Articles | Volume 19, issue 21
https://doi.org/10.5194/bg-19-5107-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-5107-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Nov 2022
Research article |
| 07 Nov 2022
| 07 Nov 2022
Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI)
Leander Moesinger
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria
Ruxandra-Maria Zotta
Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria
Robin van der Schalie
VanderSat, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands
Tracy Scanlon
Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria
Richard de Jeu
VanderSat, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands
Department of Geodesy and Geoinformation, Technische Universität Wien, Vienna, Austria
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Cited articles
Aldred, F., Gobron, N., Miller, J. B., Willett, K. M., and Dunn, R.: Global
climate, Bull. Am. Meteorol. Soc., 102, S11–S142, https://doi.org/10.1175/BAMS-D-21-0098.1, 2021. a
Allan, R.: Können. G. P., Jones, P. D., Katofen, M. H., and Allan, R. J.,
1998: Pre-1866 extensions of the Southern Oscillation Index using early
Indonesian and Tahitian meteorological readings, J. Clim., 11,
2325–2339, 1998. a
Allan, R. J., Nicholls, N., Jones, P. D., and Butterworth, I. J.: A Further
Extension of the Tahiti–Darwin SOI, Early ENSO Events and Darwin Pressure,
J. Clim., 4, 743–749, 1991. a
Bédard, F., Crump, S., and Gaudreau, J.: A comparison between Terra MODIS and
NOAA AVHRR NDVI satellite image composites for the monitoring of natural
grassland conditions in Alberta, Canada, Can. J. Remote Sens.,
32, 44–50, https://doi.org/10.5589/m06-001, 2006. a
Crocetti, L., Forkel, M., Fischer, M., Jurečka, F., Grlj, A., Salentinig,
A., Trnka, M., Anderson, M., Ng, W.-T., Kokalj, Ž., Bucur, A., and
Dorigo, W.: Earth Observation for agricultural drought monitoring in the
Pannonian Basin (southeastern Europe): current state and future directions,
Reg. Environ. Change, 20, 123 pp., https://doi.org/10.3929/ETHZ-B-000459516, 2020. a, b
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi,
M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D.,
Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C.,
van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA
CCI Soil Moisture for improved Earth system understanding: State-of-the art
and future directions, Remote Sens. Environ., 203, 185–215,
https://doi.org/10.1016/j.rse.2017.07.001, 2017. a
Dorigo, W. A., Zurita-Milla, R., de Wit, A. J., Brazile, J., Singh, R., and
Schaepman, M. E.: A review on reflective remote sensing and data
assimilation techniques for enhanced agroecosystem modeling, Int.
J. Appl. Earth Obs., 9, 165–193,
https://doi.org/10.1016/j.jag.2006.05.003, 2007. a
Dunn, R. J., Stanitski, D. M., Gobron, N., and Willett, K. M.: Global
climate, Bull. Am. Meteorol. Soc., 101, S9–S128, https://doi.org/10.1175/BAMS-D-20-0104.1, 2020. a
Frappart, F., Wigneron, J.-P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang,
M., Moisy, C., Le Masson, E., Aoulad Lafkih, Z., Vallé, C., Ygorra,
B., and Baghdadi, N.: Global Monitoring of the Vegetation Dynamics from the
Vegetation Optical Depth (VOD): A Review, Remote Sens., 12, 2915,
https://doi.org/10.3390/rs12182915, 2020. a, b
Gaiser, P. W., St. Germain, K. M., Twarog, E. M., Poe, G. A., Purdy, W.,
Richardson, D., Grossman, W., Jones, W. L., Spencer, D., Golba, G.,
Cleveland, J., Choy, L., Bevilacqua, R. M., and Chang, P. S.: The windSat
spaceborne polarimetric microwave radiometer: Sensor description and early
orbit performance, IEEE Trans. Geosci. Remote Sens., 42,
2347–2361, https://doi.org/10.1109/TGRS.2004.836867, 2004. a
Gallo, K., Ji, L., Reed, B., Eidenshink, J., and Dwyer, J.: Multi-platform
comparisons of MODIS and AVHRR normalized difference vegetation index data,
Remote Sens. Environ., 99, 221–231, https://doi.org/10.1016/j.rse.2005.08.014,
2005. a
Guo, Y., Huang, S., Huang, Q., Wang, H., Fang, W., Yang, Y., and Wang, L.:
Assessing socioeconomic drought based on an improved Multivariate
Standardized Reliability and Resilience Index, J. Hydrol., 568,
904–918, https://doi.org/10.1016/j.jhydrol.2018.11.055, 2019. a, b
Hao, Z. and AghaKouchak, A.: Multivariate Standardized Drought Index: A
parametric multi-index model, Adv. Water Resour., 57, 12–18,
https://doi.org/10.1016/j.advwatres.2013.03.009, 2013. a, b
Hashimoto, H., Nemani, R., Bala, G., Cao, L., Michaelis, A., Ganguly, S., Wang,
W., Milesi, C., Eastman, R., Lee, T., and Myneni, R.: Constraints to
Vegetation Growth Reduced by Region-Specific Changes in Seasonal Climate,
Climate, 7, 27, https://doi.org/10.3390/cli7020027, 2019. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Holmes, T. R. H., De Jeu, R. A. M., Owe, M., and Dolman, A. J.: Land surface
temperature from Ka band (37 GHz) passive microwave observations, J.
Geophys. Res., 114, D04113, https://doi.org/10.1029/2008JD010257, 2009. a
Huang, J. and van Den Dool, H. M.: Monthly precipitation-temperature
relations and temperature prediction over the United States, J.
Clim., 6, 1111–1132,
https://doi.org/10.1175/1520-0442(1993)006<1111:mptrat>2.0.co;2, 1993. a
Huang, S., Tang, L., Hupy, J. P., Wang, Y., and Shao, G.: A commentary review
on the use of normalized difference vegetation index (NDVI) in the era of
popular remote sensing, J. Forest. Res., 32, 1–6, https://doi.org/10.1007/s11676-020-01155-1, 2021. a
Huete, A. R., Didan, K., Shimabukuro, Y. E., Ratana, P., Saleska, S. R.,
Hutyra, L. R., Yang, W., Nemani, R. R., and Myneni, R.: Amazon rainforests
green-up with sunlight in dry season, Geophys. Res. Lett., 33,
L06405, https://doi.org/10.1029/2005GL025583, 2006. a
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a
Jackson, T. and Schmugge, T.: Vegetation effects on the microwave emission of
soils, Remote Sens. Environ., 36, 203–212,
https://doi.org/10.1016/0034-4257(91)90057-D, 1991. a, b
Janssen, T., van der Velde, Y., Hofhansl, F., Luyssaert, S., Naudts, K.,
Driessen, B., Fleischer, K., and Dolman, H.: Drought effects on leaf fall,
leaf flushing and stem growth in the Amazon forest: reconciling remote
sensing data and field observations, Biogeosciences, 18, 4445–4472,
https://doi.org/10.5194/bg-18-4445-2021, 2021. a
Jones, M. O., Jones, L. A., Kimball, J. S., and McDonald, K. C.: Satellite
passive microwave remote sensing for monitoring global land surface
phenology, Remote Sens. Environ., 115, 1102–1114,
https://doi.org/10.1016/J.RSE.2010.12.015, 2011. a
Katz, R. W. and Glantz, M. H.: Anatomy of a rainfall index., Mon. Weather
Rev., 114, 764–771, https://doi.org/10.1175/1520-0493(1986)114<0764:AOARI>2.0.CO;2,
1986. a
Kawanishi, T., Sezai, T., Ito, Y., Imaoka, K., Takeshima, T., Ishido, Y.,
Shibata, A., Miura, M., Inahata, H., and Spencer, R.: The advanced microwave
scanning radiometer for the earth observing system (AMSR-E), NASDA's
contribution to the EOS for global energy and water cycle studies, IEEE
Trans. Geosci. Remote Sens., 41, 184–194,
https://doi.org/10.1109/TGRS.2002.808331, 2003. a
Knowles, K., Savoie, M., Armstrong, R., and Brodzik, M. J.: AMSR-E/Aqua Daily
EASE-Grid Brightness Temperatures, Version 1 [Data Set], Boulder, Colorado USA, NASA National
Snow and Ice Data Center Distributed Active Archive Center,
https://doi.org/10.5067/XIMNXRTQVMOX, 2006. a
Kogan, F. N.: Remote sensing of weather impacts on vegetation in
non-homogeneous areas, Int. J. Remote Sens., 11,
1405–1419, https://doi.org/10.1080/01431169008955102, 1990. a, b, c, d
Kogan, F. N.: Operational space technology for global vegetation assessment,
Bull. Am. Meteorol. Soc., 82, 1949–1964,
https://doi.org/10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2, 2001. a, b, c, d
Konings, A. G., Holtzman, N. M., Rao, K., Xu, L., and Saatchi, S. S.:
Interannual Variations of Vegetation Optical Depth are Due to Both Water
Stress and Biomass Changes, Geophys. Res. Lett., 48,
e2021GL095267, https://doi.org/10.1029/2021gl095267, 2021. a, b
Kummerow, C., Barnes, W., Kozu, T., Shiue, J., Simpson, J., Kummerow, C.,
Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical Rainfall
Measuring Mission (TRMM) Sensor Package, J. Atmos. Ocean.
Technol., 15, 809–817,
https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2, 1998. a
Larcher, W.: Temperature stress and survival ability of mediterranean
sclerophyllous plants, Plant Biosyst., 134, 279–295,
https://doi.org/10.1080/11263500012331350455, 2000. a
Lewis, S. L., Brando, P. M., Phillips, O. L., Van Der Heijden, G. M., and
Nepstad, D.: The 2010 Amazon drought, Science, 331, p. 554, https://doi.org/10.1126/science.1200807, 2011. a
Li, W., Migliavacca, M., Forkel, M., Walther, S., Reichstein, M., and Orth, R.:
Revisiting Global Vegetation Controls Using Multi-Layer Soil Moisture,
Geophys. Res. Lett., 48, e2021GL092856,
https://doi.org/10.1029/2021GL092856, 2021. a
Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., and Holmes, T. R. H.: An
analysis of spatiotemporal variations of soil and vegetation moisture from a
29-year satellite-derived data set over mainland Australia, Water Resour.
Res., 45, 7, https://doi.org/10.1029/2008WR007187, 2009. a
Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., Canadell, J. G., McCabe, M. F.,
Evans, J. P., and Wang, G.: Recent reversal in loss of global terrestrial
biomass, Nat. Clim. Change, 5, 470–474, https://doi.org/10.1038/nclimate2581,
2015. a, b
Liu, Y. Y., van Dijk, A. I., Miralles, D. G., McCabe, M. F., Evans, J. P.,
de Jeu, R. A., Gentine, P., Huete, A., Parinussa, R. M., Wang, L., Guan, K.,
Berry, J., and Restrepo-Coupe, N.: Enhanced canopy growth precedes
senescence in 2005 and 2010 Amazonian droughts, Remote Sens.
Environ., 211, 26–37, https://doi.org/10.1016/J.RSE.2018.03.035, 2018. a, b
Markus, T., Comiso, J. C., and Meier, W. N.:
AMSR-E/AMSR2 Unified L3 Daily 25 km Brightness Temperatures & Sea Ice Concentration Polar Grids,
Version 1 [Data Set], Boulder, Colorado USA, NASA National Snow and
Ice Data Center Distributed Active Archive Center,
https://doi.org/10.5067/TRUIAL3WPAUP,
2018. a
Martens, B., Miralles, D. G., Lievens, H., Van Der Schalie, R., De Jeu,
R. A., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest,
N. E.: GLEAM v3: Satellite-based land evaporation and root-zone soil
moisture, Geosci. Model Dev., 10, 1903–1925,
https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
Martens, B., Waegeman, W., Dorigo, W. A., Verhoest, N. E., and Miralles, D. G.:
Terrestrial evaporation response to modes of climate variability, npj
Clim. Atmos. Sci., 1, 1–7, https://doi.org/10.1038/s41612-018-0053-5,
2018. a
Meesters, A., DeJeu, R., and Owe, M.: Analytical Derivation of the Vegetation
Optical Depth From the Microwave Polarization Difference Index, IEEE
Geosci. Remote Sens. Lett., 2, 121–123,
https://doi.org/10.1109/LGRS.2005.843983, 2005. a, b
Miralles, D. G., Van Den Berg, M. J., Gash, J. H., Parinussa, R. M., De
Jeu, R. A., Beck, H. E., Holmes, T. R., Jiménez, C., Verhoest, N. E.,
Dorigo, W. A., Teuling, A. J., and Johannes Dolman, A.: El Niño-La
Niña cycle and recent trends in continental evaporation, Nat.
Clim. Change, 4, 122–126, https://doi.org/10.1038/nclimate2068, 2014. a
Mo, T., Choudhury, B. J., Schmugge, T. J., Wang, J. R., and Jackson, T. J.: A
model for microwave emission from vegetation-covered fields, J.
Geophys. Res., 87, 11229, https://doi.org/10.1029/JC087iC13p11229, 1982. a
Moesinger, L., Zotta, R.-M., van der Schalie, R., Scanlon, T., de Jeu, R.,
Teubner, I., and Dorigo, W.: The Standardized Vegetation Optical Depth Index
SVODI, Zenodo [data set], https://doi.org/10.5281/zenodo.7114654, 2022. a, b
Morton, D. C., Nagol, J., Carabajal, C. C., Rosette, J., Palace, M., Cook,
B. D., Vermote, E. F., Harding, D. J., and North, P. R. J.: Amazon forests
maintain consistent canopy structure and greenness during the dry season,
Nature, 506, 221–224, https://doi.org/10.1038/nature13006, 2014. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Palmer, W. C.: Meteorological drought, U.S. Research Paper No. 45, US Weather Bureau,
Washington, DC, 1965. a
Panisset, J. S., Libonati, R., Gouveia, C. M. P., Machado-Silva, F.,
França, D. A., França, J. R. A., and Peres, L. F.: Contrasting
patterns of the extreme drought episodes of 2005, 2010 and 2015 in the Amazon
Basin, Int. J. Climatol., 38, 1096–1104,
https://doi.org/10.1002/joc.5224, 2018. a
Papagiannopoulou, C., Miralles, D. G., Dorigo, W. A., Verhoest, N. E.,
Depoorter, M., and Waegeman, W.: Vegetation anomalies caused by antecedent
precipitation in most of the world, Environ. Res. Lett., 12,
074016, https://doi.org/10.1088/1748-9326/aa7145, 2017. a, b
Pause, M., Schweitzer, C., Rosenthal, M., Keuck, V., Bumberger, J., Dietrich,
P., Heurich, M., Jung, A., and Lausch, A.: In Situ/Remote Sensing
Integration to Assess Forest Health – A Review, Remote Sens., 8, 471,
https://doi.org/10.3390/rs8060471, 2016. a
Petersen, L.: Real-Time Prediction of Crop Yields From MODIS Relative
Vegetation Health: A Continent-Wide Analysis of Africa, Remote Sens., 10,
1726, https://doi.org/10.3390/rs10111726, 2018. a
Rodríguez-Pérez, J. R., Ordóñez, C.,
González-Fernández, A. B., Sanz-Ablanedo, E., Valenciano, J. B.,
and Marcelo, V.: Leaf water content estimation by functional linear
regression of field spectroscopy data, Biosyst. Eng., 165, 36–46,
https://doi.org/10.1016/J.BIOSYSTEMSENG.2017.08.017, 2018. a, b
Saji, N. H. and Yamagata, T.: Possible impacts of Indian Ocean Dipole mode
events on global climate, Clim. Res., 25, 151–169,
https://doi.org/10.3354/CR025151, 2003. a
Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T.: A dipole
mode in the tropical Indian Ocean, Nature, 401, 360–363,
https://doi.org/10.1038/43854, 1999. a
Saleska, S. R., Didan, K., Huete, A. R., and da Rocha, H. R.: Amazon forests
green-up during 2005 drought, Science, 318, p. 612,
https://doi.org/10.1126/science.1146663, 2007. a
Samanta, A., Ganguly, S., Hashimoto, H., Devadiga, S., Vermote, E., Knyazikhin,
Y., Nemani, R. R., and Myneni, R. B.: Amazon forests did not green-up during
the 2005 drought, Geophys. Res. Lett., 37, 5,
https://doi.org/10.1029/2009GL042154, 2010. a, b
Samanta, A., Ganguly, S., Vermote, E., Nemani, R. R., and Myneni, R. B.: Why
Is Remote Sensing of Amazon Forest Greenness So Challenging?, Earth
Interact., 16, 1–14, https://doi.org/10.1175/2012EI440.1, 2012. a
Szpakowski, D. M. and Jensen, J. L.: A review of the applications of remote
sensing in fire ecology, Remote Sens., 11, 2638, https://doi.org/10.3390/rs11222638, 2019. a
Teubner, I. E., Forkel, M., Camps-Valls, G., Jung, M., Miralles, D. G.,
Tramontana, G., van der Schalie, R., Vreugdenhil, M., Mösinger, L., and
Dorigo, W. A.: A carbon sink-driven approach to estimate gross primary
production from microwave satellite observations, Remote Sens.
Environ., 229, 100–113, https://doi.org/10.1016/J.RSE.2019.04.022, 2019. a
McKee, T. B., Doesken, N. J., and Kleist, J.:
The Relation of Drought Frequency and Duration to Time Scales,
Proceedings of the 8th Conference on Applied Climatology, Anaheim, California, 17–22 January 1993, 179–184, 1993. a
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W.,
Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8 km
NDVI dataset compatible with MODIS and SPOT vegetation NDVI data,
Int. J. Remote Sens., 26, 4485–4498,
https://doi.org/10.1080/01431160500168686, 2005. a, b
van der Schalie, R., de Jeu, R., Kerr, Y., Wigneron, J.,
Rodríguez-Fernández, N., Al-Yaari, A., Parinussa, R.,
Mecklenburg, S., and Drusch, M.: The merging of radiative transfer based
surface soil moisture data from SMOS and AMSR-E, Remote Sens.
Environ., 189, 180–193, https://doi.org/10.1016/J.RSE.2016.11.026, 2017. a, b
Van Der Schrier, G., Barichivich, J., Briffa, K. R., and Jones, P. D.: A
scPDSI-based global data set of dry and wet spells for 1901–2009, J.
Geophys. Res.-Atmos., 118, 4025–4048, https://doi.org/10.1002/jgrd.50355,
2013.
a, b
van Marle, M. J. E., van der Werf, G. R., de Jeu, R. A. M., and Liu, Y. Y.: Annual South American forest loss estimates based on passive microwave remote sensing (1990–2010), Biogeosciences, 13, 609–624, https://doi.org/10.5194/bg-13-609-2016, 2016. a
Vogelmann, J. E., Xian, G., Homer, C., and Tolk, B.: Monitoring gradual
ecosystem change using Landsat time series analyses: Case studies in selected
forest and rangeland ecosystems, Remote Sens. Environ., 122,
92–105, https://doi.org/10.1016/j.rse.2011.06.027, 2012. a
Vreugdenhil, M., Navacchi, C., Bauer-Marschallinger, B., Hahn, S.,
Steele-Dunne, S., Pfeil, I., Dorigo, W., and Wagner, W.: Sentinel-1 Cross
Ratio and Vegetation Optical Depth: A Comparison over Europe, Remote
Sens., 12, 3404, https://doi.org/10.3390/rs12203404, 2020. a
Wells, N., Goddard, S., and Hayes, M. J.: A self-calibrating Palmer Drought
Severity Index, J. Clim., 17, 2335–2351,
https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2, 2004. a
Wentz, F. J.: A well-calibrated ocean algorithm for special sensor microwave/imager, J. Geophys. Res.-Ocean., 102, 8703–8718,
https://doi.org/10.1029/96JC01751, 1997. a
Zeng, F.-W., Collatz, G., Pinzon, J., and Ivanoff, A.: Evaluating and
Quantifying the Climate-Driven Interannual Variability in Global Inventory
Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index
(NDVI3g) at Global Scales, Remote Sens., 5, 3918–3950,
https://doi.org/10.3390/rs5083918, 2013. a
Zhao, J., Lu, Z., Wang, L., and Jin, B.: Plant Responses to Heat Stress:
Physiology, Transcription, Noncoding RNAs, and Epigenetics, Int.
J. Mol. Sci., 22, 117, https://doi.org/10.3390/ijms22010117, 2020. a, b
Short summary
The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation...
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