연구동향 분석
박사

다양한 스펙트럼에서의 산란 기반 원격탐사기법 연구 및 응용 = Research and application technique on scattering based remote sensing in a variety of spectral bands

권영주 2019년

논문상세정보
인용/피인용
' 다양한 스펙트럼에서의 산란 기반 원격탐사기법 연구 및 응용 = Research and application technique on scattering based remote sensing in a variety of spectral bands' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • 강수
  • 블랙아이스
  • 산란
  • 원격탐사
  • 토양 수분
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
273 0

0.0%

' 다양한 스펙트럼에서의 산란 기반 원격탐사기법 연구 및 응용 = Research and application technique on scattering based remote sensing in a variety of spectral bands' 의 참고문헌

  • eoPotal Directory, 2015: TRMM (Tropical Rainfall Measuring Mission). [Available online at https://directory.eoportal.org/web/eoportal/satellitemissions/t/trmm.]
  • Zavorotny, V. U. and Voronovich, A. G., 2000: Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Transactions on Geoscience and Remote Sensing, 38 (2), 951-964.
  • Yuter, S. E., and R. A. Houze Jr, 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity and differential reflectivity. Mon. Wea. Rev., 123, 1941-1963.
  • Wu, S. T. and Fung, A. K., 1972: Noncoherent Model for Microwave Emissions and Backscattering from Sea-Surface. Journal of Geophysical Research, 77 (30), 5917
  • Wu, R., and Weinman, J. A., 1984: Microwave radiances from precipitating clouds containing aspherical ice, combined phase, and liquid hydrometeors. J. Geophys. Res., 89, 7170-7178.
  • Wilheit, T.T., Chang, A.T.C., V. Rao, M.S., Rodgers, E.B. and Theon, J.S., 1977: A satellite technique for quantitatively mapping rainfall rates over the oceans. Journal of applied meteorology, 16(5), pp.551-560.
  • Wilheit, T., Kummerow, C. D. and Ferraro, R., 2003: Rainfall algorithms for AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 204-214.
  • Wilheit, T. T., Chang, A. T. C. and Chiu, L. S., 1991: Retrieval of monthly rainfall indices from microwave radiometric measurements using probability distribution functions. J. Atmos. Oceanic Technol., 8, 118136.
  • Weng, F., N. C. Grody, R. Ferraro, A. Basist, and D. Forsyth, 1997: Cloud liquid water climatology from the Special Sensor Microwave/Imager. J. climate, 10,1086-1098.
  • Wang, C., Qi, S., Niu, Z., and Wang, J., 2004: Evaluating soil moisture status in China using the temperature–vegetation dryness index (TVDI). Canadian Journal of Remote Sensing, 30(5), 671-679.
  • Viikari, V., Varpula, T. and Kantanen, M., 2008: Automotive radar technology for detecting road conditions. Backscattering properties of dry, wet, and icy asphalt,” in Proceedings of the 5th European Radar Conference, IEEE Computer Society (Amsterdam, The Netherlands, EuRAD '08), pp. 276–279
  • Vicente, G.A., Scofield, R.A. and Menzel, W.P., 1998: The operational GOES infra-red estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-98.
  • Ulaby, F., Moore, R. and Fung, A., 1982: Microwave remote sensing: Active and passive. Volume 2-Radar remote sensing and surface scattering and emission theory.
  • Ulaby, F. T., Moore, R. K. and Fung, A. K., 1986: Microwave Remote Sensing, Active and Passive, Volume I, Microwave Remote Sensing Fundamentals and Radiometry. Reading, MA: Addison-Wesley.
  • Tsai, M.-D., Chen, W.-J., and Wang, J.-L., 2010: Implementing Polarization-Corrected Temperature into Typhoon Rainfall Estimates over Taiwan from TRMM/TMI Data. Terr. Atmos. Oceanic Sci., 21, 697-712.
  • Todd, M. C. and Bailey, J. O., 1995: Estimates of rainfall over the United Kingdom and surrounding seas from the SSM/I using the polarization corrected temperature algorithm. J. Appl. Meteorol., 34, 1254-1265.
  • Stoner, E. R., and Baumgardner, M. F., 1981: Characteristic variations in reflectance of surface soils. Soil Science Society of America Journal, 45, 1161-1165.
  • Stogryn, A., 1997: Equations for Calculating Dielectric Constant of Saline Water. Ieee Transactions on Microwave Theory and Techniques, vol. Mt19 (8), 733.
  • Spencer, R. W., Goodman, H. M., and Hood, R. E., 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal, Journal of atmospheric and oceanic technology, 6(2), 254
  • Spencer, R. W., Goodman, H. M. and Hood, R. E., 1989: Precipitation retrieval over land and ocean with the SSM/I: identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254-273.
  • Spencer, R. W., 1986: A satellite passive 37-GHz scattering-based method for measuring oceanic rain rates. J. Climate Appl, Meteor., 25, 754-766.
  • Shenk, W. E., Holub, R. J. and Neff, R. A., 1976: A multispectral cloud type identification method using Nimbus-3 MRIR measurements. Mon. Wea. Rev., 104, 284-291.
  • Sellers, P. J., Hall, F. G., Asrar, G., Strebel, D. E. and Murphy, R. E., 1988: The first ISLSCP Field Experiment (FIFE), Bull. Am. Meteorol. Soc., 69, 22–27, doi:10.1175/1520-0477(1988)069-2.0.CO;2.
  • Sandholt, I., Rasmussen, K., and Andersen, J., 2002: A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of environment, 79, 213-224.
  • Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., and Harlan, J. C., 1974: Monitoring the vernal advancement of retrogradation of natural vegetation (p. 371). Greenbelt, MD: NASA/GSFC (Type III, Final Report).
  • Rossow, W. B., and Schiffer, R. A., 1991: ISCCP cloud data products. Bull. Amer. Meteor. Soc., 72, 2-20.
  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., and Sorooshian, S., 1994: A modified soil adjusted vegetation index. Remote sensing of environment, 48, 119-126.
  • Priestley, C. H. B., and Taylor, R. J., 1972: On the assessment of surface heat flux and evaporation using large‐scale parameters, Mon. Weather Rev., 100, 81–92, doi:10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.
  • Price, J. C., 1990: Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28, 940-948.
  • Price, J. C., 1977: Thermal inertia mapping: A new view of the earth. Journal of Geophysical Research 82(18):2582–2590.
  • Piuzzi, E., Cannazza, G., Cataldo, A., Chicarella, S., De Benedetto, E., Frezza, F., Pisa, S., Prontera, S. and Timpani, F., 2016: Measurement System for Evaluating Dielectric Permittivity of Granular Materials in the 1.7-2.6-GHz Band. Ieee Transactions on Instrumentation and Measurement, 65 (5), 1051-1059.
  • Petty, Grant W. A First Course in Atmospheric Radiation, 2nd edition, Sundog Publishing, Madison, WI, 2006: Permission being sought
  • Petty, G. W., and Krajewski, W. F., 1996: Satellite estimation of precipitation over land. Hydro. Sci. J., 41, 433-451.
  • Petty, G. W., 1995: The status of satellite-based rainfall estimation over land. Remote Sens. Environ., 51, 125-137.
  • Petropoulos, G., Carlson, T. N., Wooster, M. J., and Islam, S., 2009: A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Progress in Physical Geography, 33, 224-250.
  • Parkinson, B. and Spilker, J., 1996: Global Positioning System: Theory and Applications, vol. 1; Progress in Astronautics and Aeronatics, vol. 163, American Institute of Aeronautics and Astronautics. Inc.
  • Olson, W.S., 1989. Physical retrieval of rainfall rates over the ocean by multispectral microwave radiometry: Application to tropical cyclones. Journal of Geophysical Research: Atmospheres, 94(D2), pp.2267-2280.
  • Oki, T., Imaoka, K. and Kachi, M., 2010: Concepts and applications of GCOM-W1 and W2. Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., 38, 86-90.
  • Oh, Y., 2004: Quantitative retrieval of soil moisture content and surface roughness from multi-polarized radar observations of bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 42, 596-601.
  • Njoku, E. G. and Entekhabi, D., 1996: Passive microwave remote sensing of soil moisture. Journal of Hydrology, 184 (1-2), 101-129.
  • National Emergency Management Agency (NEMA), 2002: 2002 Disaster report. 732 pp.
  • Naeimi, V., K. Scipal, Z. Bartalis, S. Hasenauer, and Wagner., W., 2009: An improved soil moisture retrieval algorithm for ERS and METOP scatterometer observations. IEEE Transactions on Geoscience and Remote Sensing 47(7):1999–2013.
  • NASA, 2015: The TRMM and GPM Missions. [Available online at http:// pmm.nasa.gov/waterfalls/science/trmm-gpm-missions.]
  • Mugnai, A., and Smith, E. A., 1988: Radiative transfer to space through a precipitating cloud at multiple microwave frequencies. Part 1: model description. J. Appl. Meteorol., 27, 1055-1073.
  • Moran, M. S., Rahman, A. F., Washburne, J. C., Goodrich, D. C., Weltz, M. A., and Kustas, W. P., 1996: Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology, 80, 87-109.
  • Mo, T., Choudhury, B. J., Schmugge, T. J., Wang, J. R., and Jackson, T. J., 1982: A model for microwave emission from vegetation‐covered fields. Journal of Geophysical Research: Oceans (1978–2012), 87, 11229-11237.
  • Mishra, A. K., and Kumar, R., 2012: Study of Rainfall from TRMM Microwave Imager Observation over India. ISRN Geophys., 2012, 921824, doi:10.5402/2012/921824.
  • Mika, M., Hiroyuki, Y., Takao, K., Takeshi, I. and Mitsuo, S., 2000: Road surface condition detector using high peak power fiber laser,” Transactions of the Institute of Electrical Engineers of Japan, vol. 10, no. 4, pp. 1198–1204.
  • Mattikalli, N. M., 1997: Soil color modeling for the visible and near-infrared bands of Landsat sensors using laboratory spectral measurements. Remote Sensing of Environment, 59, 14-28.
  • Mannstein, H., 1987: Surface energy budget, surface temperature and thermal inertia, in Remote Sensing Applications in Meteorology and Climatology, edited by R. A. Vaughan, pp. 391–410, D. Reidel, Dordrecht, Netherlands.
  • Maki, M., Ishiahra, M., and Tamura, M., 2004: Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment, 90(4), 441-450.
  • Lobell, D. B., and Asner, G. P., 2002: Moisture effects on soil reflectance. Soil Science Society of America Journal, 66, 722-727.
  • Liu, W., F. Baret, Xingfa Gu, Bing Zhang, Qingxi Tong, and Lanfen Zheng., 2003: Evaluation of methods for soil surface moisture estimation from reflectance data. International Journal of Remote Sensing 24(10):2069–2083.
  • Li, K., E. Njoku, E. Im, P. Chang, and K. St. Germain, 2004: A preliminary survey of radio-frequency interference over the U.S. in Aqua AMSR-E data. IEEE Transactions on Geoscience and Remote Sensing, 42, 380–390.
  • Larson, K. M., Small, E. E., Gutmann, E., Bilich, A., Axelrad, P. and Braun, J., 2008: Using GPS multipath to measure soil moisture fluctuations: initial results. Gps Solutions, 12 (3), 173-177.
  • Larson, K. M., Gutmann, E. D., Zavorotny, V. U., Braun, J. J., Williams, M. W. and Nievinski, F. G., 2009: Can we measure snow depth with GPS receivers? Geophysical Research Letters, 36.
  • Larson, K. M., 2016: GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States. Wiley Interdisciplinary Reviews-Water, 3 (6), 775-787.
  • Larson, K. M. and Small, E. E., 2014: Normalized Microwave Reflection Index: A Vegetation Measurement Derived From GPS Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (5), 1501-1511.
  • Larson, K. M. and Nievinski, F. G., 2013: GPS snow sensing: results from the EarthScope Plate Boundary Observatory. Gps Solutions, 17 (1), 41-52.
  • Kummerow, C., and Giglio, L., 1994: A passive microwave technique for estimating rainfall and vertical structure information from space. Part II: Applications to SSM/I data. J. Appl. Meteorol., 33, 19-34.
  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteorol., 40, 1801-1820.
  • Kummerow, C., Simpson, J., Thiele, O., Barnes, W., Chang, A. T. C., Stocker, E., ... and Ashcroft, P., 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. Journal of Applied meteorology, 39(12), 1965-1982.
  • Kummerow, C., Mack, R. A. and Hakkarinen, I. M., 1989: A self-consistency approach to improve microwave rainfall rate estimation from space. J. Appl. Meteorol., 28, 869-884.
  • Kummerow, C., Barnes, W., Kozu, T., Shiue, J., & Simpson, J., 1998: The tropical rainfall measuring mission (TRMM) sensor package. Journal of atmospheric and oceanic technology, 15(3), 809-817.
  • Kummerow, C., Barnes, W., Kozu, T., Shiue, J. and Simpson, J., 1998: The tropical rainfall measuring mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809-817.
  • Komjathy, A., Zavorotny, V. U., Axelrad, P., Born, G. H. and Garrison, J. L., 2000: GPS signal scattering from sea surface: Wind speed retrieval using experimental data and theoretical model, Remote Sens Environ, 73(2), 162-174
  • Koch, F., Schlenz, F., Prasch, M., Appel, F., Ruf, T. and Mauser, W., 2016: Soil Moisture Retrieval Based on GPS Signal Strength Attenuation. Water, 8 (7).
  • Kim, J.-H., Ou, M.-L., Park, J.-D., Morris, K. R., Schwaller, M. R. and Wolff, D. B., 2014: Global precipitation measurement (GPM) ground validation (GV) prototype in the Korean Peninsula. J. Atmos. Oceanic Technol., 31, 1902-1921, doi:10.1175/JTECH-D-13-00193.1.
  • Kidd, C., 1998: On rainfall retrieval using polarization-corrected temperatures. Int. J. Remote Sens., 19, 981-996.
  • Khandelwal, S., Goyal, R., Kaul, N. and Mathew, A., 2017: Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Science.
  • KMA National Typhoon Center (NTC), cited 2015: Genesis. [Available online at http://typ.kma.go.kr/TYPHOON/statistics/statistics_02_1.jsp.] (in Korean).
  • Jokela, M., Kutila, M. and Le, L., 2009: Road condition monitoring system based on a stereo camera, in Proceedings of the IEEE 5th International Conference on Intelligent Computer Communication and Processing (ICCP '09), pp. 423–428.
  • Jin, S. G. and Najibi, N., 2014: Sensing snow height and surface temperature variations in Greenland from GPS reflected signals. Advances in Space Research, 53 (11), 1623-1633.
  • Jin, M., and R. E. Dickinson, 2010: Land surface skin temperature climatology: Benefitting from the strengths of satellite observations, Environ. Res. Lett., 5, 044004, doi:10.1088/1748-9326/5/4/044004.
  • Jacob, F., F. Petitcolin, T. Schmugge, E. Vermote, A. French, and K. Ogawa, 2004: Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors, Remote Sens. Environ., 90, 137–152, doi:10.1016/j.rse.2003.11.015.
  • Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P. and Hunt, E. R., 2004: Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, 92(4), 475-482.
  • Imaoka, K., Kachi, M., Kasahara, M., Ito, N., Nakagawa, K. and Oki, T., 2010: Instrument performance and calibration of AMSR-E and AMSR2. Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., 38, 13-16.
  • Hufford, G., 1991: A Model for the Complex Permittivity of Ice at Frequencies Below 1 THz. International Journal of Infrared and Millimeter Waves, 12 (7), 677-682.
  • Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., ... & Stocker, E. F., 2007: The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of hydrometeorology, 8(1), 38-55.
  • Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., & Yoo, S. H., 2015: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm theoretical basis document, version, 4, 30.
  • Huete, A. R., 1988: A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25, 295-309.
  • Hong, S., 2010: Surface roughness and polarization ratio in microwave remote sensing. International Journal of Remote Sensing, 31 (10), 2709-2716.
  • Hong, S., 2010: Global retrieval of small-scale roughness over land surfaces at microwave frequency. Journal of Hydrology, 389 (1-2), 121-126.
  • Hong, S., 2009: Effects of vertical resolution in plane-parallel atmospheric model on brightness temperature. IEEE Geosci. Remote Sens. Lett., 6, 602-605.
  • Holah, N., Baghdadi, N., Zribi, M., Bruand, A., and King, C., 2005: Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields. Remote sensing of environment, 96, 78-86.
  • Han, Y., Wang, Y., and Zhao, Y., 2010: Estimating soil moisture conditions of the greater Changbai Mountains by land surface temperature and NDVI. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2509-2515.
  • Hallikainen, M., Ulaby, F., Dobson, M., El-Rayes, M. and Wu, L., 1985: Microwave dielectric behavior of wet soil—Part I: Empirical models and experimental observations,” IEEE Trans. Geosci. Remote Sens., GE-23(1), pp. 25–34.
  • Hain, C. R., Mecikalski, J. R., and Anderson., M. C. 2009: Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation. Journal of Hydrometeorology 10, 665–683. doi:10.1175/2008JHM1024.1.
  • Grody, N. C., 1991: Classification of snow cover and precipitation using the special sensor Microwave Imager. J. Geophys. Res., 96, 7423-7435.
  • Goetz, S. J. 1997: Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing, 18, 71-94.
  • Gillies, R. R., and Carlson, T. N., 1995: Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, 34, 745-756.
  • Gillies, R. R., Kustas, W. P., and Humes, K. S., 1997: A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface. International Journal of Remote Sensing, 18, 3145-3166.
  • Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3), 257-266.
  • GCOS 2010: Implementation Plan for the Global Climate Observing System in Support of the UNFCCC (2010 Update), GCOS-138, Geneva, Switzerland
  • Friedl, M. A., and F. W. Davis., 1994: Sources of variation in radiometric surface temperature over a tallgrass prairie. Remote Sensing of Environment 48(1):1–17.
  • Ferraro, R. R., and Marks, G. F., 1995: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J. Atmos. Oceanic Technol., 12, 755-770.
  • Ferraro, R. R., Grody, N. C., Wang, F. and Basist, A., 1996: An eight-year (1987-1994) time series of rainfall, clouds, water vapor, snow cover, and seaice derived from SSM/I measurements. Bull. Amer. Meteor. Soc., 77, 891-905.
  • Evans, S. G., Small, E. E. and Larson, K. M., 2014: Comparison of vegetation phenology in the western USA determined from reflected GPS microwave signals and NDVI. International Journal of Remote Sensing, 35 (9), 2996-3017.
  • Evans, K. F., Turk, J., Wong, T. and Stephens, G. L., 1995: A Bayesian approach to microwave precipitation profile retrieval. J. Appl. Meteorol., 34, 260-278.
  • Escadafal, R., Girard, M. C., and Courault, D., 1989: Munsell soil color and soil reflectance in the visible spectral bands of Landsat MSS and TM data. Remote Sensing of Environment, 27, 37-46.
  • Ellingson, S. W. and J. T. Johnson, 2006: A polarimetric survey of radio frequency interference in C- and X-bands in the continental United States using WindSat radiometry, IEEE Transactions on Geoscience and Remote Sensing, 44, 540–548.
  • Dobson, M. C., Ulaby, F. T., Hallikainen, M. T. and Elrayes, M. A., 1985: Microwave Dielectric Behavior of Wet Soil .2. Dielectric Mixing Models. IEEE Transactions on Geoscience and Remote Sensing, 23 (1), 35-46.
  • Delbart, N., Kergoat, L., Le Toan, T., Lhermitte, J., and Picard, G., 2005: Determination of phenological dates in boreal regions using normalized difference water index. Remote sensing of environment, 97(1), 26-38.
  • Debye, P., 1913: Ver. Deut. Phys. Gesell. 15, 777; reprinted 1954 in collected papers of Peter J.W. Debye. Interscience, New York
  • Collier, C. G., Lovejoy, S. and Austin, G. L., 1980: Analysis of bright bands from 3D radar data (for snow/rain delineation). Preprints, 19th Conf. on Radar Meteorology, Miami Beach, 44-47.
  • Choy, S., Wang, C. S., Yeh, T. K., Dawson, J., Jia, M. H. and Kuleshov, Y., 2015: Precipitable Water Vapor Estimates in the Australian Region from Ground-Based GPS Observations. Advances in Meteorology, 1-14.
  • Choudhury, B. J., Schmugge, T. J., Chang, A. and Newton, R. W., 1979: Effect of Surface-Roughness on the Microwave Emission from Soils. Journal of Geophysical Research-Oceans 84 (Nc9), 5699-5706.
  • Chen, D., Huang, J., and Jackson, T. J., 2005: Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near-and shortwave infrared bands. Remote Sensing of Environment, 98(2-3), 225-236.
  • Chen, C. F., Son, N. T., Chang, L. Y., and Chen, C. C. , 2011: Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Applied Geography, 31(2), 463-475.
  • Ceccato, P., Gobron, N., Flasse, S., Pinty, B., and Tarantola, S., 2002: Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sensing of Environment, 82(2-3), 188-197.
  • Casselgren, J., Kutila, M. and Jokela, M., 2012: Slippery road detection by using different methods of polarised light, in Advanced Microsystems for Automotive Applications, pp. 207–220.
  • Carlson, T. N., 2007: An overview of the Triangle Method for estimating surface evapotranspiration and soil moisture from satellite imagery, Sensors, 7, 1612- 1629.
  • Carlson, T. N., 1986: Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements. Remote Sensing Reviews 1(2):197–247.
  • Bowers, S. A., and Hanks, R. J., 1965: Reflection of radiant energy from soils. Soil Science, 100, 130-138.
  • Bohren, C. F., and Huffman, D. R., 1998: Absorption and scattering of light by small particles, New York: Wiley, 1998, 530 p., ISBN 0-471-29340-7, ISBN 978-0-471- 29340-8
  • Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A. and Ware, R. H., 1992: GPS Meteorology - Remote-Sensing of Atmospheric Water-Vapor Using the Global Positioning System. Journal of Geophysical Research-Atmospheres, 97 (D14), 15787-15801.
  • Bartalis, Z., W. Wagner, V. Naeimi, S. Hasenauer, K. Scipal, H. Bonekamp, J. Figa, and C. Anderson., 2007: Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT). Geophysical Research Letters 34(20):L20401.
  • Barrett, E. C., and Kidd, C., 1990: Rainfall monitoring by the SSM/I in the mid-latitudes. Extended Abstract, Fifth Conf. on Satellite Meteorology and Oceanography, London, 210-214.
  • Baret, F. and Guyot, G., 1991: Potentials and limits of vegetation indices for LAI and APAR assessment. Remote sensing of environment, 35, 161-173.
  • Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., and Kustas, W. P., 2007: A Climatological Study of Evapotranspiration and Moisture Stress across the Continental United States Based on Thermal Remote Sensing: 1. Model Formulation. Journal of Geophysical Research 112, D10117. doi:10.1029/2006JD007506.
  • Adler, R. F., Huffman, G. J. and Keehn, P. R. 1994: Global tropical rain estimates from microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11, 125-152.
  • Adler, R. F. and Negri, A. J., 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27, 30-51.