RESEARCH PAPER
Differences in vegetation index values using measurements from two azimuth and multiple zenith viewing angles
,
 
,
 
Yuna Cho 1,2
,
 
Euni Jo 1,2
,
 
,
 
,
 
,
 
,
 
,
 
Jaeil Cho 1,2
 
 
 
More details
Hide details
1
Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Republic of Korea
 
2
BK21 FOUR Center for IT-Bio Convergence System Agriculture, Chonnam National University, Gwangju 61186, Republic of Korea
 
3
National Institute of Crop Sciences, Rural Development Administration, Wanju 55365, Republic of Korea
 
4
National Institute of Agricultural Sciences, Rural Development Administration, Wanju 55365, Republic of Korea
 
5
Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea
 
 
Final revision date: 2023-12-17
 
 
Acceptance date: 2023-12-27
 
 
Publication date: 2024-01-31
 
 
Corresponding author
Jaeil Cho   

Applied Plant Science, Chonnam National University, Korea (South)
 
 
Int. Agrophys. 2024, 38(1): 77-86
 
HIGHLIGHTS
  • To investigate how crop surface reflectance and vegetation indices varied according to the direction of the light source and sensor viewing, a hyper-spectrometer of visible to near-infrared wavelengths mounted on a field goniometer was used at four growth stages in rice paddies. The NDVI was less sensitive to the directions of sensor viewing than the EVI.
KEYWORDS
TOPICS
ABSTRACT
Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, it has been determined that the anisotropic properties of the crop canopy surface can govern both the spectral reflectance and vegetation indices. In this study, in order to investigate how crop surface reflectance and vegetation indices varied according to the direction of the light source and sensor viewing, a hyper-spectrometer of visible to near-infrared wavelengths mounted on a field goniometer was used at vegetative and reproductive growth stages in rice paddy. It was found that most of the wavelength reflectance measurements produced by the sparse vegetation cover fraction were not sensitive to solar-illumination and sensor-viewing angles. In addition, the reflectance of visible wavelengths was found to be less sensitive to the solar and sensor angles than the red-edge and near-infrared wavelengths. The lowest normalized difference vegetation index value in a day occurred at the nadir sensor-viewing angle before rice heading, but after heading, when ripened grains began to bow, the lowest value was recorded at the sensor zenith angle of 25°. Enhanced vegetation index measurements were found to be more sensitive to the direction of sensor viewing and less affected by sun glint than normalized difference vegetation index measurements. Additional field observation measurements should increase our level of understanding of how vegetation indices change on anisotropic crop surfaces.
FUNDING
This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. RS-2021-RD009991)" by the Rural Development Administration.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
REFERENCES (29)
1.
Campos I., Neale C.M., López M.L., Balbontín C., and Calera A., 2014. Analyzing the effect of shadow on the relationship between ground cover and vegetation indices by using spectral mixture and radiative transfer models. J. Appl. Remote Sens., 8(1), 83562-83562, https://doi.org/10.1117/1.JRS.....
 
2.
Dash J., and Curran P.J., 2007. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv. Space Res., 39(1), 100-104, https://doi.org/10.1016/j.asr.....
 
3.
Gamon J.A., Penuelas J., and Field C.B., 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Envion., 41(1), 35-44, https://doi.org/10.1016/0034-4....
 
4.
Gamon J.A., Huemmrich K.F., Wong C.Y., Ensminger I., Garrity S., Hollinger D.Y., Noormets A., and Peñuelas J., 2016. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proc. Natl. Acad. Sci., 113(46), 13087-13092, https://doi.org/10.1073/pnas.1....
 
5.
Gao F., Schaaf C.B., Strahler A.H., Jin Y., and Li X., 2003. Detecting vegetation structure using a kernel-based BRDF model. Remote Sens. Environ., 86(2): 198-205, https://doi.org/10.1016/S0034-....
 
6.
Jain S.K. and Singh V.P., 2003. Developments in water science. Elsevier, Amst., The Netherlands, 5, 123-205, https://doi.org/10.1016/S0167-....
 
7.
Kamble B., Kilic A., and Hubbard K., 2013. Estimating crop coefficients using remote sensing-based vegetation index. Remote Sens., 5(4), 1588-1602, https://doi.org/10.3390/rs5041....
 
8.
Lee K., Park C.W., Na S.I., Jung M.P., and Kim J., 2017. Monitoring on crop condition using remote sensing and model. Korean J. Remote Sens., 33(5-2), 617-620, https://doi.org/10.7780/kjrs.2....
 
9.
Li F., Jupp D.L.B., Reddy S., Lymburner L., Mueller N., Tan P., and Islam A., 2010. An evaluation of the use of atmospheric and BRDF correction to standardize landsat data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 3(3), 257-270, https://doi.org/10.1109/JSTARS....
 
10.
Li W., Jiang J., Weiss M., Madec S., Tison F., Philippe B., Comar A., and Baret F., 2021. Impact of the reproductive organs on crop BRDF as observed from a UAV. Remote Sens. Environ., 259(15), 112433, https://doi.org/10.1016/j.rse.....
 
11.
Liu H.Q. and Huete A.R., 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens., 33, 457-465, https://doi.org/10.1109/TGRS.1....
 
12.
Ma D., Rehman T.U., Zhang L., Maki H., Tuinstra M.R., and Jin J., 2021. Modeling of diurnal changing patterns in airborne crop remote sensing images. Remote Sens., 13(9), 1719, https://doi.org/10.3390/rs1309....
 
13.
Matsushita B., Yang W., Chen J., Onda Y., and Qiu G., 2007. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11), 2636-2651, https://doi.org/10.3390/s71126....
 
14.
Na S.I., Park C.W., Cheong Y.K., Kang C.S., Choi I.B., and Lee K.D., 2016. Selection of optimal vegetation indices for estimation of barley wheat growth based on remote sensing. Korean J. Remote Sens., 32(5), 483-497, https://doi.org/10.7780/kjrs.2....
 
15.
Ortega-Terol D., Hernandez-Lopez D., Ballesteros R., and Gonzalez-Aguilera D., 2017. Automatic hotspot and sun glint detection in UAV multispectral images. Sensors, 17(10), 2352, https://doi.org/10.3390/s17102....
 
16.
Qiu B., Guo W., Xue Y., and Dai Q., 2016. Implementation and evaluation of a generalized radiative transfer scheme within canopy in the soil-vegetation-atmosphere transfer (SVAT) model. J. Geophys. Res. Atmos., 121(20), 12145-12163, https://doi.org/10.1002/2016JD....
 
17.
Queally N., Ye Z., Zheng T., Chlus A., Schneider F., Pavlick R.P., and Townsend P.A., 2022. FlexBRDF: A flexible BRDF correction for grouped processing of airborne imaging spectroscopy flightlines. J. Geophys. Res.: Biogeosci., 127(1), e2021JG006622, https://doi.org/10.1029/2021JG....
 
18.
Ryu J.H., Jeong H., and Cho J., 2020. Performances of vegetation indices on paddy rice at elevated air temperature, heat stress, and herbicide damage. Remote Sens., 12(16), 2654, https://doi.org/10.3390/rs1216....
 
19.
Sandmeier S.R. and Itten K.I., 1999. A field goniometer system (FIGOS) for acquisition of hyperspectral BRDF data. IEEE Trans. Geosci. Remote Sens., 37(2), 978-986, https://doi.org/10.1109/36.752....
 
20.
Schill S.R., Jensen J.R., Raber G.T., and Porter D.E., 2004. Temporal modeling of bidirectional reflection distribution function (BRDF) in coastal vegetation. GIsci. Remote Sens., 41(2), 116-135, https://doi.org/10.2747/1548-1....
 
21.
Shafian S., Rajan N., Schnell R., Bagavathiannan M., Valasek J., Shi Y., and Olsenholler J., 2018. Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development. PLoS one, 13(5), e0196605, https://doi.org/10.1371/journa....
 
22.
Sharifi A., 2020. Using sentinel-2 data to predict nitrogen uptake in maize crop. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 13, 2656-2662, https://doi.org/10.1109/JSTARS....
 
23.
Sinclair T.R. and Sheehy J.E., 1999. Erect leaves and photosynthesis in rice. Science, 283(5407), 1455-1455, https://doi.org/10.1126/scienc....
 
24.
Sun T., Fand H., Liu W., and Ye Y., 2017. Impact of water background on canopy reflectance anisotropy of a paddy rice field from multi-angle measurements. Agric. For. Meteorol., 233(15), 143-152, https://doi.org/10.1016/j.agrf....
 
25.
Suomalainen J., Hakala T., Peltoniemi J., and Puttonen E., 2009. Polarised multiangular reflectance measurements using the finnish geodetic institute field goniospectrometer. Sensors, 9(5), 3891-3907, https://doi.org/10.3390/s90503....
 
26.
Towers P.C., and Poblete-Echeverría C., 2021. Effect of the illumination angle on NDVI data composed of mixed surface values obtained over vertical-shoot-positioned vineyards. Remote Sens., 13(5), 855, https://doi.org/10.3390/rs1305....
 
27.
Yan J., Zhang G., Ling H., and Han F., 2022. Comparison of time-integrated NDVI and annual maximum NDVI for assessing grassland dynamics. Ecol. Indic., 136, 108611, https://doi.org/10.1016/j.ecol....
 
28.
Zhang Q., Cheng Y.B., Lyapustin A.I., Wang Y., Xiao X., Suyker A., Verma S., Tan B., and Middleton E.M., 2014. Estimation of crop gross primary production (GPP): I. Impact of MODIS observation footprint and impact of vegetation BRDF characteristics. Agric. For. Meteorol., 191(15), 51-63, https://doi.org/10.1016/j.agrf....
 
29.
Zhang Y., Liu X., Su S., and Wang C., 2014. Retrieving canopy height and density of paddy rice from Radarsat-2 images with a canopy scattering model. Int. J. Appl. Earth Obs. Geoinf., 28, 170-180, https://doi.org/10.1016/j.jag.....
 
eISSN:2300-8725
ISSN:0236-8722
Journals System - logo
Scroll to top