT 7 automatic cloud cover assessment. Proc. SPIE 4049, 348–355 (2000)

    Article  Google Scholar 

  • Biasutti, R.: Cloud cover evaluation. In: 8th LANDSAT Tech. Work. Group Meet. p. 10. (2000)

    Google Scholar 

  • Borg, E., Fichtelmann, B., Asche, H.: Assessment for remote sensing data: Accuracy of interactive data quality interpretation. Lect. Notes Comput. Sci. 6783, 366–375 (2011)

    Article  Google Scholar 

  • Scaramuzza, P., Micijevic, E., Chander, G.: SLC Gap-Filled Products – Phase One Methodology. SLC Gap-Fill Methodology, 18 March 2004 (2004). https://landsat.usgs.gov/filling-gaps-use-scientific-analysis#2, Accessed 22 Nov 2018

  • USGS: Landsat Missions. Landsat 7 Data Users Handbook – Appendix A, Known Issues (2018). https://landsat.usgs.gov/landsat-7-data-users-handbook-appendix-a, Accessed 22 Nov 2018

  • Richards, J. A., Jia, X.: Remote sensing digital image analysis: An introduction, 4th edn. Springer-Verlag, Berlin, Germany, p. 439 (2006)

    Google Scholar 

  • Kaur, S.: Noise types and various removal techniques. Int. J. Adv. Res. Electron. Commun. Eng. 4(2), 226–230 (2015)

    Google Scholar 

  • Al-amri, S.S., Kalyankar, N.V., Khamitkar, S.D.: A comparative study of removal noise from remote sensing image. Int. J. Comput. Sci. Issues 7(1), 32–36 (2010)

    Google Scholar 

  • Bhosale, N.P., Manza, R.R.: A review on noise removal techniques from remote sensing images. In: Nat. Conf. Radhai College (2013) https://doi.org/10.13140/RG.2.1.1104.6168. CMS-13

    Chapter  Google Scholar 

  • Townshend, J.R.G., Huang, C., Kalluri, S.N.V., Defries, R.S., Liang, S., Yang, K.: The impact of misregistration on change detection. IEEE Trans. Geosci. Remote Sens. 30(5), 1054–1060 (1992)

    Article  Google Scholar 

  • Dai, X., Khorram, S.: The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Trans. Geosci. Remote Sens. 36(5), 1566–1577 (1998)

    Article  Google Scholar 

  • Verbyla, D.L., Boles, S.H.: Bias in land cover change estimates due to misregistration. Int. J. Remote Sens. 21, 3553–3560 (2000)

    Article  Google Scholar 

  • Hill, L.L.: Georeferencing: The Geographic Associations of Information. MIT Press, London, p. 280 (2006)

    Book  Google Scholar 

  • Hackeloeer, A., Klasing, K., Krisp, J.M., Meng, L.: Georeferencing: A review of methods and applications. Annals GIS 20(1), 61–69 (2014)

    Article  Google Scholar 

  • Richards, R.A.: Remote Sensing Digital Image Analysis – An Introduction. Springer, Berlin, Heidelberg, p. 494 (2013)

    Google Scholar 

  • Toutin, T.: Geometric processing of remote sensing images: Models, algorithms and methods. Int. J. Remote Sens. 25(10), 1893–1924 (2004). Review article

    Article  Google Scholar 

  • Ries, C.: Ein allgemeiner Ansatz zur Georeferenzierung von multispektralen Flugzeugscanneraufnahmen. Dissertation. TU Wien, Wien (2004)

    Google Scholar 

  • Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. 24(4), 325–376 (1992)

    Article  Google Scholar 

  • Bannari, A., Morin, D., Bénié, G.B., Bonn, F.J.: A theoretical review of different mathematical models of geometric corrections applied to remote sensing images. Remote Sens. Rev. 13, 27–47 (1995)

    Article  Google Scholar 

  • Fonseca, L.M.G., Manjunath, B.S.: Registration techniques for multisensor remotely sensed imagery. Photogramm. Eng. Remote Sens. 62(9), 1049–1056 (1996)

    Google Scholar 

  • Bartoli, G.: Image Registration Techniques: A Comprehensive Survey (2007). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.471.1134&rank=1, Accessed 25 Jan 2019

  • Hoja, D., Schneider, M., Müller, R., Lehner, M., Reinartz, P.: Comparison of orthorectification methods suitable for rapid mapping using direct georeferencing and RPC for optical satellite data. ISPRS Archives 37(B4), 1617–1623 (2008)

    Google Scholar 

  • Norjamäki, I., Tokola, T.: Comparison of atmospheric correction methods in mapping timber volume with multitemporal Landsat images in Kainuu, Finland. Photogramm. Eng. Remote Sens. 73(2), 155–163 (2007)

    Article  Google Scholar 

  • Richter, R., Schläpfer, D.: Atmospheric/Topographic Correction for Satellite Imagery: ATCOR-2/3 User Guide, Version 9.2.0. German Aerospace Center (DLR), Wessling (2018). DLR Report DLR-IB 565-01/2018

    Google Scholar 

  • Chavez, P.S.: An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens. Environ. 24, 459–479 (1988)

    Article  Google Scholar 

  • Kaufman, Y.J., Wald, A.E., Remer, L.A., Gao, B.C., Li, R.R., Flynn, L.: The MODIS 2.1-\({\upmu}\)m channel – Correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans. Geosci. Remote Sens. 35, 1286–1298 (1997)

    Article  Google Scholar 

  • Rahman, H., Dedieu, G.: SMAC: A simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Int. J. Remote Sens. 15(1), 123–143 (1994)

    Article  Google Scholar 

  • Song, C., Woodcock, C.E.: Monitoring forest succession with multitemporal Landsat images: Factors of uncertainty. IEEE Trans. Geosci. Remote Sens. 41, 2557–2567 (2003)

    Article  Google Scholar 

  • Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A.: Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sens. Environ. 75, 230–244 (2001)

    Article  Google Scholar 

  • Chavez, P.S.: Radiometric calibration of Landsat thematic mapper multispectral images. Photogramm. Eng. Remote Sens. 55, 1285–1294 (1989)

    Google Scholar 

  • Kneizys, F.X., Shettle, E.P., Gallery, W.O., Chetwynd, J.H., Anderson, G.P., Abreu, L.W., Gallery, W.O., Selby, J.E.A., Clough, S.A.: User Guide to LOWTRAN 7. Environmental Research Papers No. 1010, p. 137 (1988)

    Google Scholar 

  • Kneizys, F.X., Robertson, D.C., Abreu, L.W., Acharya, P., Anderson, G.P., Rothman, L.S., Chetwynd, J.H., Selby, J.E.A., Shettle, E.P., Gallery, W.O., Berk, A., Clough, S.A., Bernstein, L.S.: The MODTRAN 2/3 Report and LOWTRAN 7 Model (1996). www.gps.caltech.edu/~vijay/pdf/modrept.pdf, Accessed 30 Jan 2019

  • Berk, A., Anderson, G. P., Bernstein, L. S., Acharya, P. K., Dothe, H., Matthew, M. W. ,Adler-Golden, S. M., Chetwynd, Jr., J. H., Richtsmeier, S. C., Pukall, B., Allred, C. L., Jeong, L. S., Hoke, M. L.: MODTRAN4 Radiative Transfer Modeling for Atmospheric Correction. SPIE Proceeding, Optical Spectroscopic Techniques and Instrumentation for Atmospheric and Space Research III, Volume 3756, (1999), https://www.spectral.com/wp-content/uploads/2017/04/MODTRAN4_Radiative_Transfer.pdf (last access: 2019/02/10)

  • Berk, A., Anderson, G.P., Acharya, P.K., Chetwynd, Jr., J. H.,Bernstein, L.S., Dothe, H., Matthew, M.W., Adler-Golden, S.M., Richtsmeier, S. C., Pukall, B., Allred, C. L., Jeong, L. S., Hoke, M. L.: MODTRAN4 User's Manual. Air Force Research Laboratory, Space Vehicles Directorate, Air Force Materiel Command, Hanscom AFB, MA 01731-3010.- p. 94. (1999), ftp://ftp.pmodwrc.ch/pub/Vorlesung%20K+S/MOD4_user_guide.pdf (last access: 2019/02/10)

  • Popp, T.: Correcting atmospheric masking to retrieve the spectral albedo of land surfaces from satellite. Int. J. Remote Sens. 16, 3483–3508 (1995)

    Article  Google Scholar 

  • Fraser, R.S., Ferrare, R.A., Kaufman, Y.J., Mattoo, S.: Algorithm for atmospheric corrections of aircraft and satellite imagery. Int. J. Remote Sens. 13, 541–557 (1992)

    Article  Google Scholar 

  • Richter, R., Schlaepfer, D., and Mueller, A.: Operational atmospheric correction for imaging spectrometers accounting the smile effect. IEEE TGRS, Vol. 49, 1772–1780 (2011)

    Google Scholar 

  • Richter, R.: Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Comput. Geosci. 22, 675–681 (1996)

    Google Scholar 

  • Richter, R.: Correction of atmospheric and topographic effects for high spatial resolution satellite imagery. Int. J. Remote Sens. 18, 1099–1111 (1997)

    Google Scholar 

  • Kaufman, Y.J., Sendra, C.: Automatic atmospheric correction. Int. J. Remote Sens. 9, 1357–1381 (1988)

    Article  Google Scholar 

  • Maiersperger, T.K., Scaramuzza, P.L., Leigh, L., Shrestha, S., Gallo, K.P., Jenkerson, C.B., Dwyer, J.L.: Characterizing LEDAPS surface reflectance products by comparisons with AERONET, field spectrometer, and MODIS data. Remote Sens. Environ. 136, 1–13 (2013)

    Article  Google Scholar 

  • Claverie, M., Vermote, E.F., Franch, B., Masek, J.G.: Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products. Remote Sens. Environ. 169, 390–403 (2015)

    Article  Google Scholar 

  • Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Müller-Wilm, U., Gascon, F.: Sen2Cor version 2.5: Radiometric Validation – 2nd Sentinel-2 Validation Teem Meeting 29–31 January 2018. ESA-ESRIN, Frascati, p. 14 (2018)

    Google Scholar 

  • Louis, J., Pflug, B., Main-Knorn, M., Debaecker, V., Müller-Wilm, U., Gascon, F.: Integration and Assimilation of metrological (ECMWF) aerosol estimates into Sen2Cor atmospheric correction. In: Proc. IGARSS, p. 4. (2018)

    Google Scholar 

  • Wald, L.: Some terms of reference in data fusion. IEEE Trans. Geosci. Remote Sens. 37(3), 1190–1193 (1999)

    Article  Google Scholar 

  • Pohl, C., van Genderen, J.L.: Multisensor image fusion in remote sensing: Concepts, methods and applications. Int. J. Remote Sens. 19(5), 823–854 (1998)

    Article  Google Scholar 

  • Smith, A.R.: Color gamut transformation pairs. In: Proc. 5th Annu. Conf. Comput. Graph. Interact. Tech. (SIGGRAPH ’78), pp. 12–19. (1978)

    Google Scholar 

  • Joblove, G.H., Greenberg, D.: Color spaces for computer graphics. ACM SIGGRAPH Comput. Graph. 12(3), 20–25 (1978)

    Article  Google Scholar 

  • Hayden, R., Henkel, G.W.J., Bare, J.E.: Application of the IHS color transform to the processing of multisensor data and image enhancement. In: Proc. Int. Symp. Remote Sens. Environ. pp. 599–616. (1982)

    Google Scholar 

  • Polidori, L., Mangolini, M.: Potentials and limitations of multisensor data fusion. In: Proc. EARSeL Conf. Fus. Earth Data, pp. 13–19. (1996)

    Google Scholar 

  • Cetin, M., Mussaoglu, N.: Merging hyperspectral and panchromatic image data: Qualitative and quantitative analysis. Int. J. Remote Sens. 30(7), 1779–1804 (2009)

    Article  Google Scholar 

  • Vivone, G., Alparone, L., Chanussot, J., Mura, M.D., Garzelli, A., Licciardi, G.A., Restaino, R., Wald, L.: A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 53(5), 2565–2586 (2015)

    Article  Google Scholar 

  • Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)

    Article  Google Scholar 

  • Goodman, I.R., Mahler, R.P., Nguyen, H.T.: Mathematics of Data Fusion. Theory and Decision Library B, vol. 37. Springer, Dordrecht (1997)

    Book  MATH  Google Scholar 

  • Haberäcker, P.: Digitale Bildverarbeitung – Grundlagen und Anwendungen. Hanser, München, p. 404 (1989)

    MATH  Google Scholar 

  • Canty, M.J.: Image Analysis, Classification and Change Detection in Remote Sensing – With Algorithms for ENVI/IDL and Python. CRC, Boca Raton (2014)

    MATH  Google Scholar 

  • Pavlides, T.: Algorithmen zur Grafik und Bildverarbeitung. Heise, Hannover, p. 508 (1990)

    Google Scholar 

  • Bässmann, H., Kreyss, J.: Bildverarbeitung ad Oculos, 3rd edn. Springer, Berlin, p. 473 (1998)

    Book  Google Scholar 

  • Liedke, C.-E., Ender, M.: Wissensbasierte Bildverarbeitung. Nachrichtentechnik, vol. 19. Springer, Berlin, Heidelberg (1989)

    Book  MATH  Google Scholar 

  • Rees, W.G.: Physical Principles of Remote Sensing. Cambridge Univ. Press, Cambridge, p. 345 (2001)

    Book  Google Scholar 

  • Lu, D., Weng, Q.: Review article – A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007)

    Article  Google Scholar 

  • Prasad, S.V.S., Savithri, T.S., Krishn, I.V.M.: Techniques in image classification. A survey. Version 1. Glob. J. Res. Eng. F 15(6), 17–32 (2015)

    Google Scholar 

  • Borg, E., Fichtelmann, B., Asche, H.: Cloud classification in JPEG-compressed remote sensing data (LANDSAT 7/ETM+). Lect. Notes Comput. Sci. 7334, 347–357 (2012)

    Article  Google Scholar 

  • Zhukov, B., Oertel, D., Lanzl, F., Reinhäckel, G.: Unmixing-based multisensor multiresolution image fusion. IEEE Trans. Geosci. Remote Sens. 37(3), 1212–1226 (1999)

    Article  Google Scholar 

  • Hansen, M., Dubayah, R., Defries, R.: Classification trees: An alternative to traditional land cover classifiers. Int. J. Remote Sens. 17(5), 1075–1081 (1996)

    Article  Google Scholar 

  • Schowengerdt, R.A.: On the estimation of spatial-spectral mixing with classifier likelihood functions. Pattern Recognit. Lett. 17(13), 1379–1387 (1996)

    Article  Google Scholar 

  • Pal, M., Mather, P.M.: An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 86, 554–565 (2003)

    Article  Google Scholar 

  • Chen, K.S., Tzeng, Y.C., Chen, C.F., Kao, W.L.: Land-cover classification of multispectral imagery using a dynamic learning neural network. Photogramm. Eng. Remote Sens. 61, 403–408 (1995)

    Google Scholar 

  • Witten, I.H., Frank, E., Hall, M.A.: Data Mining. Elsevier, Amsterdam, p. 629 (2011)

    Google Scholar 

  • Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction. Springer, New York, p. 745 (2009)

    MATH  Google Scholar 

  • Mitra, P., Shankar, B.U., Pal, S.K.: Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognit. Lett. 25, 1067–1074 (2004)

    Article  Google Scholar 

  • Lohmann, G.: An evidential reasoning approach to the classification of satellite images. Lect. Notes Comput. Sci. 548, 227–231 (1991)

    Article  Google Scholar 

  • Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58(3/4), 239–258 (2004)

    Article  Google Scholar 

  • Stuckens, J., Coppin, P.R., Bauer, M.E.: Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sens. Environ. 71, 282–296 (2000)

    Article  Google Scholar 

  • Congalton, R.G.: A review of assessing the accuracy of classification of remotely sensed data. Remote Sens. Environ. 37, 35–46 (1991)

    Article  Google Scholar 

  • Congalton, R.G., Green, K.: Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd edn. CRC, Boca Raton, p. 192 (1999)

    Google Scholar 

  • Dorigo, W.A., Zurita-Milla, R., De Wit, A.J.W., Brazile, J., Singh, R., Schaepman, M.E.: A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 9, 165–193 (2007)

    Google Scholar 

  • Verrelst, J., Malenovský, Z., Van der Tol, C., Camps Valls, G., Gastellu Etchegorry, J.-P., Lewis, P., North, P., Moreno, J.: Quantifying vegetation biophysical variables from imaging spectroscopy data: A review on retrieval methods. Surv. Geophys. 5(1), 589–629 (2018)

    Google Scholar 

  • Verhoef, W.: Theory of radiative transfer models applied in optical remote sensing of vegetation canopies, Thesis Landbouwuniversiteit Wageningen, pp. 310. Grafisch Service Centrum Van Gils b.v., Wageningen (1998)

    Google Scholar 

  • Verhoef, W., Bach, H.: Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models. Remote Sens. Environ. 87, 23–41 (2003)

    Article  Google Scholar 

  • Combal, B., Baret, F., Weiss, M., Trubuil, A., Macé, D., Pragnère, A., Myneni, R., Knyazikhin, Y., Wang, L.: Retrieval of canopy biophysical variables from bidirectional reflectance. Using prior information to solve the ill-posed inverse problem. Remote Sens. Environ. 84, 1–15 (2002)

    Article  Google Scholar 

  • Garabedian, P.: Partial differential equations. Wiley, New York, p. 672 (1964)

    MATH  Google Scholar 

  • Ertel, W.: Grundkurs Künstliche Intelligenz – Folien zum Buch (2018). www.hs-weingarten.de/~ertel/kibuch/kibuch-folien.pdf, Accessed 30 Jan 2019

  • Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org/ (last access: 23 January 2019)

    MATH  Google Scholar 

  • Bengio, Y.: Deep Learning (2015). http://www.iro.umontreal.ca/~bengioy/talks/lisbon-mlss-19juillet2015.pdf, Accessed 25 Jan 2019

  • Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A.: Deep learning in remote sensing applications: A meta-analysis and review. ISPRS J. Photogramm. Remote Sens. 152, 166–177 (2019)

    Google Scholar 

  • Mitchell, T.: Machine Learning. McGraw-Hill, New York, p. 414 (1997)

    MATH  Google Scholar 

  • Boutaba, R., Salahuddin, M.A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., Caicedo, O.M.: A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities. J. Internet Serv. Appl. 9, 16 (2018)

    Article  Google Scholar 

  • Solares, C., Sanz, A.M.: Bayesian network classifiers. An application to remote sensing image classification. In: Proc. 6th WSEAS Int. Conf. Neural Networks, pp. 62–67. (2005)

    Google Scholar 

  • Solares, C., Sanz, A.M.: Different Bayesian network models in the classification of remote sensing images. Lect. Notes Comput. Sci. 4881, 10–16 (2007)

    Article  Google Scholar 

  • Mountrakis, G., Ogole, J.C.: Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 66(3), 247–259 (2011)

    Article  Google Scholar 

  • Belgiu, M., Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016)

    Article  Google Scholar 

  • Kolesnikov, A.: Efficient Algorithms for Vectorization and Polygonal Approximation. Dissertation. Univ. Joensuu, Joensuu, p. 110 (2003)

    Google Scholar 

  • Borg, E., Ressl, R., Günther, K.: Fernerkundungsdaten und -produkte für die Landschaftsstrukturanalyse. In: Walz, U., Lutze, G., Schultz, A., Syrbe, R.-U. (eds.) Landschaftsstruktur – Methoden und Anwendungen im Kontext von naturräumlicher Vorprägung und Nutzung IÖR-Schriften, vol. 43, pp. 79–90. Sächsische Druck- und Verlagshaus AG, Dresden (2004)

    Google Scholar 

  • H. Leser: Landschaftsökologie. Ansatz, Modelle, Methodik, Anwendung. 4. neu bearbeitete Auflage. Ulmer, Stuttgart 1997, ISBN 3-8252-0521-5, S. 145, 148 (UTB 521)

    Google Scholar 

  • Tomlinson, C.J., Chapman, L., Thornes, J.E., Baker, C.: Remote sensing land surface temperature for meteorology and climatology: A review. Meteorol. Appl. 18, 296–306 (2011)

    Article  Google Scholar 

  • Adler, R.F., Negri, A.J.: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteorol. 27, 30–51 (1988)

    Article  Google Scholar 

  • Vicente, G.A., Scofield, R.A., Menzel, W.P.: The operational GOES infrared rainfall estimation technique. Bull. Am. Meteorol. Soc. 79, 1883–1898 (1998)

    Article  Google Scholar 

  • Velden, C.S., Olander, T.L., Wanzong, S.: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description, and case analysis. Mon. Weather Rev. 126, 1202–1218 (1998)

    Article  Google Scholar 

  • Velden, C.S., Stettner, D., Daniels, J.: Wind vector fields derived from GOES rapid-scan imagery. In: Proc. 10th Conf. Satell. Meteorol. Oceanogr. pp. 20–23. (2000)

    Google Scholar 

  • King, M.D., Menzel, W.P., Kaufman, Y.J., Tanré, D., Gao, B.C., Platnick, S., Ackerman, S.A., Remer, L., Pincus, R., Hubanks, P.A.: Cloud and aerosol properties, precipitable water, and profiles of temperature and humidity from MODIS. IEEE Trans. Geosci. Remote Sens. 41(2), 442–458 (2003)

    Article  Google Scholar 

  • Morsy, M., Dietrich, P., Scholten, T., Michaelides, S., Borg, E., Sherief, Y.: The potential of using satellite-related precipitation data sources in arid regions (CHAPTER 7). In: Michaelides, S. (ed.) Precipitation Science - Measurement, Remote Sensing, Microphysics, and Modeling, pp. 201–237. Elsevier Inc., Amsterdam (2021). Available from https://doi.org/10.1016/B978-0-12-822973-6.00002-0.

    Google Scholar 

  • Saunders, R.W.: An automated scheme for the removal of cloud contamination from AVHRR radiances over Western Europe. Int. J. Remote Sens. 7(7), 867–886 (1986)

    Article  Google Scholar 

  • Saunders, R.W., Kriebel, K.T.: An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens. 9(1), 123–150 (1988)

    Article  Google Scholar 

  • Feijt, A., De Valk, P., Van Der Veen, S.: Cloud detection using meteosat imagery and numerical weather prediction model data. J. Appl. Meteorol. Clim. 39(7), 1017–1030 (2010)

    Article  Google Scholar 

  • Taravat, A., Proud, S., Peronaci, S., Del Frate, F., Oppelt, N.: Multilayer perceptron neural networks model for meteosat second generation SEVIRI daytime cloud masking. Remote Sens. 7(2), 1529–1539 (2015)

    Article  Google Scholar 

  • Szturc, J., Osrodka, K., Jurczyk, A., Otop, I., Linkowska, J., Bochenek, B., Pasierb, M. : Quality control and verification of precipitation observations, estimates, and forecasts (CHAPTER 3). In: Michaelides, S. (ed.) Precipitation Science - Measurement, Remote Sensing, Microphysics, and Modeling, pp. 91–133. Elsevier Inc., Amsterdam (2021). Available from https://doi.org/10.1016/B978-0-12-822973-6.00002-0

    Google Scholar 

  • WMO: Weather Radar – Part 1: System Performance and Operation (Draft Text of the Common ISO/WMO Standard) (2008). http://www.wmo.int/pages/prog/www/IMOP/ISO/Std_Radar_Part1_For_Members_Review.pdf, Accessed 23 Nov 2018

  • Marshall, J.S., Palmer, W.M.: The distribution of raindrops with size. J. Atmos. Sci. 5, 165–166 (1948)

    Google Scholar 

  • Atlas, D., Srivastava, R.C., Sekhon, R.S.: Doppler radar characteristics of precipitation at vertical incidendence. Rev. Geophys. Space Phys. 11, 1–35 (1973)

    Article  Google Scholar 

  • Kumar, L.S., Lee, Y.H., Yeo, J.X., Ong, J.T.: Tropical rain classification and estimation of rain from Z-R (reflectivity-rain rate) relationships. Prog. Electromagn. Res. B 32, 107–127 (2011)

    Article  Google Scholar 

  • Lehner, S., Tings, B.: Maritime products using TerraSAR-X and Sentinel-1 imagery. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XL(7/W3), 967–973 (2015)

    Article  Google Scholar 

  • Gayathri, K.D., Ganasri, B.P., Dwarakish, G.S.: Applications of remote sensing in satellite oceanography: A review, International Conference on Water Resources, Coastal and Ocean Engineering (ICWRCOE'15). Aquat. Procedia 4, 579–584 (2015)

    Google Scholar 

  • Klemas, V.: Fisheries applications of remote sensing: An overview. Fish. Res. 148, 124–136 (2013)

    Article  Google Scholar 

  • Fingas, M., Brown, C.E.: A review of oil spill remote sensing. Sensors 18(1), 91 (2018)

    Google Scholar 

  • Maximenko, N., Corradi, P., Law, K.L., Van Sebille, E., Garaba, S.P., Lampitt, R.S., Galgani, F., Martinez-Vicente, V., Goddijn-Murphy, L., Veiga, J.M., Thompson, R.C., Maes, C., Moller, D., Löscher, C.R., Addamo, A.M., Lamson, M.R., Centurioni, L.R., Posth, N.R., Lumpkin, R., Vinci, M., Martins, A.M., Pieper, C.D., Isobe, A., Hanke, G., Edwards, M., Chubarenko, I.P., Rodriguez, E., Aliani, S., Arias, M., Asner, G.P., Brosich, A., Carlton, J.T., Chao, Y., Cook, A.-M., Cundy, A.B., Galloway, T.S., Giorgetti, A., Goni, G.J., Guichoux, Y., Haram, L.E., Hardesty, B.D., Holdsworth, N., Lebreton, L., Leslie, H.A., Macadam-Somer, I., Mace, T., Manuel, M., Marsh, R., Martinez, E., Mayor, D.J., Le Moigne, M., Molina Jack, M.E., Mowlem, M.C., Obbard, R.W., Pabortsava, K., Robberson, B., Rotaru, A.-E., Ruiz, G.M., Spedicato, M.T., Thiel, M., Turra, A., Wilcox, C.: Toward the Integrated Marine Debris Observing System. Front. Mar. Sci. 6, 447 (2019) https://doi.org/10.3389/fmars.2019.00447

  • Kako, S., Isobe, A., Magome, S.: Sequential monitoring of beach litter using webcams. Mar. Pollut. Bull. 60(5), 775–779 (2010)

    Article  Google Scholar 

  • Kako, S., Isobe, A., Magome, S.: Low altitude remote-sensing method to monitor marine and beach litter of various colors using a balloon equipped with a digital camera. Mar. Pollut. Bull. 64(6), 1156–1162 (2012)

    Article  Google Scholar 

  • Odum, E.P.: Grundlagen der Ökologie. Thieme, Stuttgart, p. 836 (1983)

    Google Scholar 

  • Gholizadeh, M.H., Melesse, A.M., Reddi, L.: A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors 16(8), 43 (2016)

    Article  Google Scholar 

  • Kutser, T., Arst, H., Miller, T., Käärmann, L., Milius, A.: Telespectrometrical estimation of water transparency, chlorophyll-a and total phosphorus concentration of Lake Peipsi. Int. J. Remote Sens. 16(16), 3069–3085 (1995)

    Article  Google Scholar 

  • Gómez, B., Mertes, L.A.K., Phillpe, J.D., Magilligan, F.J., James, L.A.: Sediment characteristics of an extreme flood: 1993 upper Mississippi River valley. Geology 23(11), 963–966 (1995)

    Article  Google Scholar 

  • Mannheim, S., Segl, K., Heim, B., Kaufmann, H.: Monitoring of lake water quality using hyperspectral CHRIS-PROBA data. In: Proc. 2nd CHRIS/PROBA Workshop, pp. 28–30. (2004)

    Google Scholar 

  • Dekker, A.G., Vos, R., Peters, S.: Analytical algorithms for lake water TSM estimation for retrospective analyses of TM and SPOT sensor data. Int. J. Remote Sens. 23(1), 15–35 (2002)

    Article  Google Scholar 

  • Wloczyk, C., Richter, R., Borg, E., Neubert, W.: Sea and lake surface temperaure retrieval from Landsat thermal data in Northern Germany. Int. J. Remote Sens. 27(12), 2489–2502 (2006)

    Article  Google Scholar 

  • Markogianni, V., Dimitriou, E., Tzortziou, M.: Monitoring of chlorophyll-a and turbidity in Evros River (Greece) using Landsat imagery. In: Proc. 1st Int. Conf. Remote Sens. Geoinf. Environ. (RSCy2013) (2013)

    Google Scholar 

  • Duan, W., Takara, K., He, B., Luo, P., Nover, D., Yamashiki, Y.: Spatial and temporal trends in estimates of nutrient and suspended sediment loads in the Ishikari River, Japan, 1985 to 2010. Sci. Total Environ. 461/462, 499–508 (2013)

    Article  Google Scholar 

  • Karaska, M.A., Huguenin, R.L., Beacham, J.L., Wang, M.-H., Jensen, J.R., Kaufmann, R.S.: AVIRIS measurements of chlorophyll, suspended minerals, dissolved organic carbon, and turbidity in the Neuse River, North Carolina. Photogramm. Eng. Remote Sens. 70(1), 125–133 (2004)

    Article  Google Scholar 

  • Jensen, J.R., Hodgson, M.E., Christensen, E., Mackey Jr, H.E., Tinney, L.R.: Remote sensing inland wetlands: A multispectral approach. Photogramm. Eng. Remote Sens. 52(1), 87–100 (1986)

    Google Scholar 

  • Milton, E.J., Hughes, P.D., Anderson, K., Schulz, J., Lindsay, R., Kelday, S.B., Hill, C.T.: Remote Sensing of Bog Surfaces (2005) JNCC Report No. 366, pp. 99

    Google Scholar 

  • Dabrowska-Zielinska, K., Budzynska, M., Tomaszewska, M., Bartold, M., Gatkowska, M., Malek, I., Turlej, K., Napiorkowska, M.: Monitoring wetlands ecosystems using ALOS PALSAR (L-Band, HV) supplemented by optical data: A case study of Biebrza Wetlands in northeast Poland. Remote Sens. 6(2), 1605–1633 (2014)

    Article  Google Scholar 

  • Schneider, S.: Luftbild und Luftbildinterpretation. Walter de Gruyter, Berlin, p. 519 (1974)

    Google Scholar 

  • Forkuo, E.K.: Digital terrain modeling in a GIS environment. ISPRS Archives 37(B2), 1023–1030 (2008)

    Google Scholar 

  • Mulder, V.L., de Bruin, S., Schaepman, M.E., Mayr, T.R.: The use of remote sensing in soil and terrain mapping – A review. Geoderma 162, 1–19 (2011)

    Article  Google Scholar 

  • Castaldi, F., Chabrillat, S., Jones, A., Vreys, K., Bomans, B., Van Wesemael, B.: Soil organic carbon estimation in croplands by hyperspectral remote APEX data using the LUCAS topsoil database. Remote Sens. 10(2), 153 (2018)

    Article  Google Scholar 

  • Chabrillat, S., Eisele, A., Guillaso, S., Rogaß, C., Ben-Dor, E., Kaufmann, H.: HYSOMA: An easy-to-use software interface for soil mapping applications of hyperspectral imagery. In: Proc. EARSeL SIG Imaging Spectrosc. Workshop, p. 7. (2011)

    Google Scholar 

  • van der Meer, F.D., van der Werff, H.M.A., van Ruitenbeek, F.J.A.: Potential of ESA’s Sentinel-2 for geological applications. Remote Sens. Environ. 148, 124–133 (2014)

    Article  Google Scholar 

  • Mielke, C., Boesche, N.K., Rogass, C., Segl, K., Gauert, C., Kaufmann, H.: Potential applications of the Sentinel-2 multispectral sensor and the EnMAP hyperspectral sensor in mineral exploration. EARSeL EProceedings 13(2), 93–102 (2014)

    Google Scholar 

  • Driscoll, R.S.: Color Aerial Photography – A New View for Range Management (1971) USDA Forest Service Research Paper RM-67

    Book  Google Scholar 

  • Cernansky, R.: Biodiversity moves beyond counting species. Nature 546, 22–24 (2017)

    Article  Google Scholar 

  • Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J.M., Heurich, M., Jung, A., Klenke, R., Neumann, C., Pause, M., Rocchini, D., Schaepman, M.E., Schmidtlein, S., Schulz, K., Selsam, P., Settele, J., Skidmore, A.K., Cord, A.F.: Linking Earth observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecol. Indic. 70, 317–339 (2016)

    Article  Google Scholar 

  • Lausch, A., Erasmi, S., King, D., Magdon, P., Heurich, M.: Understanding forest health with remote sensing – Part II: A review of approaches and data models. Remote Sens. 9, 129 (2017)

    Article  Google Scholar 

  • Lausch, A., Erasmi, S., King, D.J., Magdon, P., Heurich, M.: Understanding forest health with remote sensing – Part I: A review of spectral traits, processes and remote-sensing characteristics. Remote Sens. 8(12), 1029 (2016)

    Article  Google Scholar 

  • Lausch, A., Bastian, O., Klotz, S., Leitão, P.J., Jung, A., Rocchini, D., Schaepman, M.E., Skidmore, A.K., Tischendorf, L., Knapp, S.: Understanding and assessing vegetation health by in-situ species and remote sensing approaches. Methods Ecol. Evol. 9(8), 1799–1809 (2018)

    Article  Google Scholar 

  • Asner, G.P., Martin, R.E.: Airborne spectranomics: Mapping canopy chemical and taxonomic diversity in tropical forests. Front. Ecol. Environ. 7, 269–276 (2009)

    Article  Google Scholar 

  • Asner, G.P., Martin, R.E.: Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Glob. Ecol. Conserv. 8, 212–219 (2016)

    Article  Google Scholar 

  • Schweiger, A.K., Cavender-Bares, J., Townsend, P.A., Hobbie, S.E., Madritch, M.D., Wang, R., Tilman, D., Gamon, J.A.: Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evol. 2, 976–982 (2018)

    Article  Google Scholar 

  • McManus, K.M., Asner, G.P., Martin, R.E., Dexter, K.G., Kress, W.J., Field, C.B.: Phylogenetic structure of foliar spectral traits in tropical forest canopies. Remote Sens. 8, 1–16 (2016)

    Article  Google Scholar 

  • Richter, R., Reu, B., Wirth, C., Doktor, D.: The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area. Int. J. Appl. Earth Obs. Geoinf. 52, 464–474 (2016)

    Google Scholar 

  • Tanase, M.A., Kennedy, R., Aponte, C.: Radar burn ratio for fire severity estimation at canopy level: An example for temperate forests. Remote Sens. Environ. 170, 14–31 (2015)

    Article  Google Scholar 

  • Heurich, M., Thoma, F.: Estimation of forestry stand parameters using laser scanning data in temperate, structurally rich natural European beech (Fagus sylvatica) and Norway spruce (Picea abies) forests. Forestry 81, 645–661 (2008)

    Article  Google Scholar 

  • Schneider, F.D., Morsdorf, F., Schmid, B., Petchey, O.L., Hueni, A., Schimel, D.S., Schaepman, M.E.: Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017)

    Article  Google Scholar 

  • Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., Paasche, H., Paulheim, H., Pause, M., Schweitzer, C., Schmullius, C., Settele, J., Skidmore, A., Wegmann, M., Zacharias, S., Kirsten, T., Schaepman, M.: Understanding forest health with remote sensing – Part III: Requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sens. 10, 1120 (2018)

    Article  Google Scholar 

  • Bardgett, R.D., Wardle, D.A.: Aboveground-belowground linkages: Biotic interactions, ecosystem processes, and global change. Oxford Univ. Press, Oxford (2010)

    Google Scholar 

  • Roser, M., Ritchie, H.: Yields and Land Use in Agriculture (2018) https://ourworldindata.org/yields-and-land-use-in-agriculture, Accessed 28 Nov 2018

  • Wulder, M.A., Coops, N.C.: Make Earth observations open access. Nature 513, 30–31 (2014)

    Article  Google Scholar 

  • Griffiths, P., Nendel, C., Hostert, P.: Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping. Remote Sens. Environ. 220, 135–151 (2019)

    Article  Google Scholar 

  • Blume, H.-P., Brümmer, G.W., Horn, R., Kandeler, E., Kögel-Knabner, I., Kretschmar, R., Stahr, K., Wilke, B.-M.: Lehrbuch der Bodenkunde. Springer, Berlin, Heidelberg, p. 569 (2016)

    Google Scholar 

  • Buringh, P.: The applications of aerial photographs in soil surveys. In: Manual of Photographic Interpretation, pp. 633–666. ASPRS, Washington (1960)

    Google Scholar 

  • Wloczyk, C., Borg, E., Richter, R., Miegel, K.: Estimation of instantaneous air temperature above vegetation and soil surfaces from Landsat 7 ETM+ data in Northern Germany. Int. J. Remote Sens. 32(24), 9119–9136 (2011)

    Article  Google Scholar 

  • Ben-Dor, E., Inbar, Y., Chen, Y.: The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sens. Environ. 61(1), 1–15 (1997)

    Article  Google Scholar 

  • Gerighausen, H., Menz, G., Kaufmann, H.: Spatially explicit estimation of clay and organic carbon content in agricultural soils using multi-annual imaging spectroscopy data. Appl. Environ. Soil Sci. (2012) https://doi.org/10.1155/2012/868090

    Article  Google Scholar 

  • Mulder, V.L., de Bruin, S., Weyermann, J., Kokaly, R.F., Schaepman, M.E.: Characterizing regional soil mineral composition using spectroscopy and geostatistics. Remote Sens. Environ. 139, 415–429 (2013)

    Article  Google Scholar 

  • Wulf, H., Mulder, V.L., Schaepman, M.E., Keller, A., Jörg, A., Claudio, P.: Remote Sensing of Soils. Univ. Zurich, Zurich (2014) https://doi.org/10.13140/2.1.1098.0649. Technical Report

    Book  Google Scholar 

  • Franke, J., Keuck, V., Siegert, F.: Assessment of grassland use intensity by remote sensing to support conservation schemes. J. Nat. Conserv. 20, 125–134 (2012)

    Article  Google Scholar 

  • Kuemmerle, T., Erb, K., Meyfroidt, P., Müller, D., Verburg, P.H., Estel, S., Haberl, H., Hostert, P., Jepsen, M.R., Kastner, T., Levers, C., Lindner, M., Plutzar, C., Verkerk, P.J., van der Zanden, E.H., Reenberg, A.: Challenges and opportunities in mapping land use intensity globally. Curr. Opin. Environ. Sustain. 5(5), 484–493 (2013)

    Article  Google Scholar 

  • Graesser, J., Aide, T.M., Grau, H.R., Ramankutty, N.: Cropland/pastureland dynamics and the slowdown of deforestation in Latin America. Environ. Res. Lett. 10(3), 34017 (2015)

    Article  Google Scholar 

  • Wachendorf, M., Fricke, T., Möckel, T.: Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands. Grass Forage Sci. 73(1), 1–14 (2018)

    Article  Google Scholar 

  • Mahlein, A.-K.: Plant disease detection by imaging sensors – Parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241–251 (2016)

    Article  Google Scholar 

  • Bill, R.: Interoperable GIS-Infrastruktur im landwirtschaftlichen Betrieb. In: Heineke, H.J. (ed.) Geographische Informationssysteme in der Landwirtschaft und im ländlichen Raum: Defizite und Entwicklungspotentiale KTBL-Schriften, vol. 428, pp. 69–74. KTBL-Schriften-Vertrieb, Münster (2004)

    Google Scholar 

  • Thenkabail, P.S., Enclona, E.A., Ashton, M.S., Van Der Meer, B.: Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens. Environ. 91, 354–376 (2004)

    Article  Google Scholar 

  • Ríos-Casanova, L., Valiente-Banuet, A., Rico-Gray, V.: Ant diversity and its relationship with vegetation and soil factors in an alluvial fan of the Tehuacán Valley, Mexico. Acta Oecol. 29, 316–323 (2006)

    Article  Google Scholar 

  • Orwin, K.H., Buckland, S.M., Johnson, D., Turner, B.L., Smart, S., Oakley, S., Bardgett, R.D.: Linkages of plant traits to soil properties and the functioning of temperate grassland. J. Ecol. 98, 1074–1083 (2010)

    Article  Google Scholar 

  • Mueller, K.E., Hobbie, S.E., Chorover, J., Reich, P.B., Hale, C.M., Jagodzin, A.M.: Effects of litter traits, soil biota, and soil chemistry on soil carbon stocks at a common garden with 14 tree species. Biogeochemistry 123, 313–327 (2015)

    Article  Google Scholar 

  • Krishnan, P., Kochendorfer, J., Dumas, E.J., Guillevic, P.C., Baker, C.B., Meyers, T.P., Martos, B.: Comparison of in-situ, aircraft, and satellite land surface temperature measurements over a NOAA Climate Reference Network site. Remote Sens. Environ. 165, 249–264 (2015)

    Article  Google Scholar 

  • de Vries, F.T., Manning, P., Tallowin, J.R.B., Mortimer, S.R., Pilgrim, E.S., Harrison, K.A., Hobbs, P.J., Quirk, H., Shipley, B., Cornelissen, J.H.C., Kattge, J., Bardgett, R.D.: Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. Ecol. Lett. 15(11), 1230–1239 (2012)

    Article  Google Scholar 

  • Atkinson, N.J., Urwin, P.E.: The interaction of plant biotic and abiotic stresses: From genes to the field. J. Exp. Bot. 63(10), 3523–3543 (2012)

    Article  Google Scholar 

  • Hall, K., Lamont, N.: Zoogeomorphology in the Alpine: Some observations on abiotic–biotic interactions. Geomorphology 55, 219–234 (2003)

    Article  Google Scholar 

  • Sala, O.E., Chapin, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L.F., Jackson, R.B., Kinzig, A., Leemans, R., Lodge, D.M., Harold, A., Mooney, M.O., Poff, N.L., Sykes, M.T., Walker, B.H., Walker, M., Wall, D.H.: Biodiversity: Global biodiversity scenarios for the year 2100. Sci. New Ser. 287(5459), 1770–1775 (2005)

    Google Scholar 

  • Beier, P., Brost, B.: Uso de elementos territoriales para planificar para el cambio climático: Conservando las arenas, no los actors. Conserv. Biol. 24, 701–710 (2010)

    Article  Google Scholar 

  • Pressey, R.L., Cabeza, M., Watts, M.E., Cowling, R.M., Wilson, K.A.: Conservation planning in a changing world. Trends Ecol. Evol. 22(11), 583–592 (2007)

    Article  Google Scholar 

  • Dörnhöfer, K., Oppelt, N.: Remote sensing for lake research and monitoring – Recent advances. Ecol. Indic. 64, 105–122 (2016)

    Article  Google Scholar 

  • Wulder, M.A., White, J.C., Loveland, T.R., Woodcock, C.E., Belward, A.S., Cohen, W.B., Fosnight, E.A., Shaw, J., Masek, J.G., Roy, D.P.: The global Landsat archive: Status, consolidation, and direction. Remote Sens. Environ. 185, 271–283 (2016)

    Article  Google Scholar 

  • Braun, G.: Vegetationsgeographische Untersuchungen im NW-Karakorum (Pakistan): Kartierung der aktuellen Vegetation und Rekonstruktion der potentiellen Waldverbreitung auf der Basis von Satellitendaten, Gelände- und Einstrahlungsmodellen. Bonner geographische Abhandlungen, vol. 7643. Dümmler, Bonn, p. 156 (1996)

    Google Scholar 

  • Jones, T.G., Coops, N.C., Sharma, T.: Assessing the utility of airborne hyperspectral and LIDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sens. Environ. 114, 2841–2852 (2010)

    Article  Google Scholar 

  • Sollins, P.: Factors influencing species composition in tropical lowland rain forest: Does soil matter? Ecology 79, 23–30 (1998)

    Article  Google Scholar 

  • Rowan, L.C., Mars, J.C.: Lithologic mapping in the Mountain Pass, California area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data. Remote Sens. Environ. 84, 350–366 (2003)

    Article  Google Scholar 

  • Gholizadeh, H., Gamon, J.A., Zygielbaum, A.I., Wang, R., Schweiger, A.K., Cavender-Bares, J.: Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of \({\upalpha}\)-diversity (species richness) in prairie ecosystems. Remote Sens. Environ. 206, 240–253 (2018)

    Article  Google Scholar 

  • Anderson, K., Croft, H.: Remote sensing of soil surface properties. Prog. Phys. Geogr. 33(4), 457–473 (2009)

    Article  Google Scholar 

  • McColl, K.A., Alemohammad, S.H., Akbar, R., Konings, A.G., Yueh, S., Entekhabi, D.: The global distribution and dynamics of surface soil moisture. Nat. Geosci. 10, 100–104 (2017)

    Article  Google Scholar 

  • Vereecken, H., Schnepf, A., Hopmans, J.W., Javaux, M., Or, D., Roose, T., Vanderborght, J., Young, M.H., Amelung, W., Aitkenhead, M., Allison, S.D., Assouline, S., Baveye, P., Berli, M., Brüggemann, N., Finke, P., Flury, M., Gaiser, T., Govers, G., Ghezzehei, T., Hallett, P., Hendricks Franssen, H.J., Heppell, J., Horn, R., Huisman, J.A., Jacques, D., Jonard, F., Kollet, S., Lafolie, F., Lamorski, K., Leitner, D., McBratney, A., Minasny, B., Montzka, C., Nowak, W., Pachepsky, Y., Padarian, J., Romano, N., Roth, K., Rothfuss, Y., Rowe, E.C.C., Schwen, A., Šimůnek, J., Tiktak, A., Van Dam, J., van der Zee, S.E.A.T.M., Vogel, H.J., Vrugt, J.A., Wöhling, T., Young, I.M.: Modeling soil processes: Review, key challenges, and new perspectives. Vadose Zone J. 15(5), 57 (2016)

    Article  Google Scholar 

  • Vereecken, H., Huisman, J.A., Bogena, H., Vanderborght, J., Vrugt, J.A., Hopmans, J.W.: On the value of soil moisture measurements in vadose zone hydrology: A review. Water Resour. Res. 44(4), W00D06 (2008)

    Article  Google Scholar 

  • Vogel, H.J., Bartke, S., Daedlow, K., Helming, K., Kögel-Knabner, I., Lang, B., Rabot, E., Russell, D., Stößel, B., Weller, U., Wiesmeier, M., Wollschläger, U.: A systemic approach for modeling soil functions. Soil 4, 83–92 (2018)

    Article  Google Scholar 

  • Rabot, E., Wiesmeier, M., Schlüter, S., Vogel, H.J.: Soil structure as an indicator of soil functions: A review. Geoderma 134, 122–137 (2018)

    Article  Google Scholar 

  • Wessolek, G.: Bodenwasserhaushalt. In: Fohrer, N., Bormann, H., Miegel, K., Casper, M., Bronstert, A., Schumann, A., Weiler, M. (eds.) Hydrologie, pp. 69–90. Haupt, Bern (2016)

    Google Scholar 

  • Ulaby, F.T., Dubois, P.C., van Zyl, J.: Radar mapping of surface soil moisture. J. Hydrol. 184(1/2), 57–84 (1996)

    Article  Google Scholar 

  • Moreira, A., Prats, P., Younis, M., Krieger, G., Hajnsek, I., Papathanassiou, K.A.: Tutorial on synthetic aperture radar. IEEE Geosci. Remote Sens. Mag. 1(1), 6–43 (2013)

    Article  Google Scholar 

  • Jagdhuber, T., Member, S., Hajnsek, I., Member, S., Bronstert, A., Papathanassiou, K.P., Member, S.: Soil moisture estimation under low vegetation cover using a multi-angular polarimetric decomposition. IEEE Trans. Geosci. Remote Sens. 51(4), 2201–2215 (2013)

    Article  Google Scholar 

  • Jagdhuber, T.: Soil Parameter Retrieval under Vegetation Cover Using SAR Polarimetry. Dissertation. Univ. Potsdam, Potsdam, p. 270 (2012)

    Google Scholar 

  • Bruckler, L., Witono, H., Stengel, P.: Near surface soil moisture estimation from microwave measurements. Remote Sens. Environ. 26(2), 101–121 (1988)

    Article  Google Scholar 

  • Zribi, M., Le Hégarat-Mascle, S., Ottlé, C., Kammoun, B., Guerin, C.: Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS wind scatterometer and SAR data. Remote Sens. Environ. 86(1), 30–41 (2003)

    Article  Google Scholar 

  • Pasolli, L., Notarnicola, C., Bertoldi, G., Bruzzone, L., Remelgado, R., Greifeneder, F., Niedrist, G., Della Chiesa, S., Tappeiner, U., Zebisch, M.: Estimation of soil moisture in mountain areas using SVR technique applied to multiscale active radar images at C-band. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(1), 262–283 (2015)

    Article  Google Scholar 

  • Dobson, M.C., Ulaby, F.T.: Preliminary evaluation of the SIRB response to soil moisture, surface roughness, and crop canopy cover. IEEE Trans. Geosci. Remote Sens. 24, 517–526 (1986)

    Article  Google Scholar 

  • Dubois, P.C., Engman, T.: Measuring soil moisture with imaging radars. IEEE Trans. Geosci. Remote Sens. 33, 915–926 (1995)

    Article  Google Scholar 

  • Bindlish, R., Jackson, T.J., van der Velde, R.: High resolution soil moisture mapping using AIRSAR observations during SMEX02. In: IEEE Int. Conf. Geosci. Remote Sens. Symp. pp. 2324–2327. (2006) https://doi.org/10.1109/IGARSS.2006.601

    Chapter  Google Scholar 

  • Tsang, L., Kong, J.A., Ding, K.-H.: Scattering of Electromagnetic Waves. Theories and Applications. Wiley, New York, p. 445 (2000)

    Book  Google Scholar 

  • Elfouhaily, T. M., Guerin, C.-A.: A Critical Survey of Approximate Scattering Theories from Random Rough Surfaces. Wave Random Complex Media, 14(4), 1–40 (2004) https://doi.org/10.1088/0959-7174/14/4/R01

  • Morrison, K., Bennett, J.C., Nolan, M., Menon, R.: Laboratory measurement of the DInSAR response to spatiotemporal variations in soil moisture. IEEE Trans. Geosci. Remote Sens. 49(10), 3815–3823 (2011)

    Article  Google Scholar 

  • de Zan, F., Parizzi, A., Prats-Iraola, P., López-Dekker, P.: A SAR interferometric model for soil moisture. IEEE Trans. Geosci. Remote Sens. 52(1), 418–425 (2014)

    Article  Google Scholar 

  • Zwieback, S., Hensley, S., Hajnsek, I.: Assessment of soil moisture effects on L-band radar interferometry. Remote Sens. Environ. 164, 77–89 (2015)

    Article  Google Scholar 

  • Kerr, Y.H., Waldteufel, P., Wigneron, J.P., Martinuzzi, J.M., Font, J., Berger, M.: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens. 39(8), 1729–1735 (2001)

    Article  Google Scholar 

  • Wigneron, J.-P.: Soil moisture retrieval algorithms in the framework of the SMOS mission: Current status and requirements for the EuroSTARRS Campaign. In: Fletcher, P. (ed.) Proc. First Results Workshop Eurostarrs, Wise, Losac Campaigns (2002), pp. 199–202. ESA Pub Division, Noordwijk (2003)

    Google Scholar 

  • Huo, A., Zhang, J., Cheng, Y., Yi, X., Qiao, L., Du Su, F., Mao, Y.: Assessing the effect of scaling methods on retrieval of soil moisture based on MODIS images in arid regions. Toxicol. Environ. Chem. 98(3/4), 410–418 (2016)

    Article  Google Scholar 

  • Li, B., Ti, C., Zhao, Y., Yan, X.: Estimating soil moisture with Landsat data and its application in extracting the spatial distribution of winter flooded paddies. Remote Sens. 8(1), 38 (2016)

    Article  Google Scholar 

  • Haubrock, S.-N., Chabrillat, S., Kuhnert, M., Hostert, P., Kaufmann, H.: Surface soil moisture quantification and validation based on hyperspectral data and field measurements. J. Appl. Remote Sens. 2(1), 23552 (2008)

    Article  Google Scholar 

  • Lausch, A., Zacharias, S., Dierke, C., Pause, M., Kühn, I., Doktor, D., Dietrich, P., Werban, U.: Analysis of vegetation and soil patterns using hyperspectral remote sensing, EMI, and gamma-ray measurements. Vadose Zone J. 12, 15 (2013)

    Article  Google Scholar 

  • Townshend, J.R., Justice, C.O., Skole, D., Malingreau, J.P., Cihlar, J., Teillet, P., Sadowski, F., Ruttenberg, S.: The 1 km resolution global data set: Needs of the International Geosphere Biosphere Programme. Int. J. Remote Sens. 15, 3417–3441 (1994)

    Article  Google Scholar 

  • Kustas, W.P., French, A.N., Hatfield, J.L., Jackson, T.J., Moran, M.S., Rango, A., Ritchie, J.C., Schmugge, T.J., Moran, M.S., Rango, A., Ritchie, J.C., Schmugge, T.J.: Remote sensing research in hydrometeorology. Photogramm. Eng. Remote Sens. 69(6), 631–646 (2003)

    Article  Google Scholar 

  • Karnieli, A., Agam, N., Pinker, R.T., Anderson, M., Imhoff, M.L., Gutman, G.G., Panov, N., Goldberg, A.: Use of NDVI and land surface temperature for drought assessment: Merits and limitations. J. Clim. 23, 618–633 (2010)

    Article  Google Scholar 

  • Müller, B., Bernhardt, M., Schulz, K.: Identification of catchment functional units by time series of thermal remote sensing images. Hydrol. Earth Syst. Sci. 18(12), 5345–5359 (2014)

    Article  Google Scholar 

  • Krajewski, W.F., Anderson, M.C., Eichinger, W.E., Entekhabi, D., Hornbuckle, B.K., Houser, P.R., Katul, G.G., Kustas, W.P., Norman, J.M., Peters-Lidard, C., Wood, E.F.: A remote sensing observatory for hydrologic sciences: A genesis for scaling to continental hydrology. Water Resour. Res. 42(7), 1–13 (2006)

    Article  Google Scholar 

  • Li, Z.-L., Becker, F.: Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sens. Environ. 43, 67–85 (1993)

    Article  Google Scholar 

  • Esch, T., Marconcini, M., Felbier, A., Roth, A., Heldens, W., Huber, M., Schwinger, M., Taubenböck, H., Müller, A., Dech, S.: Urban footprint processor – Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission. IEEE Geosci. Remote Sens. Lett. 10(6), 1617–1621 (2013)

    Article  Google Scholar 

  • Felbier, A., Esch, T., Heldens, W., Marconcini, M., Zeidler, J., Roth, A., Klotz, M., Wurm, M., Taubenböck, H.: The global urban footprint – Processing status and cross comparison to existing human settlement products. In: IEEE Int. Geosci. Remote Sens. Symp. pp. 4816–4819. (2014)

    Google Scholar 

  • Parcak, S., Gathings, D., Childs, C., Mumford, G., Cline, E.: Satellite evidence of archaeological site looting in Egypt: 2002–2013. Antiquity 90, 188–205 (2016)

    Article  Google Scholar 

  • Miller, W.C.: Uses of aerial photographs in archaeological field work. Am. Antiq. 23(1), 46–62 (1957)

    Article  Google Scholar 

  • Weber, S.A., Yool, S.R.: Detection of subsurface archaeological architecture by computer assisted airphoto interpretation. Geoarchaeology 14(6), 481–493 (1999)

    Article  Google Scholar 

  • Netzband, M., Meinel, G., Lippold, R.: Classification of settlement structures using morphological and spectral features in fused high resolution satellite images (IRS-1C) Int. Arch. Photogramm. Remote Sens. 32(7-4-3 W6), 160–166 (1999)

    Google Scholar 

  • Ricchetti, E.: Application of optical high resolution satellite data for archaeological prospection over Hierapolis (Turkey) IEEE Trans. Geosci. Remote Sens. 62, 3898–3901 (2004)

    Google Scholar 

  • Golden, C., Murtha, T., Cook, B., Shaffer, D.S., Schroder, W., Hermitt, E.J., Firpi, O.A., Scherer, A.K.: Reanalyzing environmental LIDAR data for archaeology: Mesoamerican applications and implications. J. Archaeol. Sci. Rep. 9, 293–308 (2016)

    Google Scholar 

  • Hutson, S.R.: Adapting LIDAR data for regional variation in the Tropics: A case study from the Northern Maya Lowlands. J. Archaeol. Sci. Rep. 4, 252–263 (2015)

    Google Scholar 

  • von Schwerin, J., Richards-Rissetto, H., Remondino, F., Grazia Spera, M., Auer, M., Billen, N., Loos, L., Stelson, L., Reindel, M.: Airborne LIDAR acquisition, post-processing and accuracy-checking for a 3D WebGIS of Copan, Honduras. J. Archaeol. Sci. Rep. 5, 85–104 (2016)

    Google Scholar 

  • Parcak, S.H.: Satellite Remote Sensing for Archaeology. Routledge, New York, p. 292 (2009)

    Book  Google Scholar 

  • Parcak, S.H.: GIS, Remote Sensing, and Landscape Archaeology. Oxford Univ. Press, Oxford (2017)

    Google Scholar 

  • IFRC: What is a disaster? (2019) https://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/what-is-a-disaster/, Accessed 19 Feb 2019

  • United Nations: UNISDR Terminology on Disaster Risk Reduction. UNISDR, Geneva (2009) https://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf

    Google Scholar 

  • WHO: Disasters & Emergencies – Definitions: Training Package (2002) http://apps.who.int/disasters/repo/7656.pdf

  • Benzi, E., Shurmer, I., Policella, N., Troendle, D., Lutzer, M., Kuhlmann, S., James, M.: Optical inter-satellite communication: The Alphasat and Sentinel-1A in-orbit experience. In: AIAA SpaceOps Conf. p. 13. (2016) https://doi.org/10.2514/6.2016-2389

    Chapter  Google Scholar 

  • Berk, A., Anderson, G.P., Bernstein, L.S., Acharya, P.K., Dothe, H., Matthew, M.W., Adler-Golden, S.M., Chetwynd Jr., J.H., Richtsmeier, S.C., Pukall, B., Allred, C.L., Jeong, L.S., Hoke, M.L.: MODTRAN4 radiative transfer modeling for atmospheric correction. Proc. SPIE (1999) https://doi.org/10.1117/12.366388

    Article  Google Scholar 

  • Berk, A., Anderson, G.P., Acharya, P.K., Chetwynd Jr., J.H., Bernstein, L.S., Dothe, H., Matthew, M.W., Adler-Golden, S.M., Richtsmeier, S.C., Pukall, B., Allred, C.L., Jeong, L.S., Hoke, M.L.: MODTRAN4 User’s Manual. Air Force Research Laboratory, Hanscom AFB (1999) ftp://ftp.pmodwrc.ch/pub/Vorlesung%20K+S/MOD4_user_guide.pdf (last access: 10 February 2019)

    Google Scholar 

  • Braun, D., Damm, A., Paul-Limoges, E., Revill, A., Buchmann, N., Petchey, O.L., Hein, L., Schaepman, M.E.: From instantaneous to continuous: Using imaging spectroscopy and in situ data to map two productivity-related ecosystem services. Ecol. Indic. 82, 409–419 (2017) doi:https://doi.org/10.1016/j.ecolind.2017.06.045

    Article  Google Scholar 

  • Henderson, F.M., Lewis, A.J.: Principles and applications of imaging radar. In: Ryerson, R.A. (ed.) Manual of Remote Sensing, vol. 2, p. 896. Wiley, New York (1983)

    Google Scholar 

  • Jagdhuber, T., Hajnsek, I., Papathanassiou, K.P.: An iterative generalized hybrid decomposition for soil moisture retrieval under vegetation cover using fully polarimetric SAR. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(8), 3911–3922 (2015)

    Article  Google Scholar 

  • Pause, M., Schweitzer, C., Rosenthal, M., Keuck, V., Bumberger, J., Dietrich, P., Heurich, M., Jung, A., Lausch, A.: In Situ/Remote Sensing Integration to Assess Forest Health – A Review. Remote Sens. 8, 471 (2016) doi:https://doi.org/10.3390/rs8060471

    Article  Google Scholar 

  • ISO/TS 19159-1:2014: Geographic Information – Calibration and Validation of Remote Sensing Imagery Sensors and Data. ISO, Geneva (2014)

    Google Scholar 

  • ISO 19115-2:2019: Geographic Information – Metadata – Part 2: Extension for Acquisition and Processing. ISO, Geneva (2019)

    Google Scholar 

  • Glossary. https://podaac.jpl.nasa.gov/Glossary

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Authors and Affiliations

  1. German Remote Sensing Data Center, German Aerospace Center (DLR), Neustrelitz, Germany

    Erik Borg

  2. Institute of Geography, Earth Observation, Friedrich Schiller University Jena, Jena, Germany

    Sina C. Truckenbrodt

  3. Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

    Angela Lausch

  4. Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany

    Peter Dietrich

  5. Remote Sensing Technology Institute, German Aerospace Center (DLR), Neustrelitz, Germany

    Karsten Schmidt

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  1. Erik Borg
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  2. Sina C. Truckenbrodt
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  3. Angela Lausch
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  4. Peter Dietrich
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  5. Karsten Schmidt
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Corresponding author

Correspondence to Erik Borg .

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Editors and Affiliations

  1. University of Applied Sciences Neubrandenburg, Neubrandenburg, Germany

    Wolfgang Kresse

  2. Esri, Vienna, VA, USA

    David Danko

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Borg, E., Truckenbrodt, S.C., Lausch, A., Dietrich, P., Schmidt, K. (2022). Remote Sensing. In: Kresse, W., Danko, D. (eds) Springer Handbook of Geographic Information. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-53125-6_10

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