Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 5 Sayı: 1, 1 - 13, 30.06.2019
https://doi.org/10.26558/ijcst.427945

Öz

Kaynakça

  • Al-Jamal, M. S., Sammis, T. W., Ball, S., & Smeal, D. (1999). Yield-based, irrigated onion crop coefficients. Applied Engineering in Agriculture, 15(6), 659.
  • Al-Jamal, M. S., Sammis, T. W., Ball, S., & Smeal, D. (2000). Computing the crop water production function for onion. Agricultural Water Management, 46(1), 29-41.
  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.
  • Allen, R. G., Tasumi, M., Morse, A., & Trezza, R. (2005). A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrigation and Drainage Systems, 19(3), 251-268.
  • ASCE-EWRI. (2005). The ASCE Standardized reference evapotranspiration equation. Reported by the American Society of Civil Engineers (ASCE) Task Committee on Standardization of Reference Evapotranspiration. In: R.G. Allen, I.A. Walter. R.L. Elliot, T.A. Howell, D. Itenfisu, M.E. Jensen and R.L. Snyder (Eds.), ASCE 0-7844-0805-X, Reston, VA, 204pp.
  • Bandyopadhyay, P. K., Mallick, S., & Rana, S. K. (2003). Actual evapotranspiration and crop coefficients of onion (Allium cepa L.) under varying soil moisture levels in the humid tropics of India. TROPICAL AGRICULTURE-LONDON THEN TRINIDAD-, 80(2), 83-90.
  • Barsi, J. A., Schott, J. R., Palluconi, F. D., & Hook, S. J. (2005, August). Validation of a web-based atmospheric correction tool for single thermal band instruments. In Optics & Photonics 2005 (pp. 58820E-58820E). International Society for Optics and Photonics.
  • Barsi, J.A.; Lee, K.; Kvaran, G.; Markham, B.L.; Pedelty, J.A. (2014). The Spectral Response of the Landsat-8 Operational Land Imager. Remote Sens. 2014, 6, 10232-10251.
  • Bastiaanssen, W. G., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998a). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology, 212, 198-212.
  • Bastiaanssen, W. G., Pelgrum, H., Wang, J., Ma, Y., Moreno, J. F., Roerink, G. J., & Van der Wal, T. (1998b). A remote sensing surface energy balance algorithm for land (SEBAL).: Part 2: Validation. Journal of hydrology, 212, 213-229.
  • Bastiaanssen, W. G. M., Noordman, E. J. M., Pelgrum, H., Davids, G., Thoreson, B. P., & Allen, R. G. (2005). SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. Journal of irrigation and drainage engineering, 131(1), 85-93.
  • Bawazir, A. S., Samani, Z., Bleiweiss, M., Skaggs, R., & Schmugge, T. (2009). Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande, New Mexico. International journal of remote sensing, 30(21), 5593-5603.
  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2012). Feature extraction using Normalized Difference Vegetation Index (NDVI): a case study of Jabalpur city. Procedia Technology, 6, 612-621.
  • Corgan, J. N., Wall, M. M., Cramer, C. S., Sammis, T., Lewis, B., & Schroeder, J. (2000). Bulb onion culture and management. new mexico: Cooperative Extension Service, College of Agriculture and Home Economics, New Mexico State University.
  • de Santa Olalla, F. M., Domı́nguez-Padilla, A., & Lopez, R. (2004). Production and quality of the onion crop (Allium cepa L.) cultivated under controlled deficit irrigation conditions in a semi-arid climate. Agricultural Water Management, 68(1), 77-89.
  • Doorenbos, J., & Kassam, A.H. (1986). Yield response to water. FAO Irrig. and Drain, Paper 33. Rome. Italy.
  • Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and GIS–A case study of Vellore district. Procedia Computer Science, 57, 1199-1210.
  • Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.
  • Kumar, S., Imtiyaz, M., Kumar, A., & Singh, R. (2007). Response of onion (Allium cepa L.) to different levels of irrigation water. Agricultural Water Management, 89(1), 161-166.
  • Liang, S. (2001). Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2), 213-238.
  • López-Urrea, R., de Santa Olalla, F. M., Montoro, A., & López-Fuster, P. (2009). Single and dual crop coefficients and water requirements for onion (Allium cepa L.) under semiarid conditions. Agricultural Water Management, 96(6), 1031-1036.
  • Malm, N. R. (2003). Climate Guide, Las Cruces, 1892-2000. New Mexico State University, Agricultural Experiment Station.
  • Meranzova, R., & Babrikov, T. (2002). Evapotranspiration of long-day onion, irrigated by microsprinklers. Journal of Central European Agriculture, 3(3).
  • Mermoud, A., Tamini, T. D., & Yacouba, H. (2005). Impacts of different irrigation schedules on the water balance components of an onion crop in a semi-arid zone. Agricultural water management, 77(1), 282-295.
  • Montandon, L. M., & Small, E. E. (2008). The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment, 112(4), 1835-1845.
  • Piccinni, G., Ko, J., Marek, T., & Leskovar, D. I. (2009). Crop coefficients specific to multiple phenological stages for evapotranspiration-based irrigation management of onion and spinach. HortScience, 44(2), 421-425.
  • Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS.
  • Samani, Z., Bawazir, A. S., Bleiweiss, M., Skaggs, R., Longworth, J., Tran, V. D., & Pinon, A. (2009). Using remote sensing to evaluate the spatial variability of evapotranspiration and crop coefficient in the lower Rio Grande Valley, New Mexico.
  • Samani, Z., Skaggs, R., Bawazir, A., Bleiweiss, M., Tran V., Pinon, A. (2012, December). Remote sensing of agricultural water use in New Mexico from theory to practice. New Mexico Journal of Science,46, 1-16.
  • Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 90(4), 434-440.
  • Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., ... & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316-327.
  • USDA-NASS & NMDA (2010). New Mexico Agricultural Statistic Bulletin-2010. Las Cruces, NM.
  • USDA-NASS & NMDA (2016). 2015 NM Agricultural Statistics. Las Cruces, NM.
  • USGS, (2016). Landsat 8 (L8) Data Users Handbook. Sioux Falls, South Dakota
  • Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829-9852.
  • Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote sensing of Environment, 106(3), 375-386.
  • Zheng, J., Huang, G., Wang, J., Huang, Q., Pereira, L. S., Xu, X., & Liu, H. (2013). Effects of water deficits on growth, yield and water productivity of drip-irrigated onion (Allium cepa L.) in an arid region of Northwest China. Irrigation science, 31(5), 995-1008.

Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico

Yıl 2019, Cilt: 5 Sayı: 1, 1 - 13, 30.06.2019
https://doi.org/10.26558/ijcst.427945

Öz





Using Plant Phenology and Landsat-8
Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New
Mexico

Cantekin KIVRAK1*, A. Salim BAWAZIR2,
Zohrab SAMANI3, Caiti STEELE4, and Bülent SÖNMEZ5

1.      
Graduate Student, Dept. of Civil Engineering, New Mexico State
University, MSC 3CE, PO Box 30001, Las Cruces, NM 88003, E-mail:
cantekin@nmsu.edu , Corresponding
author

2.      
Associate Professor, Dept. of Civil Engineering, New Mexico State
University, MSC 3CE, PO Box 30001, Las Cruces, NM 88003, E-mail:
abawazir@nmsu.edu

3.      
Professor, Dept. of Civil Engineering, New Mexico State University,
MSC 3CE, PO Box 30001, Las Cruces, NM 88003, E-mail:
zsamani@nmsu.edu

4.      
Assistant
Professor, Dept. of Plant and Environmental Science Jornada Experimental Range
Station, USDA-ARS-Jornada Experimental Range, P.O. Box 30003, MSC 3JER, NMSU,
Las Cruces, NM  88003, E-mail:
caiti@nmsu.edu

5.      
Dept.
Chair of Soil and Water Resources, MFAL, General Directorate of Agricultural Research
and Policies, Ankara, TURKEY, E-mail:
bulent.sonmez@tarim.gov.tr

 

 

ABSTRACT





















Non-storage summer
dry onion crop is among the top 10 agricultural commodities in New Mexico (NM),
USA. In 2000, NM was ranked the second in the nation as the leading state in
growing summer dry onion. According to USDA statistical records, onion
production or sales in NM was valued at $91.4 million. Mesilla Valley is one of
the major onion-producing regions of NM. Due to many years of drought in the
region and the concern for climate change, irrigation managers and decision
makers are interested in quantifying water use or evapotranspiration (ET) and
the number of acreage of onion crop grown in the Valley. This information can
then be used for managing the scarce water resources of the region.  Plant phenology, Landsat-8 satellite data,
and USDA crop data were used to identify onion crops in the Valley (area of
about 47,000 ha) and to determine their consumptive water use or ET using
remote sensing Regional ET Estimation Model (REEM) from 2014 through 2016. Time
series of NDVI clearly identified Fall and Spring-season onion crops in the
Valley. REEM estimated Spring-season onion crop maximum ET of 973 mm in 2015 and
975 mm in 2016 during the growing season. These values compared reasonably well
to ET estimates of 894 and 955 mm for the same periods (i.e. 2015 and 2016)
using FAO-56 crop coefficient based method. 
The methodology presented could be used in other regions to identify
onion crops and their consumptive water use.

Key words:Onion, NDVI, Evapotranspiration, Landsat-8,
Remote Sensing

 

















[*] Corresponding author: cantekin@nmsu.edu





Kaynakça

  • Al-Jamal, M. S., Sammis, T. W., Ball, S., & Smeal, D. (1999). Yield-based, irrigated onion crop coefficients. Applied Engineering in Agriculture, 15(6), 659.
  • Al-Jamal, M. S., Sammis, T. W., Ball, S., & Smeal, D. (2000). Computing the crop water production function for onion. Agricultural Water Management, 46(1), 29-41.
  • Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome, 300(9), D05109.
  • Allen, R. G., Tasumi, M., Morse, A., & Trezza, R. (2005). A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrigation and Drainage Systems, 19(3), 251-268.
  • ASCE-EWRI. (2005). The ASCE Standardized reference evapotranspiration equation. Reported by the American Society of Civil Engineers (ASCE) Task Committee on Standardization of Reference Evapotranspiration. In: R.G. Allen, I.A. Walter. R.L. Elliot, T.A. Howell, D. Itenfisu, M.E. Jensen and R.L. Snyder (Eds.), ASCE 0-7844-0805-X, Reston, VA, 204pp.
  • Bandyopadhyay, P. K., Mallick, S., & Rana, S. K. (2003). Actual evapotranspiration and crop coefficients of onion (Allium cepa L.) under varying soil moisture levels in the humid tropics of India. TROPICAL AGRICULTURE-LONDON THEN TRINIDAD-, 80(2), 83-90.
  • Barsi, J. A., Schott, J. R., Palluconi, F. D., & Hook, S. J. (2005, August). Validation of a web-based atmospheric correction tool for single thermal band instruments. In Optics & Photonics 2005 (pp. 58820E-58820E). International Society for Optics and Photonics.
  • Barsi, J.A.; Lee, K.; Kvaran, G.; Markham, B.L.; Pedelty, J.A. (2014). The Spectral Response of the Landsat-8 Operational Land Imager. Remote Sens. 2014, 6, 10232-10251.
  • Bastiaanssen, W. G., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998a). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of hydrology, 212, 198-212.
  • Bastiaanssen, W. G., Pelgrum, H., Wang, J., Ma, Y., Moreno, J. F., Roerink, G. J., & Van der Wal, T. (1998b). A remote sensing surface energy balance algorithm for land (SEBAL).: Part 2: Validation. Journal of hydrology, 212, 213-229.
  • Bastiaanssen, W. G. M., Noordman, E. J. M., Pelgrum, H., Davids, G., Thoreson, B. P., & Allen, R. G. (2005). SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. Journal of irrigation and drainage engineering, 131(1), 85-93.
  • Bawazir, A. S., Samani, Z., Bleiweiss, M., Skaggs, R., & Schmugge, T. (2009). Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande, New Mexico. International journal of remote sensing, 30(21), 5593-5603.
  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2012). Feature extraction using Normalized Difference Vegetation Index (NDVI): a case study of Jabalpur city. Procedia Technology, 6, 612-621.
  • Corgan, J. N., Wall, M. M., Cramer, C. S., Sammis, T., Lewis, B., & Schroeder, J. (2000). Bulb onion culture and management. new mexico: Cooperative Extension Service, College of Agriculture and Home Economics, New Mexico State University.
  • de Santa Olalla, F. M., Domı́nguez-Padilla, A., & Lopez, R. (2004). Production and quality of the onion crop (Allium cepa L.) cultivated under controlled deficit irrigation conditions in a semi-arid climate. Agricultural Water Management, 68(1), 77-89.
  • Doorenbos, J., & Kassam, A.H. (1986). Yield response to water. FAO Irrig. and Drain, Paper 33. Rome. Italy.
  • Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and GIS–A case study of Vellore district. Procedia Computer Science, 57, 1199-1210.
  • Jensen, J. R. (2009). Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education India.
  • Kumar, S., Imtiyaz, M., Kumar, A., & Singh, R. (2007). Response of onion (Allium cepa L.) to different levels of irrigation water. Agricultural Water Management, 89(1), 161-166.
  • Liang, S. (2001). Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2), 213-238.
  • López-Urrea, R., de Santa Olalla, F. M., Montoro, A., & López-Fuster, P. (2009). Single and dual crop coefficients and water requirements for onion (Allium cepa L.) under semiarid conditions. Agricultural Water Management, 96(6), 1031-1036.
  • Malm, N. R. (2003). Climate Guide, Las Cruces, 1892-2000. New Mexico State University, Agricultural Experiment Station.
  • Meranzova, R., & Babrikov, T. (2002). Evapotranspiration of long-day onion, irrigated by microsprinklers. Journal of Central European Agriculture, 3(3).
  • Mermoud, A., Tamini, T. D., & Yacouba, H. (2005). Impacts of different irrigation schedules on the water balance components of an onion crop in a semi-arid zone. Agricultural water management, 77(1), 282-295.
  • Montandon, L. M., & Small, E. E. (2008). The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sensing of Environment, 112(4), 1835-1845.
  • Piccinni, G., Ko, J., Marek, T., & Leskovar, D. I. (2009). Crop coefficients specific to multiple phenological stages for evapotranspiration-based irrigation management of onion and spinach. HortScience, 44(2), 421-425.
  • Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS.
  • Samani, Z., Bawazir, A. S., Bleiweiss, M., Skaggs, R., Longworth, J., Tran, V. D., & Pinon, A. (2009). Using remote sensing to evaluate the spatial variability of evapotranspiration and crop coefficient in the lower Rio Grande Valley, New Mexico.
  • Samani, Z., Skaggs, R., Bawazir, A., Bleiweiss, M., Tran V., Pinon, A. (2012, December). Remote sensing of agricultural water use in New Mexico from theory to practice. New Mexico Journal of Science,46, 1-16.
  • Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment, 90(4), 434-440.
  • Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., ... & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2), 316-327.
  • USDA-NASS & NMDA (2010). New Mexico Agricultural Statistic Bulletin-2010. Las Cruces, NM.
  • USDA-NASS & NMDA (2016). 2015 NM Agricultural Statistics. Las Cruces, NM.
  • USGS, (2016). Landsat 8 (L8) Data Users Handbook. Sioux Falls, South Dakota
  • Yu, X., Guo, X., & Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6(10), 9829-9852.
  • Yuan, F., & Bauer, M. E. (2007). Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote sensing of Environment, 106(3), 375-386.
  • Zheng, J., Huang, G., Wang, J., Huang, Q., Pereira, L. S., Xu, X., & Liu, H. (2013). Effects of water deficits on growth, yield and water productivity of drip-irrigated onion (Allium cepa L.) in an arid region of Northwest China. Irrigation science, 31(5), 995-1008.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Cantekin Kıvrak

Salim Bawazir Bu kişi benim

Zohrab Samani Bu kişi benim

Caiti Steele Bu kişi benim

Bülent Sönmez Bu kişi benim

Yayımlanma Tarihi 30 Haziran 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 5 Sayı: 1

Kaynak Göster

APA Kıvrak, C., Bawazir, S., Samani, Z., Steele, C., vd. (2019). Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico. International Journal of Crop Science and Technology, 5(1), 1-13. https://doi.org/10.26558/ijcst.427945
AMA Kıvrak C, Bawazir S, Samani Z, Steele C, Sönmez B. Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico. IJCST. Haziran 2019;5(1):1-13. doi:10.26558/ijcst.427945
Chicago Kıvrak, Cantekin, Salim Bawazir, Zohrab Samani, Caiti Steele, ve Bülent Sönmez. “Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico”. International Journal of Crop Science and Technology 5, sy. 1 (Haziran 2019): 1-13. https://doi.org/10.26558/ijcst.427945.
EndNote Kıvrak C, Bawazir S, Samani Z, Steele C, Sönmez B (01 Haziran 2019) Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico. International Journal of Crop Science and Technology 5 1 1–13.
IEEE C. Kıvrak, S. Bawazir, Z. Samani, C. Steele, ve B. Sönmez, “Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico”, IJCST, c. 5, sy. 1, ss. 1–13, 2019, doi: 10.26558/ijcst.427945.
ISNAD Kıvrak, Cantekin vd. “Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico”. International Journal of Crop Science and Technology 5/1 (Haziran 2019), 1-13. https://doi.org/10.26558/ijcst.427945.
JAMA Kıvrak C, Bawazir S, Samani Z, Steele C, Sönmez B. Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico. IJCST. 2019;5:1–13.
MLA Kıvrak, Cantekin vd. “Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico”. International Journal of Crop Science and Technology, c. 5, sy. 1, 2019, ss. 1-13, doi:10.26558/ijcst.427945.
Vancouver Kıvrak C, Bawazir S, Samani Z, Steele C, Sönmez B. Using Plant Phenology and Landsat-8 Satellite Data to Quantify Water Use by Onion Crop in the Mesilla Valley, New Mexico. IJCST. 2019;5(1):1-13.