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Rice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystems
K. Choudhary 1,2, W. Shi 1, Y. Dong 1,3

1 Department of Land Surveying and Geo-informatics, Smart Cities Research Institute,
The Hong Kong Polytechnic University, Hong Kong,
2 Samara National Research University, Moskovskoye Shosse 34, Samara, 443086, Russia,
3 Institute of Geophysics & Geomatics, China University of Geoscience, Wuhan, PR China

 PDF, 4146 kB

DOI: 10.18287/2412-6179-CO-827

Pages: 438-448.

Full text of article: English language.

Abstract:
Crop growth is one of the most important parameters of a crop and its knowledge before harvest is essential to help farmers, scientists, governments and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate rice crop growth in a single year. Sentinel 2 data provides frequent and consistent information to facilitate coastal monitoring from field scales. The aims of this study were to modify the rice growth vegetation index to improve rice growth phenology in the coastal areas. The rice growth vegetation index 2 is the best vegetation index, compared with 11 vegetation indices, plant height and biomass. The results demonstrate that the coefficient of rice growth vegetation index 2 was 0.83, has the highest correlation with plant height. Rice growth vegetation index 2 is more appropriate for enhancing and obtaining rice phenology information. This study analyses the best spectral vegetation indices for estimating rice growth.

Keywords:
crop growth, spectral indices, phenology, rice growth vegetation index 2.

Citation:
Choudhary K, Shi W, Dong Y. Rice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystems. Computer Optics 2021; 45(3): 438-448. DOI: 10.18287/2412-6179-CO-827.

Acknowledgements:
This work is supported by the Hong Kong PhD scholarship from PolyU and research grants from the Research Grants Council of (HKSAR) grant project codes B-Q49D and 1-ZVE8. Authors would also like to acknowledge the support drawn from the Agriculture department of Guangdong, China.

References:
  1. Bouman BAM, Van Laar HH. Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agric Syst 2006; 87(3): 249-273.
  2. Zhou G, Liu X, Liu M. Assimilating remote sensing phenological information into the WOFOST model for rice growth simulation. Remote Sens 2019; 11(3): 268.
  3. Wang F, Wang F, Zhang Y, Hu J, Huang J, Xie J. Rice yield estimation using parcel-level relative spectral variables from uav-based hyperspectral imagery. Front Plant Sci 2019; 10(April): 1-12.
  4. Ministry of Agriculture of the People’s Republic of China. 28 April 2012. The 12thFive Year Plan. <http://english.agri.gov.cn/>.
  5. Duncan JMA, Dash J, Atkinson PM. The potential of satellite-observed crop phenology to enhance yield gap assessments in smallholder landscapes. Front Environ Sci 2015; 3(AUG): 1-16.
  6. Singha M, Wu B, Zhang M. An object-based paddy rice classification using multi-spectral data and crop phenology in Assam, Northeast India. Remote Sens 2016; 8(6): 479.
  7. Ren S, Chen X, An S. Assessing plant senescence reflectance index-retrieved vegetation phenology and its spatiotemporal response to climate change in the Inner Mongolian Grassland. Int J Biometeorol 2017; 61(4): 601-612.
  8. Choudhary K, Shi W, Boori MS, Corgne S. Agriculture phenology monitoring using NDVI time series based on remote sensing satellites: A case study of Guangdong, China. Optical Memory and Neural Networks 2019; 28(3): 204-214.
  9. Bajocco S, Raparelli E, Teofili T, Bascietto M, Ricotta C. Text mining in remotely sensed phenology studies: A review on research development, main topics, and emerging issues. Remote Sens 2019; 11(23): 2751.
  10. Jin X, et al. A review of data assimilation of remote sensing and crop models. Eur J Agron 2018; 92(November): 141-152.
  11. Naito H, et al. Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras. ISPRS J Photogramm Remote Sens 2017; 125: 50-62.
  12. Cui B, Zhao Q, Huang W, Song X, Ye H, Zhou X. A new integrated vegetation index for the estimation of winter wheat leaf chlorophyll content. Remote Sens 2019; 11(8): 1-18.
  13. Boori MS, Choudhary K, Kupriyanov AV. Crop growth monitoring through sentinel and landsat data based ndvi time-series. Computer Optics 2020; 44(3): 409-419. DOI: 10.18287/2412-6179-CO-635.
  14. Nellis MD, Price KP, Rundquist D. Remote sensing of cropland agriculture. SAGE Handb Remote Sens 2008: 368-383.
  15. Boschetti M, Stroppiana D, Brivio PA, Bocchi S. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. Int J Remote Sens 2009; 30(18): 4643-4662.
  16. Prabhakar M, Gopinath KA, Reddy AGK, Thirupathi M, Rao CS. Mapping hailstorm damaged crop area using multispectral satellite data. Egypt J Remote Sens Sp Sci 2019; 22(1): 73-79.
  17. Singha M, Wu B, Zhang M. Object-based paddy rice mapping using HJ-1A/B data and temporal features extracted from time series MODIS NDVI data. Sensors (Switzerland) 2017; 17(1): 10.
  18. Huang Q, Zhang L, Wu W, Li D. MODIS-NDVI-based crop growth monitoring in China Agriculture Remote Sensing Monitoring System. 2010 2nd IITA Int Conf Geosci Remote Sensing (IITA-GRS 2010) 2010; 2: 287-290.
  19. Yu Q, Xiang M, Wu W, Tang H. Changes in global cropland area and cereal production: An inter-country comparison. Agric Ecosyst Environ 2019; 269: 140-147.
  20. Naito H, et al. Estimating rice yield related traits and quantitative trait loci analysis under different nitrogen treatments using a simple tower-based field phenotyping system with modified single-lens reflex cameras. ISPRS J Photogramm Remote Sens 2017; 125: 50-62.
  21. Chen J, Huang J, Hu J. Mapping rice planting areas in southern China using the China Environment Satellite data. Math Comput Model 2011; 54(3-4): 1037-1043.
  22. Guo J, Mao K, Zhao Y, Lu Z, Xiaoping L. Impact of climate on food security in mainland China: A new perspective based on characteristics of major agricultural natural disasters and grain loss. Sustain 2019; 11(3): 1-27.
  23. Onojeghuo AO, Blackburn GA, Huang J, Kindred D, Huang W. Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities. Int J Appl Earth Obs Geoinf 2018; 64(v): 62-86.
  24. Chemura A, Mutanga O, Dube T. Separability of coffee leaf rust infection levels with machine learning methods at Sentinel-2 MSI spectral resolutions. Precis Agric 2017; 18(5): 859-881.
  25. Escolà A, Badia N, Arnó J, Martínez-Casasnovas JA. Using Sentinel-2 images to implement Precision Agriculture techniques in large arable fields: First results of a case study. Adv Anim Biosci 2017; 8(2): 377-382.
  26. Pan H, Chen Z, de Allard W, Ren J. Joint assimilation of leaf area index and soil moisture from Sentinel-1 and Sentinel-2 data into the WOFOST model for winter wheat yield estimation. Sensors (Switzerland) 2019; 19(14): 3161.
  27. Li L, Zha Y. Estimating monthly average temperature by remote sensing in China. Adv Sp Res 2019; 63(8): 2345-2357.
  28. Shen W, Li M, Huang C, He T, Tao X, Wei A. Local land surface temperature change induced by afforestation based on satellite observations in Guangdong plantation forests in China. Agric For Meteorol 2019; 276-277(June): 107641.
  29. Smith B. The emergence of agriculture. New York, NY: WH Freeman and Co; 1994.
  30. Nuarsa IW, Nishio F, Hongo C. Spectral characteristics and mapping of rice plants using multi-temporal Landsat data. J Agric Sci 2011; 3(1).
  31. Yang W, et al. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems. Remote Sens Environ 2019; 228(June): 31-44.
  32. Li L, Friedl MA, Xin Q, Gray J, Pan Y, Frolking S. Mapping crop cycles in China using MODIS-EVI time series. Remote Sens 2014; 6(3): 2473-2493.
  33. Boschetti M, et al. PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. Remote Sens Environ 2017; 194: 347-365.
  34. Huang J, Rozelle S. China’s 40 years of agricultural development and reform. China’s 40 Years Reform Dev 1978-2018 2018; July: 487-506.
  35. Karthikeyan L, Chawla I, Mishra AK. A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. J Hydrol 2020; 586(March): 124905.
  36. Motohka T, Nasahara KN, Oguma H, Tsuchida S. Applicability of Green-Red Vegetation Index for remote sensing of vegetation phenology. Remote Sens 2010; 2(10): 2369-2387.
  37. Kaur R, Singh B, Singh M, Thind SK. Hyperspectral indices, correlation and regression models for estimating growth parameters of wheat genotypes. J Indian Soc Remote Sens 2015; 43(3): 551-558.
  38. Wang J, et al. Field-scale rice yield estimation using sentinel-1A synthetic aperture radar (SAR) data in coastal saline region of Jiangsu Province, China. Remote Sens 2019; 11(19): 1-9.
  39. Agapiou A, Hadjimitsis DG, Sarris A, Georgopoulos A, Alexakis DD. Optimum temporal and spectral window for monitoring crop marks over archaeological remains in the Mediterranean region. J Archaeol Sci 2013; 40(3): 1479-1492.
  40. Susantoro TM, Wikantika K, Saepuloh A, Harsolumakso AH. Selection of vegetation indices for mapping the sugarcane condition around the oil and gas field of North West Java Basin, Indonesia. IOP Conf Ser Earth Environ Sci 2018; 149(1): 0-1.

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