Download fulltext HTML
Serrano, J., Fábrega, J., Quirós, E., Galán, J. S.-, & Jiménez, J. U. (2018). Análisis Prospectivo de la Detección Hiperespectral de Cultivos de Arroz (Oryza Sativa L.). KnE Engineering, 3(2), 69-79. https://doi.org/10.18502/keg.v3i1.1414

https://doi.org/10.18502/keg.v3i1.1414

statistics

  • 790 Abstract Views
  • 680 PDF Views
  • 0 HTML Views

authors

  • Jorge Serrano

    Jorge Serrano

    Universidad Tecnológica de Panamá
  • José Fábrega

    José Fábrega

    Universidad Tecnológica de Panamá
  • Evelyn Quirós

    Evelyn Quirós

    Instituto de Investigación Agropecuaria de Panamá, Penonomé, Coclé
  • Javier Sánchez- Galán

    Javier Sánchez- Galán

    Universidad Tecnológica de Panamá
  • José Ulises Jiménez

    José Ulises Jiménez

    Universidad Tecnológica de Panamá

Published Date

  • Feb 11, 2018

Análisis Prospectivo de la Detección Hiperespectral de Cultivos de Arroz (Oryza Sativa L.)

KnE Engineering / 6th Engineering, Science and Technology Conference - Panama 2017 (ESTEC 2017) / Pages 69-79

Abstract

The objective of this work is to perform a prospective analysis of the wavelengths that can be used to recognize a rice crop due to its phenological status and variety. For this purpose, field measurements of spectral signature in the 350 nm -1049 nm range were employed. The rice cultivars FCA 616FL and IDIAP 54-05 were used. The study site was located in the Juan Hombrón area in the Coclé province, Panama. A principal component analysis (PCA) was carried out, which resulted in the lengths 728.16, 677.89 and 785.48 nm let phenological differentiation within the cultivar FCA 616FL and IDIAP 54-05, the lengths 729.72, 814.58 and 666.81 nm let distinguish between crop varieties FCA 616FL and IDIAP 54-05 in vegetative phase.

Keywords: Rice, reflectance, hyperspectral signature, phonological state.

References
[1] Emilio Chuvieco. (2010). “Teledetección Ambiental”. Edición 2010, Editorial Ariel, pag 18,46,55.


[2] Gnyp, Martin L.; Yuxin Miao, Fei Yuan, Susan L. Ustin, Kang Yu, Yinkun Yao, Shanyu Huang, Georg Bareth. (2013). “Hyperspectal canopy sensing of paddy rice aboveground biomass at different growth stages”. Field Crops Research 155 (2014) 42-55.


[3] He, Jiaoyang; Yehui Qin, Caili Guo, Liyun Zhao, Xiang Zhou, Xia Yao, Tao Cheng, Yongchao Tian. (2016). “Monitoring leaf area index after heading stage using hyperspectral remotesensing data in rice”. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pages: 6284 – 6287.


[4] Jarma, A.O. Degiovanny, V. Montoya R. (2010). “Índices fisiotécnicos, fases de crecimiento y etapas de desarrollo de la planta de arroz. Producción Eco-eficiente del Arroz en América Latina”. Tomo I. Pag 65-68.


[5] Kross, Angela; Heather McNairn, David Lapen, Mark Sunohara, Catherine Champagne. (2015) “Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops”. International Journal of Applied Earth Observation and Geoinformation 34 (2015) 235–248.


[6] Liang Liang, Liping Di, Lianpeng Zhang, Meixia Deng, Zhihao Qin, Shuhe Zhao, Hui Lin. (2015). “Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method”. Remote Sens. Environ. 165 (2015) 123–134.


[7] Perspectivas de cosechas y situación alimentaria. (2016). (Organización de las Naciones Unidas para la Agricultura y la Alimentación, IT) Septiempre 2016.


[8] Qiang Cao, Yuxin Miao, Jianning Shen, Weifeng Yu, Fei Yuan, Shanshan Cheng, Shanyu Huang, Hongye Wang, Wen Yang, Fengyan Liu. (2015). “Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor”. Precision Agric, Edición 2015.


[9] Qiaoyun Xie, Wenjiang Huang, Dong Liang, Pengfei Chen, Chaoyang Wu, Guijun Yang, Jingcheng Zhang, Linsheng Huang, and Dongyan Zhang. (2014). “Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 8, august 2014.


[10] Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N,Ohno H. (2005). “A crop phenology detection method using time-series MODIS data”. Remote Sens Environ, 96: 366–374.


[11] Shwetank,Kamal Jain,Karamjit Bhatia. (2010). “Hyperspectral Data Compression Model Using SPCA (Segmented Principal Component Analysis) and Classification of Rice Crop Varieties”. International Conference on Contemporary Computing, IC3 2010: Contemporary Computing pp 360-372.


[12] Wang, L., Zhang, F.-c., Jing, Y.-s., Jiang, X.-d., Yang, S.-b., Han, X.-m., 2014. “Multitemporal detection of rice phenological stages using canopy spectrum.” Rice Sci. 21, 108–115.


[13] Xinchuan Li, Youjing Zhang, Yansong Bao, Juhua Luo, Xiuliang Jin, Xingang Xu, Xiaoyu Song and Guijun Yang. (2014). “Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression”. Remote Sens. 2014, 6, 6221-6241.


[14] Yang, Chwen-Ming; Chen, Rong-Kuen. (2004). “Modeling Rice Growth with Hyperspectral Reflectance Data”. Crop Science; Julio/Agosto 2004; 44, 4; ProQuest página 1283.


[15] Zheng, Hengbiao; Tao Cheng, Xia Yao, Xinqiang Deng, Yongchao Tian, Weixing Cao,Yan Zhu. (2016). “Detection of rice phenology through time series analysis of ground-based spectral index data”. Field Crops Research 198 (2016) 131–139.