Determination of rice canopy growth based on high resolution satellite images: a case study using RapidEye imagery in Korea

Mijeong Kim; Seungtaek Jeong; Jong-min Yeom; Hyun-ok Kim; Jonghan Ko; Mijeong Kim; Seungtaek Jeong; Jong-min Yeom; Hyun-ok Kim; Jonghan Ko

doi:10.3934/environsci.2016.4.631

AIMS Environmental Science

2016, Volume 3, Issue 4: 631-645. doi: 10.3934/environsci.2016.4.631

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Determination of rice canopy growth based on high resolution satellite images: a case study using RapidEye imagery in Korea

1.
Applied Plant Science, Chonnam National University, 77 Youngbong-ro, Buk-gu, Gwangju 500-757, ROK
2.
Earth Observation Research Team, Korea Aerospace Research Institute, 169-84 Gwahak-ro, Yuseong-gu, Daejeon 305-806, ROK

Received: 09 May 2016 Accepted: 08 October 2016 Published: 12 October 2016

Processing to correct atmospheric effects and classify all constituent pixels in a remote sensing image is required before the image is used to monitor plant growth. The raw image contains artifacts due to atmospheric conditions at the time of acquisition. This study sought to distinguish the canopy growth of paddy rice using RapidEye (BlackBridge, Berlin, Germany) satellite data and investigate practical image correction and classification methods. The RapidEye images were taken over experimental fields of paddy rice at Chonnam National University (CNU), Gwangju, and at TaeAn, Choongcheongnam-do, Korea. The CNU RapidEye images were used to evaluate the atmospheric correction methods. Atmospheric correction of the RapidEye images was performed using three different methods, QUick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Atmospheric and Topographic Correction (ATCOR). To minimize errors in utilizing observed growth and yield estimation of paddy rice, the paddy fields were classified using a supervised classification method and normalized difference vegetation index (NDVI) thresholds, using the NDVI time-series features of the paddy fields. The results of the atmospheric correction using ATCOR on the satellite images were favorable, which correspond to those from reference UAV images. Meanwhile, the classification method using the NDVI threshold accurately classified the same pixels from each of the time-series images. We have demonstrated that the image correction and classification methods investigated here should be applicable to high resolution satellite images used in monitoring other crop growth conditions.
- atmospheric correction,
- classification,
- crop,
- remote sensing,
- satellite image
Citation: Mijeong Kim, Seungtaek Jeong, Jong-min Yeom, Hyun-ok Kim, Jonghan Ko. Determination of rice canopy growth based on high resolution satellite images: a case study using RapidEye imagery in Korea[J]. AIMS Environmental Science, 2016, 3(4): 631-645. doi: 10.3934/environsci.2016.4.631

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Abstract

Processing to correct atmospheric effects and classify all constituent pixels in a remote sensing image is required before the image is used to monitor plant growth. The raw image contains artifacts due to atmospheric conditions at the time of acquisition. This study sought to distinguish the canopy growth of paddy rice using RapidEye (BlackBridge, Berlin, Germany) satellite data and investigate practical image correction and classification methods. The RapidEye images were taken over experimental fields of paddy rice at Chonnam National University (CNU), Gwangju, and at TaeAn, Choongcheongnam-do, Korea. The CNU RapidEye images were used to evaluate the atmospheric correction methods. Atmospheric correction of the RapidEye images was performed using three different methods, QUick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Atmospheric and Topographic Correction (ATCOR). To minimize errors in utilizing observed growth and yield estimation of paddy rice, the paddy fields were classified using a supervised classification method and normalized difference vegetation index (NDVI) thresholds, using the NDVI time-series features of the paddy fields. The results of the atmospheric correction using ATCOR on the satellite images were favorable, which correspond to those from reference UAV images. Meanwhile, the classification method using the NDVI threshold accurately classified the same pixels from each of the time-series images. We have demonstrated that the image correction and classification methods investigated here should be applicable to high resolution satellite images used in monitoring other crop growth conditions.

References

[1]	Haboudane D, Miller JR, Tremblay N, et al. (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote sens environ 81: 416-426.
[2]	Berni J, Zarco-Tejada PJ, Suárez L, et al. (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47: 722-738.
[3]	Bauer ME, Cipra JE (1973) Identification of Agricultural Crops by Computer Processing of ERTS MSS Data. Symp. on Significant Results Obtained from the Earth Resources Technology Satellite-1. NASA SP-327. NASA Goddard Space Flight Center: 205-212.
[4]	Lawrence SB, Xuemin J, Brian G, et al. (2012) Quick atmospheric correction code: algorithm description and recent upgrades. Opt Eng 51: 111719. doi: 10.1117/1.OE.51.11.111719
[5]	Richter R (2005) Hyperspectral sensors for military applications. NASA technical report RTO-MP-SET-094, 2005.
[6]	Gao BC, Heidebrecht KB, Goetz AFH (1993) Derivation of scaled surface reflectance from AVIRIS data. Remote Sens Environ 44: 145-163. doi: 10.1016/0034-4257(93)90013-N
[7]	Richter R (1996) A spatially adaptive fast atmosphere correction algorithm. Int J Remote Sens 11: 159-166.
[8]	Adler-Golden S, Berk A, Bernstein LS, et al. (1998) FLAASH, A MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations. In Proc 7th Ann JPL Airborne Earth Science Workshop: 9-14.
[9]	Tuominen J, Lipping T (2011) Detection of environmental change using hyperspectral remote sensing at Olkiluoto repository site. Working Report, Posiva Oy, Eurajoki, Finland, 31-34.
[10]	Jones HG, Vaughan RA (2010) Remote sensing of vegetation: principles, techniques, and applications. Oxford university press.
[11]	Richards JA, Jia X (2006) Remote sensing digital image analysis, 4th ed. Berlin et al. Springer 78: 193.
[12]	Kim H, Yeom J (2014) Sensitivity of vegetation indices to spatial degradation of RapidEye imagery for paddy rice detection: a case study of South Korea. GISci Remote Sens 52: 1-17. doi: 10.1109/TGRS.2013.2290671
[13]	RapidEye AG (2011) Satellite imagery product specifications, Version 2.1.
[14]	Bernstein LS, Jin X, Gregor B, et al. (2012) Quick atmospheric correction code: algorithm description and recent upgrades. Optical engineering 51: 111719-1. doi: 10.1117/1.OE.51.11.111719
[15]	ENVI (2009) Atmospheric Correction Module: QUAC and FLAASH User's Guide, Version 4. 7. ITT Visual Information Solutions, Boulder, CO.
[16]	Black M, Fleming A, Riley T, et al. (2014) On the atmospheric correction of antarctic airborne hyperspectral data. Remote Sensing 6: 4498-4514. doi: 10.3390/rs6054498
[17]	ERDAS, Geosystems (2009) ATCOR for ERDAS IMAGINE 2010—Haze reduction, atmospheric and topographic correction—User manual ATCOR 2 and ATCOR 3. ERDAS Imagine 1–58. ERDAS–GeoSystems.
[18]	Liang S (2005) Quantitative remote sensing of land surfaces. John Wiley & Sons. Inc., New York.
[19]	Mujumdar PP, Kumar DN (2013) Floods in a changing climate: hydrologic modeling. International Hydrology Series, Cambridge University Press, Cambridge.
[20]	Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation (No. Ed. 5). John Wiley & Sons. Inc. New York.
[21]	Xiao X, Boles S, Frolking S, et al. (2006) Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ 100: 95-113. doi: 10.1016/j.rse.2005.10.004
[22]	Nash J, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J hydrology 10: 282-290. doi: 10.1016/0022-1694(70)90255-6
[23]	ENVI (2004) ENVI user’s guide. Research system Inc. Available from: http://aviris.gl.fcen.uba.ar/Curso_SR/biblio_sr/ENVI_userguid.pdf.
[24]	Thomas V, Treitz P, Jelinski D, et al. (2002) Image classification of a northern peatland complex using spectral and plant community data. Remote Sens Environ 84: 83-99.
[25]	Moses WJ, Gitelson AA, Perk RL, et al. (2012) Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data. Water research 46: 993-1004. doi: 10.1016/j.watres.2011.11.068
[26]	Jeong ST, Jang KC, Hong SY, et al. (2011) Detection of irrigation timing and the mapping of paddy cover in Korea using MODIS images data. Kor J Agric Forest Meteo 13: 69-78.

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