Log in

Evaluation of spectral vegetation indices for drip irrigated pumpkin seed under semi-arid conditions

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

In develo** countries, about 70% of fresh water resources are used in agriculture. Therefore, water use efficiency should be improved, and water-saving technologies should be used in irrigations. As it was in several sectors, remote sensing technologies have started to be used in agriculture together with develo** technologies. In this study, the treatments considered were irrigation to cover the 100% of the crop water needs and 5 deficit irrigation treatments that reduced the full irrigation to 80%, 60%, 40%, 20%, and 0% crop water needs, were used to irrigate pumpkin seed plants and correlations of 9 different spectral vegetation indices (SR, WI, SAVI, NDVI, OSAVI, MCARI, GNDVI, NPCI, and EVI) with seed yield, leaf area index (LAI), leaf water potential (LWP), oil, protein, and chlorophyll contents were determined. Field experiments were conducted for 2 years. In both years, significant correlations of vegetation indices with yield, LAI, LWP, protein, oil, and chlorophyll contents were observed. Present findings revealed that vegetation indices could be useful for estimating yield and quality attributes of pumpkin seed plant and in irrigation scheduling accordingly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Brazil)

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

For further information, please contact the corresponding author (haliirik42@gmail.com).

References

  • AACC (2000) Approved Methods, 10th ed, American association of cereal chemists, St. Paul, MN

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements, Irrigation and Drain, Paper No. 56. FAO, Rome, Italy, p 300

  • Amer KH (2011) Effect of irrigation method and quantity on squash yield and quality. Agr Water Manage 98(2011):1197–1206

    Article  Google Scholar 

  • Aparicio N, Viellegas D, Casadesus J, Royo AJL, C, (2000) Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron J 92:83–91

    Article  Google Scholar 

  • Babar MA, Reynolds MP, Van Ginkel M, Klatt AR, Raun WR, Stone ML (2006) Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci 46(2):578–588

    Article  Google Scholar 

  • Basyigit L, Albayrak S, Senol H, Akgül H (2008) Estimation possibility of plant nutrition contents using spectroradiometer data. 4. National Plant Nutrition and Fertilizer Congress, Konya

    Google Scholar 

  • Bolton DK, Friedl MA (2013) Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agric for Meteorol 173:74–84

    Article  Google Scholar 

  • Camoglu G, Asık S, Genc L (2010) Spectral responses to water stress of corn. J Agr Sci 3(1):37–43

    Google Scholar 

  • Camoglu G, Genc L, Asık S (2011) The effects of water stress on physiological and morphological parameters of sweet corn. Journal of Agriculture Faculty of Ege University 48(2):141–149

    Google Scholar 

  • Camoglu G, Kaya U, Akkuzu E, Genc L, Gürbüz M, Mengü GP, Kızıl Ü (2013) Prediction of leaf water status using spectral indices for young olive trees. Fresen Environ Bull 22:2713–2720

    Google Scholar 

  • Daughtry CST, Walthall CL, Kim MS, Colstoun EB, McMurtrey JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74(2):229–239

    Article  Google Scholar 

  • Dong T, Liu J, Shang J, Qian B, Ma B, Kovacs JM, Shi Y (2019) Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sens Environ 222:133–143

    Article  Google Scholar 

  • Duchemin B, Hadria R, Erraki S, Boulet G, Maisongrande P, Chehbouni A, Escadafal R, Ezzahar J, Hoedjes JCB, Kharrou MH, Khabba S, Mougenot B, Olioso A, Rodriguez JC, Simonneaux V (2006) Monitoring wheat phenology and irrigation in central Morocco: on the use of relationships between evapotranspiration, crops co efficients, leaf area index and remotely-sensed vegetation indices. Agr Water Manage 79:1–27

    Article  Google Scholar 

  • El-Hendawy S, Hassan WM, Al-Suhaibani NA, Schmidhalter U (2017) Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation. Agr Water Manage 182:1–12

    Article  Google Scholar 

  • El-Hendawy S, Al-Suhaibani NA, Elsayed S, Hassan WM, Dewir YH, Refay Y, Abdella KA (2019) Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates. Agr Water Manage 217:356–373

    Article  Google Scholar 

  • Erdle K, Mistele B, Schmidhalter U (2013) Spectral high-throughput assessments of phenotypic differences in biomass and nitrogen partitioning during grain filling of wheat under high yielding Western European conditions. Field Crop Res 141:16–26

    Article  Google Scholar 

  • Er-Raki S, Chehbouni A, Boulet G, Williams DG (2010) Using the dual approach of FAO-56 for partitioning ET into soil and plant components for olive orchards in a semi-arid regin. Agr Water Manage 97:1769–1778

    Article  Google Scholar 

  • Gitelson AA, Kaufman YJ, Merzlyak MN (1996) Use of green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ 58(3):289–298

    Article  Google Scholar 

  • Gitelson AA, Merzlyak MN (1998) Remote sensing of chlorophyll concentration in higher plant leaves. Adv Space Res 22(5):689–692. https://doi.org/10.1016/S0273-1177(97)01133-2

    Article  Google Scholar 

  • Heute AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25:53–70

    Google Scholar 

  • Hunsaker DJ, JrPJ P, Barnes EM, Kimball BA (2003) Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrigation Sci 22:95–104

    Article  Google Scholar 

  • James LG (1993) Principles of farm irrigation system design. Krieger publishing company, Florida

    Google Scholar 

  • Jones CL, Weckler PR, Maness NO, Jayasekara R, Stone ML, Chrz D (2007) Remote sensing to estimate chlorophyll concentration in spinach using multi-spectral plant reflectance. Trans ASABE 50(6):2267–2273

    Article  Google Scholar 

  • Keller J, Bliesner RD (1990) Sprinkle and trickle irrigation. Chapman and Hall,115 Fifth Avenue, New York, NY 10003

  • Kirnak H, Demirtas MN (2002) Determination of physiologic and morphologic changes in sweet cherry seedlings under water stress. Atatürk Univ J Agr Faculty 33(3)

  • Kirnak H, Irik HA, Unlükara A (2019) Potential use of crop water stress index (CWSI) in irrigation scheduling of drip-irrigated seed pumpkin plants with different irrigation levels. Sci Hortic, Amsterdam, p 108608

    Google Scholar 

  • Koksal ES (2006) Determination of the effects of different irrigation level on sugarbeet yield, quality and physiology using infrared thermometer and spectroradiometer. Ankara University, Graduate School of Natural and Applied Sciences, Deparment of Agricultural Structures and Irrigation, Ph.D. Thesis

  • Koksal ES (2008) Evaluation of spectral vegetation indices as an indicator of crop coefficient and evapotranspiration under full and deficit irrigation conditions. Int J Remote Sens 29(23):7029–7043

    Article  Google Scholar 

  • Koksal ES (2011) Hyperspectral reflectance data processing through cluster and principal component analysis for estimating irrigation and yield related indicators. Agric Water Manage 98(8):1317–1328

    Article  Google Scholar 

  • Koksal ES, Erdem C, Tasan M, Temizel KE (2021) Develo** new hyperspectral vegetation indexes sensitive to yield and evapotranspiration of dry beans. Turk J Agric for 45:743–749

    Article  Google Scholar 

  • Kovar M, Brestic M, Sytar O, Barek V, Hauptvogel P, Zivcak M (2019) Evaluation of hyperspectral reflectance parameters to assess the leaf water content in soybean. Water 11(3):443. https://doi.org/10.3390/w11030443

    Article  Google Scholar 

  • Lichtenthaler HK (1987) Chlorophylls and carotenoids: pigments of photosynthetic membranes. Methods Enzymol 148:350–382

    Article  Google Scholar 

  • Liu HQ, Huete AR (1995) A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE T Geosci Remote 33:457–465

    Article  Google Scholar 

  • Li-Hong X, Wei-**ng C, Lin-Zhang Y (2007) Predicting grain yield and protein content in winter wheat at different N supply levels using canopy reflectance spectra. Pedosphere 17(5):646–653

    Article  Google Scholar 

  • Mandal KU, Victor US, Srivastava NN, Sharma KL, Ramesh V, Vanaja M, Korwar GR, Ramakrishna YS (2007) Estimating yield of sorghum using root zone water balance model and spectral characteristics of crop in a dryland alfisol. Agric Water Manage 87:315–327

    Article  Google Scholar 

  • Mason EG, Diepstraten M, Pinjuv GL, Lasserre JP (2012) Comparison of direct and indirect leaf area index measurements of Pinus radiata D. Don Agr Forest Meteorol 166:113–119

    Article  Google Scholar 

  • Marino S, Aria M, Basso B, Leone AP, Alvino A (2014) Use of soil and vegetation spectroradiometry to investigate crop water use efficiency of a drip irrigated tomato. Eur J Agron 59:67–77

    Article  Google Scholar 

  • Marino S, Alvino A (2015) Hyperspectral vegetation indices for predicting onion (Allium cepa L.) yield spatial variability. Comput Electron Agr 116:109–117

    Article  Google Scholar 

  • Merzlyak MN, Gitelson AA, Chivkunova OB, Solovchenko AE, Pogosyan SI (2003) Application of reflectance spectroscopy for analysis of higher plant pigments. Russ j Plant Phys 50(5):704–710

    Article  Google Scholar 

  • Ones A, Demir K, Cakmak B, Kendirli B (1995) Drip irrigation scheduling in head lettuce grown in greenhouse condition. 5. Kemer-Antalya, National Irrigation and Agricultural Structure Congress, p 208

    Google Scholar 

  • Ribera-Fonseca A, Jorquera-Fontena E, Castro M, Acevedo P, Parra JC, Reyes-Diaz M (2019) Exploring VIS/NIR reflectance indices for the estimation of water status in highbush blueberry plants grown under full and deficit irrigation. Sci Hort-Amsterdam 256:108557

    Article  Google Scholar 

  • Penuelas J, Filella I, Biel C, Serrano L, Save R (1993) The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sensing 14(10):1887–1905

    Article  Google Scholar 

  • Penuelas J, Gamon JA, Fredeen AL, Merino J, Field CB (1994) Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens Environ 48:135–146

    Article  Google Scholar 

  • Penuelas J, Pinol J, Ogaya R, Fiella I (1997) Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int J Remote Sensing 18:2869–2875

    Article  Google Scholar 

  • Rodriguez MG, Estrada JAE, Gonzalez MTR, Reynolds MP (2006) Canopy reflectance indices and its relationship with yield in common bean plants (Phaseolus vulgaris L.) with phosphorous supply. Int J Agric Bio 2:1560–8530

    Google Scholar 

  • Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55:95–107

    Article  Google Scholar 

  • SAS Institute Inc (1999) SAS/GRAPH software: reference, version 8. NC: SAS Institute Inc, Cary

    Google Scholar 

  • Seymen M, Yavuz D, Dursun A, Kurtar ES, Türkmen O (2019) Identification of drought-tolerant pumpkin (Cucurbita pepo L.) genotypes associated with certain fruit characteristics, seed yield and quality. Agric Water Manage 221:150–159

    Article  Google Scholar 

  • Sharifi A (2020a) Yield prediction with machine learning algorithms and satellite images. J Sci Food Agric 101:891–896

    Article  Google Scholar 

  • Sharifi A (2020b) Using sentinel-2 data to predict nitrogen uptake in maize crop. Ieee J Sel Top Appl 13:2656–2662. https://doi.org/10.1109/JSTARS.2020.2998638

    Article  Google Scholar 

  • Sonmez N, Sonmez NK, Aslan GE, Kurunc A (2015) Relationship spectral reflectance under different salt stress conditions of tomato. J Agr Sci 21(4):585–595

    Google Scholar 

  • TUIK 2021. http://rapory.tuik.gov.tr/14118508949029401031786289904.html (Access Date: February 27, 2022).

  • Tunca E, Koksal ES, Cetin S, Ekiz NM, Balde H (2018) Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images. Environ Monit Assess 190(11):1–12

    Article  Google Scholar 

  • Toureiro C, Serralheiro R, Shahidian S, Sousa A (2016) Irrigation management with remote sensing:evaluating irrigation requirement for maize under mediterranean climate condition. Agric Water Manage 184:211–220

    Article  Google Scholar 

  • Weber VS, Araus JL, Cairns JE, Sanchez C, Melchinger AE, Orsini E (2012) Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crop Res 128:82–90

    Article  Google Scholar 

  • **e Q, Dash J, Huang W, Peng D, Qin Q, Mortimer H, Dong Y (2018) Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE J Sel Top App 11(5):1482–1493

    Google Scholar 

  • Yavuz D, Seymen M, Yavuz N, Türkmen O (2015) Effects of ırrigation ınterval and quantity on the yield and quality of confectionary pumpkin grown under field conditions. Agric Water Manage 159:290–298

    Article  Google Scholar 

  • Yavuz D, Suheri S, Yavuz N (2016) Energy and water use for drip-irrigated potato in the Middle Anatolian region of Turkey. Environ Prog Sustain Energy 35(1):212–220

    Article  Google Scholar 

  • Yang X, Yu Y, Fan W (2015) Chlorophyll content retrieval from hyperspectral remote sensing imagery. Environ Monit Assess 187(7):456

    Article  Google Scholar 

  • Zhang Z, Sun H, Qiao X, Yan X, Feng M, **ao L, Song X, Zhang M, Shafiq F, Yang W, Wang C (2022) Hyperspectral estimation of canopy chlorophyll of winter wheat by using the optimized vegetation indices. Comput Electron Agr 193:106654

    Article  Google Scholar 

Download references

Acknowledgements

This study presents partial results of the Ph.D. Thesis conducted by Hasan Ali Irik. Authors would like to thank Turkish Scientific and Technical Research Council (TUBITAK) for financial support to the project of TOVAG-114O225 and Scientific Research Projects Department of Erciyes University and for financial support provided to the project of FDK-2015-5875.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hasan Ali Irik.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Responsible Editor: Biswajeet Pradhan

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Irik, H.A., Kirnak, H. Evaluation of spectral vegetation indices for drip irrigated pumpkin seed under semi-arid conditions. Arab J Geosci 15, 861 (2022). https://doi.org/10.1007/s12517-022-10136-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-022-10136-z

Keywords

Navigation