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2021 Vol.63, Issue 6 Preview Page
A Comparative Analysis of Vegetation and Agricultural Monitoring of Terra MODIS and Sentinel-2 NDVIs

Terra MODIS 및 Sentinel-2 NDVI의 식생 및 농업 모니터링 비교 연구

Moo-Been Sona
,
Jee-Hun Chungb†
,
Yong-Gwan Leec
,
Seong-Joon Kimd
손 무빈a
,
정 지훈b†
,
이 용관c
,
김 성준d
aGraduate Student, Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University
bPh.D. Student, Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University
cPh.D. Candidate, Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University
dProfessor, Division of Civil and Environmental Engineering, College of Engineering, Konkuk University
a건국대학교 대학원 토목환경플랜트공학과 대학원생
b박사 건국대학교 대학원 토목환경플랜트공학과 학생
c박사 건국대학교 대학원 토목환경플랜트공학과 후보자
d건국대학교 공과대학 건설환경공학부 교수
† Corresponding author Tel.: +82-2-450-3749, Fax: +82-2-444-0186 E-mail: gop1519@konkuk.ac.kr
2021. pp. 101-115
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Abstract

The purpose of this study is to evaluate the compatibility of the vegetation index between the two satellites and the applicability of agricultural monitoring by comparing and verifying NDVI (Normalized Difference Vegetation Index) based on Sentinel-2 and Terra MODIS (Moderate Resolution Imaging Spectroradiometer). Terra MODIS NDVI utilized 16-day MOD13Q1 data with 250 m spatial resolution, and Sentinel-2 NDVI utilized 10-day Level-2A BOA (Bottom Of Atmosphere) data with 10 m spatial resolution. To compare both NDVI, Sentinel-2 NDVIs were reproduced at 16-day intervals using the MVC (Maximum Value Composite) technique. As a result of time series NDVIs based on two satellites for 2019 and compare by land cover, the average R2 (Coefficient of determination) and RMSE (Root Mean Square Error) of the entire land cover were 0.86 and 0.11, which indicates that Sentinel-2 NDVI and MODIS NDVI had a high correlation. MODIS NDVI is overestimated than Sentinel-2 NDVI for all land cover due to coarse spatial resolution. The high-resolution Sentinel-2 NDVI was found to reflect the characteristics of each land cover better than the MODIS NDVI because it has a higher discrimination ability for subdivided land cover and land cover with a small area range.

Keywords
Terra MODIS
MVC
NDVI
remote sensing
Sentinel-2
References

REFERENCES

1
J. Amorós-López, L. Gómez-Chova, L. Alonso, L. Guanter, R. Zurita-Milla, J. Moreno and G. Camps-Valls, International Journal of Applied Earth Observation and Geoinformation, Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring, 23; 132-141 (2013)10.1016/j.jag.2012.12.004.
2
J. Bukowiecki, T. Rose and H. Kage, Sensors, Sentinel-2 Data for Precision Agriculture?-A UAV-based assessment, 21(8); 2861 (2021)10.3390/s21082861
3
S. Y. Cha, D. J. Seo and C. H. Park, Korean Journal of Remote Sensing, Monitoring Vegetation Phenology using MODIS in Northern Plateau region, North Korea, 25(5); 399-409, (in Korean) (2009)
4
S. H. Chae, S. H. Park and M. J. Lee, Korean Journal of Remote Sensing, A study on the observation of soil moisture conditions and its applied possibility in agriculture using land surface temperature and NDVI from Landsat-8 OLI/TIRS satellite image, 33(6-1); 931-946, (in Korean) (2017)10.7780/kjrs.2017.33.6.1.3.
5
A. Chakhar, D. Hernández-López, R. Ballesteros and M. A. Moreno, Remote Sensing, Improving the accuracy of multiple algorithms for crop classification by integrating Sentinel-1 observations with Sentinel-2 data, 13(2); 243 (2021)10.3390/rs13020243
6
J. Chen, P. Jonsson, M. Tamura, Z. Gu, B. Matsushita and L. Eklundh, Remote Sensing of Environment, A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter, 91(3); 332-344 (2004)
7
X. Chen, D. Wang, J. Chen, C. Wang and M. Shen, Remote Sensing of Environment, The mixed pixel effect in land surface phenology: A simulation study, 211; 338-344 (2018)10.1016/j.rse.2018.04.030
8
S. Choi, S. Lee and B. Wang, Journal of the Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography, Analysis of Vegetation Cover Fraction on Landsat OLI using NDVI, 32(1); 9-17, (in Korean) (2014)10.7848/ksgpc.2014.32.1.9
9
Chung J., Lee Y., Jang W., Lee S. and Kim S., Remote Sensing, Correlation analysis between air temperature and MODIS land surface temperature and prediction of air temperature using TensorFlow long short-term memory for the period of occurrence of cold and heat waves, 12(19); 3231 (2020)10.3390/rs12193231
10
K. Didan, A. B. Munoz, R. Solano and A. Huete, MODIS vegetation index user’s guide(MOD13 series), University of Arizona. Vegetation Index and Phenology Lab. (2015)
11
P. C. Doraiswamy, J. L. Hatfield, T. J. Jackson, B. Akhmedov, J. Prueger and A. Stern, Remote Sensing of Environment, Crop condition and yield simulations using Landsat and MODIS, 92(4); 548-559 (2004)10.1016/j.rse.2004.05.017
12
ESA, Spectral bands for the SENTINEL-2 sensors, https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument. Accessed 7 Aug. 2021.
13
M. Guerini Filho, T. M. Kuplich and F. L. D. Quadros, International Journal of Remote Sensing, Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data, 41(8); 2861-2876 (2020)10.1080/01431161.2019.1697004
14
J. Y. Hong, C. S. Shim, M. J. Lee, G. H. Baek, W. K. Song, S. W. Jeon and Y. H. Park, Korean Journal of Remote Sensing, Net primary production changes over Korea and climate factors, 27(4); 467-480 (2011)10.7780/kjrs.2011.27.4.467
15
S. Jamshidi, S. Zand-Parsa and D. Niyogi, International Journal of Remote Sensing, Assessing crop water stress index of citrus using in-situ measurements, Landsat, and Sentinel-2 Data, 42(5); 1893-1916 (2021)10.1080/01431161.2020.1846224
16
Z.8 Jiang, A. R. Huete, J. Chen, Y. Chen, J. Li, G. Yan and X. Zhang, Remote Sensing of Environment, Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction, 101(3); 366-378 (2006)10.1016/j.rse.2006.01.003
17
Land and Climate Data, https://www.jinan.go.kr/stats/synap/skin/doc.html?fn=161942663069452.xlsx&rs=/upload_data/Synap/BBS_0000114/&cpath=%2Fstats. Accessed 7 Aug. 2021., Jinan Country. (2020)
18
M. H. Kalubarme, M. B. Potdar, K. R. Manjunath, R. K. Mahey and S. S. Siddhu, International Journal of Remote Sensing, Growth profile based crop yield models: a case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data, 24(10); 2037-2054 (2003)10.1080/01431160210156018
19
B. S. Kim, M. S. Park, S. W. Song and B. K. Min, Korea Rural Economic Institute, A Pilot Study on Estimating Rice Acreage with Satellite Images, (in Korean) (2010)
20
D. S. Kim, Journal of the Korean Geographical Society, Korean vegetation types using NOAA/AVHRR data, 35(1); 39-51, (in Korean) (2000)
21
G. H. Kim, J. S. Lee, J. H. Jung and S. Y. Won, Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography, A comparative analysis of field surveying vegetation data and NDVI from KOMPSAT-2 satellite imagery, 29(4); 405-411 (2011)10.7848/ksgpc.2011.29.4.405
22
H. O. Kim, J. M. Yeom and Y. S. Kim, Aerospace Engineering and Technology, The multi-temporal characteristics of spectral vegetation indices for agricultural land use on RapidEye satellite imagery, 10(1); 149-155, (in Korean) (2011)
23
Y. Kim and N. W. Park, Korean Journal of Remote Sensing, Comparison of spatio-temporal fusion models of multiple satellite images for vegetation monitoring, 35(6-3); 1209-1219, (in Korean) (2019)10.7780/kjrs. 2019.35.6.3.5.
24
E. B. Knipling, Remote Sensing of Environment, Physical, and physiological basis for the reflectance of visible and near-infrared radiation from vegetation, 1(3); 155-159 (1970)10.1016/S0034-4257(70)80021-9.
25
G. S. Lee, T. J. Kim, N. W. Park, J. H. Lee, E. M. Jang, B. G. Kang, G. H. Gwak, H. S. Lee, Y. H. Jeong, H. G. Kim, Y. Y. Park, D. J. Son, Y. S. Yoon, J. W. Jeong and S. R. Hwang, Rural Development Administration, Planning the payload system development for the Agriculture and Forestry Satellite and the establishment and operation of the Aerospace Image Applications Center for Agriculture, (in Korean) (2018)10.23000/TRKO201800 043706.
26
J. W. Lee, G. Park, H. K. Joh, K. H. Lee, S. I. Na, J. H. Park and S. J. Kim, Journal of the Korean Society of Agricultural Engineers, Analysis of relationship between vegetation indices and crop yield using KOMPSAT(KoreaMulti-Purpose SATellite)-2 imagery and field investigation data, 53(3); 75-82, (in Korean) (2011)10.5389/ksae.2011.53.3.075
27
J. Lee, S. Kang, K. Jang and S. Y. Hong, Korean Journal of Remote Sensing, A comparative study for reconstructing a high-quality NDVI time series data derived from MODIS surface reflectance, 31(2); 149-160 (2015)10.7780/kjrs.2015.31.2.9.
28
K. S. Lee and K. S. Han, Korean Journal of Remote Sensing, An improved method of NDVI correction through pattern-response low-peak detection on time series, 30(4); 505-510, (in Korean) (2014)10.7780/kjrs. 2014.30.4.8.
29
S. Lee, K. Kim, Y. Kim, J. Kim and Y. Lee, Journal of the Association of Korean Photo-geographers, Development of FBI (Fire Burn Index) for Sentinel-2 images and an experiment for detection of burned areas in Korea, 27(4); 187-202, (in Korean) (2017)10.35149/jakpg.2017.27.4.012
30
Y. G. Lee, S. H. Kim, S. R. Ahn, M. H. Choi and K. S. Lim, Journal of the Korean Association of Geographic Information Studies, Estimation of spatial evapotranspiration using Terra MODIS satellite image and SEBAL model - A case of Yongdam dam watershed, 18(1); 90-104, (in Korean) (2015)10.11108/kagis.2015.18.1.090
31
J. Liu, T. Huffman, B. Qian, J. Shang, Q. Li, T. Dong and Q. Jing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Crop yield estimation in the Canadian prairies using Terra/MODIS-derived crop metrics, 13; 2685-2697 (2020)10.1109/JSTARS. 2020.2984158
32
M. S. Mkhabela, P. Bullock, S. Raj, S. Wang and Y. Yang, Agricultural and Forest Meteorology, Crop yield forecasting on the Canadian Prairies using MODIS NDVI data, 151(3); 385-393 (2011)10.1016/j.agrformet.2010.11.012
33
D. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel and T. L. Veith, Transactions of the ASABE, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, 50(3); 885-900 (2007)10.13031/2013.23153
34
U. Mueller-Wilm, O. Devignot and L. Pessiot, ESA, S2 MPC Sen2Cor Configuration and User Manual (No. S2-PDGS-MPC-L2A-SUM-V2. 4) (2017)
35
S. I. Na, C. W. Park, Y. K. Cheong, C. S. Kang, I. B. Choi and K. D. Lee, Korean Journal of Remote Sensing, Selection of optimal vegetation indices for estimation of barley & wheat growth based on remote sensing-An application of unmanned aerial vehicle and field investigation data, 32(5); 483-497 (2016)10.7780/kjrs.2016.32.5.7
36
S. I. Na, J. H. Park and J. K. Park, Journal of the Korean Society of Agricultural Engineers, Development of Korean Paddy Rice Yield Prediction Model (KRPM) using Meteorological Element and MODIS NDVI, 54(3); 141-148, (in Korean) (2012)10.5389/ksae.2012.54.3.141
37
NASA, Data Products of MODIS, https://modis.gsfc. nasa.gov/data/. Accessed 7 Aug. 2021.
38
Y. Oguro, Y. Suga, S. Takeuchi, M. Ogawa, T. Konishi and K. Tsuchiya, Advances in Space Research, Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima, 28(1); 195-200 (2001)10.1016/s0273-1177(01)00345-3
39
J. Park, J. Baik, S. J. Kim and M. Choi, Journal of Korea Water Resources Association, Application of normalized vegetation index for estimating hydrological factors in the Korea Peninsula from COMS, 47(10); 935-943, (in Korean) (2014)10.3741/jkwra.2014.47.10.935
40
S. Park, Journal of the Korean Geographical Society, Satellite-Measured Vegetation Phenology and Atmospheric Aerosol Time Series in the Korean Peninsula, 48(4); 497-508, (in Korean) (2013)
41
C. M. Rulinda, A. Dilo, W. Bijker and A. Stein, Journal of Arid Environments, Characterising and quantifying vegetative drought in East Africa using fuzzy modelling and NDVI data, 78; 169-178 (2012)10.1016/j.jaridenv.2011.11.016
42
T. Sakamoto and T. Sakamoto, ISPRS Journal of Photogrammetry and Remote Sensing, Incorporating environmental variables into a MODIS-based crop yield estimation method for United States corn and soybeans through the use of a random forest regression algorithm, 160; 208-228 (2020)10.1016/j.isprsjprs.2019.12.012
43
T. Sakamoto, M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka and H. Ohno, Remote Sensing of Environment, A crop phenology detection method using time-series MODIS data, 96(3); 366-374 (2005)10.1016/j.rse.2005.03.008
44
C. Santhi, J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan and L. M. Hauck, JAWRA Journal of the American Water Resources Association, Validation of the swat model on a large rwer basin with point and nonpoint sources, 37(5); 1169-1188 (2001)10.1111/j.1752-1688.2001.tb03630.x
45
J. Segarra, M. L. Buchaillot, J. L. Araus and S. C. Kefauver, Agronomy, Remote sensing for precision agriculture: Sentinel-2 improved features and applications, 10(5); 641 (2020)10.3390/agronomy10050641
46
N. H. Seong, D. Jung, J. Kim and K. S. Han, Asia-Pacific Journal of Atmospheric Sciences 1-10, Evaluation of NDVI estimation considering atmospheric and BRDF correction through Himawari-8/AHI (2020)10.1007/s13143-019-00167-0
47
Shin S. C. and An T. Y., Journal of the Korean Association of Geographic Information Studies, Development of estimating method for areal evapotranspiration using satellite data, 10(2); 71-81, (in Korean) (2007)
48
I. Sola, A. García-Martín, L. Sandonís-Pozo, J. Álvarez-Mozos, F. Pérez-Cabello, M. González-Audícana and R. M. Llovería, International Journal of Applied Earth Observation and Geoinformation, Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes, 73; 63-76 (2018)10.1016/j.jag.2018.05.020
49
R. Sonobe, Y. Yamaya, H. Tani, X. Wang, N. Kobayashi and K. I. Mochizuki, Journal of Applied Remote Sensing, Crop classification from Sentinel-2-derived vegetation indices using ensemble learning, 12(2); 026019 (2018)10.1117/1.jrs.12.026019
50
W. L. Stefanov and M. Netzband, Remote Sensing of Environment, Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center, 99(1-2); 31-43 (2005)10.1117/1.jrs.12.026019
51
Suh M. S., Lee J. R., Kang J. H., Lee D. K. and Ahn M. H., Journal of Korean Meteorological Society, On the relationship between seasonal change of vegetation and climate elements in East Asia, 41(4); 557-570, (in Korean) (2005)
52
J. R. Thomas and H. W. Gausman, Agronomy Journal, Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops 1, 69(5); 799-802 (1977)10.2134/ agronj1977.00021962006900050017x
53
C. J. Tucker, Remote Sensing of Environment, Red and photographic infrared linear combinations for monitoring vegetation, 8(2); 127-150 (1979)10.1016/0034-4257(79) 90013-0
54
C. J. Tucker and P. J. Sellers, International Journal of Remote Sensing, Satellite remote sensing of primary production, 7(11); 1395-1416 (1986)10.1080/0143116 8608948944
55
United States Geological Survey (USGS), , MOD13Q1 Data. https://lpdaac.usgs.gov/products/mod13q1v006. Accessed 7 Aug. 2021.
56
M. W. Van Liew, J. G. Arnold and J. D. Garbrecht, Transactions of the ASAE, Hydrologic simulation on agricultural watersheds: Choosing between two models, 46(6); 1539 (2003)10.13031/2013.15643
57
Wan Z., Wang P. and Li X., International Journal of Remote Sensing, Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA, 25(1); 61-72 (2004)10.1080/0143116031000115328
58
J. Xin, Z. Yu, L. van Leeuwen and P. M. Driessen, International Journal of Applied Earth Observation and Geoinformation, Mapping crop key phenological stages in the North China Plain using NOAA time series images, 4(2); 109-117 (2002)10.1016/S0303-2434(02)00007-7
59
C. Yang, J. H. Everitt and D. Murden, Computers and Electronics in Agriculture, Evaluating high resolution SPOT 5 satellite imagery for crop identification, 75(2); 347-354 (2011)
60
Yeom J. M., Han K. S., Lee C. S., Park Y. Y. and Kim Y. S., Journal of the Korean Association of Geographic Information Studies, A detection of vegetation variation over North Korea using SPOT/VEGETATION NDVI, 11(2); 28-37 (2008)
61
J. M. Yeom, S. Jeong, R. C. Deo and J. Ko, GIScience & Remote Sensing, Mapping rice area and yield in northeastern asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite, 58(1); 1-27 (2021)10.1080/15481603.2020.1853352
62
S. Y. Yoon, H. Choi and G. H. Kim, Korean Society for Geospatial Information Science, Estimation of Vegetation Cover Fraction Using Landsat 8 OLI imagery and Field Survey Data, 25(3); 95-100, (in Korean) (2017)10.7319/kogsis.2017.25.3.095
Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 63
  • No :6
  • Pages :101-115
  • Received Date : 2021-08-11
  • Revised Date : 2021-09-29
  • Accepted Date : 2021-11-18
  • DOI :https://doi.org/10.5389/KSAE.2021.63.6.101
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