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Analysis of the characteristics of satellite-derived multiple channel microwave emissivity difference vegetation index (EDVI) over different vegetation types

  • School of Earth and Space Science, University of Science and Technology of China, Hefei 230026, China

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https://doi.org/10.3969/j.issn.0253-2778.2020.04.016
  • Received Date: 08 February 2019
  • Accepted Date: 15 March 2019
  • Rev Recd Date: 15 March 2019
  • Publish Date: 30 April 2020
    Abstract

  • Abstract

    The satellite-derived microwave land surface Emissivity Difference Vegetation Index (EDVI) is a good indicator of vegetation water content (VWC). And it can be used under a cloudy sky and over dense vegetation areas where satellite optical vegetation index can be easily saturated. Previous studies on EDVI used the two frequencies of 19 and 37 GHz only. The associated EDVI can only represent VWC in one single layer of the canopy. Measurements from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) onboard NASA’s Aqua satellite provided multiple choices of satellite remote sensing VWC with EDVI. In this study, multiple source measurements, including microwave brightness temperature from AMSR-E, cloud parameters from Moderate-resolution Imaging Spectroradiometer (MODIS), and reanalysis of atmosphere states from ECMWF were synergized, to retrieve the microwave land surface emissivity at 6.925, 10.65, 18.7 and 36.5 GHz in eastern and southeastern Asia from 2003 to 2010. The retrievals were then used to define multiple channel EDVIs of EDVI(6v, 37v)，EDVI(10v, 37v) and EDVI(18v, 37v) and the characteristics of those EDVIs over forest and cropland areas were studied. The results showed that, EDVI(6v, 37v) and EDVI(10v, 37v) are greater than EDVI(18v, 37v) in forest dominated areas. This is most likely due to the fact that the penetration depth of EDVI(6v, 37v) and EDVI(10v, 37v) are deeper than that of EDVI(18v, 37v), thus indicating relatively more VWC in thicker canopy layer of the forest. The differences among the three EDVIs can be used to represent the vertical distribution of VWC in upper, middle and lower layers of high and dense vegetation. However, for the shallow and sparse vegetation without significant vertical variation of VWC, the differences among the three EDVIs are too small to be used.

    Abstract

    The satellite-derived microwave land surface Emissivity Difference Vegetation Index (EDVI) is a good indicator of vegetation water content (VWC). And it can be used under a cloudy sky and over dense vegetation areas where satellite optical vegetation index can be easily saturated. Previous studies on EDVI used the two frequencies of 19 and 37 GHz only. The associated EDVI can only represent VWC in one single layer of the canopy. Measurements from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) onboard NASA’s Aqua satellite provided multiple choices of satellite remote sensing VWC with EDVI. In this study, multiple source measurements, including microwave brightness temperature from AMSR-E, cloud parameters from Moderate-resolution Imaging Spectroradiometer (MODIS), and reanalysis of atmosphere states from ECMWF were synergized, to retrieve the microwave land surface emissivity at 6.925, 10.65, 18.7 and 36.5 GHz in eastern and southeastern Asia from 2003 to 2010. The retrievals were then used to define multiple channel EDVIs of EDVI(6v, 37v)，EDVI(10v, 37v) and EDVI(18v, 37v) and the characteristics of those EDVIs over forest and cropland areas were studied. The results showed that, EDVI(6v, 37v) and EDVI(10v, 37v) are greater than EDVI(18v, 37v) in forest dominated areas. This is most likely due to the fact that the penetration depth of EDVI(6v, 37v) and EDVI(10v, 37v) are deeper than that of EDVI(18v, 37v), thus indicating relatively more VWC in thicker canopy layer of the forest. The differences among the three EDVIs can be used to represent the vertical distribution of VWC in upper, middle and lower layers of high and dense vegetation. However, for the shallow and sparse vegetation without significant vertical variation of VWC, the differences among the three EDVIs are too small to be used.
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    • References(40)
  • [1]
    BANNARI A, MORIN D, BONN F, et al. A review of vegetation indices[J]. Remote Sensing Reviews, 1995, 13(1/2): 95-120.
    [2]
    JACKSON T J, CHEN D, COSH M, et al. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J]. Remote Sensing of Environment, 2004, 92(4): 475-482.
    [3]
    TUCKER C J, SELLERS P J. Satellite remote sensing of primary production[J]. International Journal of Remote Sensing, 1986, 7(11): 1395-1416.
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    STOW D A, HOPE A, MCGUIRE D, et al. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems[J]. Remote Sensing of Environment, 2004, 89(3): 281-308.
    [5]
    TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150.
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    TUCKER C J. Remote sensing of leaf water content in the near infrared[J]. Remote Sensing of Environment, 1980, 10(1): 23-32.
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    CHEN J M, CIHLAR J. Retrieving leaf area index of boreal conifer forests using Landsat TM images[J]. Remote Sensing of Environment, 1996, 55(2): 153-162.
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    SIMS D A, GAMON J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features[J]. Remote Sensing of Environment, 2003, 84(4): 526-537.
    [9]
    LI R, MIN Q. Dynamic response of microwave land surface properties to precipitation in Amazon rainforest[J]. Remote Sensing of Environment, 2013, 133: 183-192.
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    SHI J, JACKSON T, TAO J, et al. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E[J]. Remote Sensing of Environment, 2008, 112(12): 4285-4300.
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    WANG J R, MCMURTREY III J E, ENGMAN E T, et al. Radiometric measurements over bare and vegetated fields at 1.4-GHz and 5-GHz frequencies[J]. Remote Sensing of Environment, 1982, 12(4): 295-311.
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    MO T, CHOUDHURY B J, SCHMUGGE T J, et al. A model for microwave emission from vegetation-covered fields[J]. Journal of Geophysical Research: Oceans, 1982, 87(C13): 11229-11237.
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    ULABY F T, RAZANI M, DOBSON M C. Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture[J]. IEEE Transactions on Geoscience and Remote Sensing, 1983 (1): 51-61.
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    BURKE H H K, SCHMUGGE T J. Effects of varying soil moisture contents and vegetation canopies on microwave emissions[J]. IEEE Transactions on Geoscience and Remote Sensing, 1982 (3): 268-274.
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    BRUNFELDT D R, ULABY F T. Measured microwave emission and scattering in vegetation canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 1984 (6): 520-524.
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    PAMPALONI P, PALOSCIA S. Experimental relationships between microwave emission and vegetation features[J]. International Journal of Remote Sensing, 1985, 6(2): 315-323.
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    PAMPALONI P, PALOSCIA S. Microwave emission and plant water content: A comparison between field measurements and theory[J]. IEEE Transactions on Geoscience and Remote Sensing, 1986(6): 900-905.
    [20]
    CHOUDHURY B J, TUCKER C J. Monitoring global vegetation using Nimbus-7 37GHz data: Some empirical relations[J]. International Journal of Remote Sensing, 1987, 8(7): 1085-1090.
    [21]
    CHOUDHURY B J, TUCKER C J, GOLUS R E, et al. Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer’s data[J]. International Journal of Remote Sensing, 1987, 8(3): 533-538.
    [22]
    PALOSCIA S, PAMPALONI P. Microwave polarization index for monitoring vegetation growth[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(5): 617-621.
    [23]
    PALOSCIA S, PAMPALONI P. Microwave vegetation indexes for detecting biomass and water conditions of agricultural crops[J]. Remote Sensing of Environment, 1992, 40(1): 15-26.
    [24]
    MIN Q, LIN B. Remote sensing of evapotranspiration and carbon uptake at Harvard Forest[J]. Remote Sensing of Environment, 2006, 100(3): 379-387.
    [25]
    MIN Q, LIN B. Determination of spring onset and growing season leaf development using satellite measurements[J]. Remote Sensing of Environment, 2006, 104(1): 96-102.
    [26]
    MIN Q, LIN B, LI R. Remote sensing vegetation hydrological states using passive microwave measurements[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(1): 124-131.
    [27]
    LI R, MIN Q, LIN B. Estimation of evapotranspiration in a mid-latitude forest using the Microwave Emissivity Difference Vegetation Index (EDVI)[J]. Remote Sensing of Environment, 2009, 113(9): 2011-2018.
    [28]
    LI R, MIN Q. Dynamic response of microwave land surface properties to precipitation in amazon rainforest[J]. Remote Sensing of Environment, 2013, 133: 183-192.
    [29]
    LIU G. A fast and accurate model for microwave radiance calculations[J]. Journal of the Meteorological Society of Japan. Ser. II, 1998, 76(2): 335-343.
    [30]
    ASHCROFT P, WENTZ F J. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures, Version 3[DB/OL]. Boulder, CO, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center, 2013[2019-01-10]. http://dx.doi.org/10.5067/AMSR-E/AE_L2A.003 .
    [31]
    KUMMEROW C, FERRARO R, RANDEL D. AMSR-E/Aqua L2B Global Swath Surface Precipitation GSFC Profiling Algorithm, Version 3[DB/OL]. Boulder, CO, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center, 2015[2019-01-10]. https://doi.org/10.5067/AMSR-E/AE_RAIN.003.
    [32]
    PLATNICK S, ACKERMAN S A, KING M D, et al. MODIS Atmosphere L2 Cloud Product (06_L2)[DB/OL]. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA, 2015[2019-01-10]. http://dx.doi.org/10.5067/MODIS/MYD06_L2.006.
    [33]
    POLI P, HERSBACH H, DEE D P, et al. ERA-20C: An atmospheric reanalysis of the twentieth century[J]. Journal of Climate, 2016, 29(11): 4083-4097.
    [34]
    JACKSON T J, SCHMUGGE T J. Vegetation effects on the microwave emission of soils[J]. Remote Sensing of Environment, 1991, 36(3): 203-212.
    [35]
    DIDAN K. MYD13C2 MODIS/Aqua Vegetation Indices Monthly L3 Global 0.05Deg CMG V006[DB/OL]. NASA EOSDIS Land Processes DAAC, 2015[2019-01-10]. https://doi.org/10.5067/MODIS/MYD13C2.006.
    [36]
    FRIEDL M A, SULLA-MENASHE D, TAN B, et al. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010, 114(1): 168-182.
    [37]
    LAWLOR D W, CORNIC G. Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants[J]. Plant, Cell & Environment, 2002, 25(2): 275-294.
    [38]
    ULABY F T, MCDONALD K, SARABANDI K, et al. Michigan microwave canopy scattering models (MIMICS)[C]//International Geoscience and Remote Sensing Symposium, “Remote Sensing: Moving Toward the 21st Century”. IEEE, 1988: 1009.
    [39]
    KARAM M A, FUNG A K, LANG R H, et al. A microwave scattering model for layered vegetation[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 767-784
    [40]
    WANG F, SHI J, ZHANG L, et al. Discrete scatter model for microwave radiometer response to wheat field, comparison of theory and data[C]//2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012: 638-641.)
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    [1]
    BANNARI A, MORIN D, BONN F, et al. A review of vegetation indices[J]. Remote Sensing Reviews, 1995, 13(1/2): 95-120.
    [2]
    JACKSON T J, CHEN D, COSH M, et al. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J]. Remote Sensing of Environment, 2004, 92(4): 475-482.
    [3]
    TUCKER C J, SELLERS P J. Satellite remote sensing of primary production[J]. International Journal of Remote Sensing, 1986, 7(11): 1395-1416.
    [4]
    STOW D A, HOPE A, MCGUIRE D, et al. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems[J]. Remote Sensing of Environment, 2004, 89(3): 281-308.
    [5]
    TUCKER C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8(2): 127-150.
    [6]
    TUCKER C J. Remote sensing of leaf water content in the near infrared[J]. Remote Sensing of Environment, 1980, 10(1): 23-32.
    [7]
    CHEN J M, CIHLAR J. Retrieving leaf area index of boreal conifer forests using Landsat TM images[J]. Remote Sensing of Environment, 1996, 55(2): 153-162.
    [8]
    SIMS D A, GAMON J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features[J]. Remote Sensing of Environment, 2003, 84(4): 526-537.
    [9]
    LI R, MIN Q. Dynamic response of microwave land surface properties to precipitation in Amazon rainforest[J]. Remote Sensing of Environment, 2013, 133: 183-192.
    [10]
    SHI J, JACKSON T, TAO J, et al. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E[J]. Remote Sensing of Environment, 2008, 112(12): 4285-4300.
    [11]
    KIRDIASHEV K P, CHUKHLANTSEV A A, SHUTKO A M. Microwave radiation of the Earth’s surface in the presence of vegetation cover[J]. Radiotekhnika i Elektronika, 1979, 24: 256-264.
    [12]
    JACKSON T J, SCHMUGGE T J, WANG J R. Passive microwave sensing of soil moisture under vegetation canopies[J]. Water Resources Research, 1982, 18(4): 1137-1142.
    [13]
    WANG J R, MCMURTREY III J E, ENGMAN E T, et al. Radiometric measurements over bare and vegetated fields at 1.4-GHz and 5-GHz frequencies[J]. Remote Sensing of Environment, 1982, 12(4): 295-311.
    [14]
    MO T, CHOUDHURY B J, SCHMUGGE T J, et al. A model for microwave emission from vegetation-covered fields[J]. Journal of Geophysical Research: Oceans, 1982, 87(C13): 11229-11237.
    [15]
    ULABY F T, RAZANI M, DOBSON M C. Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture[J]. IEEE Transactions on Geoscience and Remote Sensing, 1983 (1): 51-61.
    [16]
    BURKE H H K, SCHMUGGE T J. Effects of varying soil moisture contents and vegetation canopies on microwave emissions[J]. IEEE Transactions on Geoscience and Remote Sensing, 1982 (3): 268-274.
    [17]
    BRUNFELDT D R, ULABY F T. Measured microwave emission and scattering in vegetation canopies[J]. IEEE Transactions on Geoscience and Remote Sensing, 1984 (6): 520-524.
    [18]
    PAMPALONI P, PALOSCIA S. Experimental relationships between microwave emission and vegetation features[J]. International Journal of Remote Sensing, 1985, 6(2): 315-323.
    [19]
    PAMPALONI P, PALOSCIA S. Microwave emission and plant water content: A comparison between field measurements and theory[J]. IEEE Transactions on Geoscience and Remote Sensing, 1986(6): 900-905.
    [20]
    CHOUDHURY B J, TUCKER C J. Monitoring global vegetation using Nimbus-7 37GHz data: Some empirical relations[J]. International Journal of Remote Sensing, 1987, 8(7): 1085-1090.
    [21]
    CHOUDHURY B J, TUCKER C J, GOLUS R E, et al. Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer’s data[J]. International Journal of Remote Sensing, 1987, 8(3): 533-538.
    [22]
    PALOSCIA S, PAMPALONI P. Microwave polarization index for monitoring vegetation growth[J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(5): 617-621.
    [23]
    PALOSCIA S, PAMPALONI P. Microwave vegetation indexes for detecting biomass and water conditions of agricultural crops[J]. Remote Sensing of Environment, 1992, 40(1): 15-26.
    [24]
    MIN Q, LIN B. Remote sensing of evapotranspiration and carbon uptake at Harvard Forest[J]. Remote Sensing of Environment, 2006, 100(3): 379-387.
    [25]
    MIN Q, LIN B. Determination of spring onset and growing season leaf development using satellite measurements[J]. Remote Sensing of Environment, 2006, 104(1): 96-102.
    [26]
    MIN Q, LIN B, LI R. Remote sensing vegetation hydrological states using passive microwave measurements[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2010, 3(1): 124-131.
    [27]
    LI R, MIN Q, LIN B. Estimation of evapotranspiration in a mid-latitude forest using the Microwave Emissivity Difference Vegetation Index (EDVI)[J]. Remote Sensing of Environment, 2009, 113(9): 2011-2018.
    [28]
    LI R, MIN Q. Dynamic response of microwave land surface properties to precipitation in amazon rainforest[J]. Remote Sensing of Environment, 2013, 133: 183-192.
    [29]
    LIU G. A fast and accurate model for microwave radiance calculations[J]. Journal of the Meteorological Society of Japan. Ser. II, 1998, 76(2): 335-343.
    [30]
    ASHCROFT P, WENTZ F J. AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures, Version 3[DB/OL]. Boulder, CO, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center, 2013[2019-01-10]. http://dx.doi.org/10.5067/AMSR-E/AE_L2A.003 .
    [31]
    KUMMEROW C, FERRARO R, RANDEL D. AMSR-E/Aqua L2B Global Swath Surface Precipitation GSFC Profiling Algorithm, Version 3[DB/OL]. Boulder, CO, USA: NASA National Snow and Ice Data Center Distributed Active Archive Center, 2015[2019-01-10]. https://doi.org/10.5067/AMSR-E/AE_RAIN.003.
    [32]
    PLATNICK S, ACKERMAN S A, KING M D, et al. MODIS Atmosphere L2 Cloud Product (06_L2)[DB/OL]. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA, 2015[2019-01-10]. http://dx.doi.org/10.5067/MODIS/MYD06_L2.006.
    [33]
    POLI P, HERSBACH H, DEE D P, et al. ERA-20C: An atmospheric reanalysis of the twentieth century[J]. Journal of Climate, 2016, 29(11): 4083-4097.
    [34]
    JACKSON T J, SCHMUGGE T J. Vegetation effects on the microwave emission of soils[J]. Remote Sensing of Environment, 1991, 36(3): 203-212.
    [35]
    DIDAN K. MYD13C2 MODIS/Aqua Vegetation Indices Monthly L3 Global 0.05Deg CMG V006[DB/OL]. NASA EOSDIS Land Processes DAAC, 2015[2019-01-10]. https://doi.org/10.5067/MODIS/MYD13C2.006.
    [36]
    FRIEDL M A, SULLA-MENASHE D, TAN B, et al. MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets[J]. Remote Sensing of Environment, 2010, 114(1): 168-182.
    [37]
    LAWLOR D W, CORNIC G. Photosynthetic carbon assimilation and associated metabolism in relation to water deficits in higher plants[J]. Plant, Cell & Environment, 2002, 25(2): 275-294.
    [38]
    ULABY F T, MCDONALD K, SARABANDI K, et al. Michigan microwave canopy scattering models (MIMICS)[C]//International Geoscience and Remote Sensing Symposium, “Remote Sensing: Moving Toward the 21st Century”. IEEE, 1988: 1009.
    [39]
    KARAM M A, FUNG A K, LANG R H, et al. A microwave scattering model for layered vegetation[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(4): 767-784
    [40]
    WANG F, SHI J, ZHANG L, et al. Discrete scatter model for microwave radiometer response to wheat field, comparison of theory and data[C]//2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012: 638-641.)
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