Skip to main content

Advertisement

Log in

Remotely Sensed Methodologies for Crop Water Availability and Requirements in Precision Farming of Vulnerable Agriculture

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Agriculture is mainly impacted by water availability. Differences in climate conditions and the appearance of severe events, like droughts, has a significant imprint on local, regional and global agricultural productivity. The goal of this paper is to present remotely sensed approaches for water availability and requirements in vulnerable agriculture. Earth Observation (EO) data contribute to precision agriculture for efficient crop monitoring and irrigation management. A drought susceptible region considered as vulnerable farming was chosen, in the Thessaly prefecture in Central Greece. Water availability is measured by means of precipitation frequency examination and drought estimation. Crop water requirements are measured by assessing crop evapotranspiration (ET) with the synergistic use of WV-2 satellite images and ground-truth data. The remote-based ETcsat is assessed by utilizing the reference ETo derived from Food and Agriculture Organization (FAO) methodology, while the meteorological data and Kc are evolved from Normalized Difference Vegetation Index (NDVI). According to the rainfall frequency studies, indicators demonstrate a significant precipitation decrease. The results reveal the importance of water availability estimation for facing agriculture water needs and the necessity for monitoring of drought conditions in a vulnerable Mediterranean area in order to plan an integrated strategy for climate adaptation. Moreover, the conclusions clarify the usefulness of collaborating innovative very high spatial and sperctral resolution EO images along with ground-truth data for crop ET monitoring and also the assimilation into the precision agriculture methodology which is valuable for optimal agricultural production.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abuzar M, Sheffield K, Whitfield D, O’Connell M, McAllister A (2014) Comparing inter-sensor NDVI for the analysis of horticulture crops in South-Eastern Australia. American Journal of Remote Sensing 2(1):1–9

    Article  Google Scholar 

  • Ahmed A, Nithya R (2016) Within-season growth and spectral reflectance of cotton and their relation to lint yield. Crop Sci 56:2688–2701. https://doi.org/10.2135/cropsci2015.05.0296

    Article  Google Scholar 

  • Alexandrov VA, Hoogenboom G (2000) The impact of climate variability and change on crop yield in Bulgaria. Agric For Meteorol 104:315–327

    Article  Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: guidelines for computing crop requirements. Irrigation and drainage paper no. 56. FAO, Rome, p 300

    Google Scholar 

  • Bampzelis D, Chatzipli A, Dadali O, Dalezios NR (2006) Frequency analysis of precipitation characteristics in different climate zones for Greece. In: Proceedings of the 3rd HAICTA international conference: information systems in sustainable agriculture, agroenvironment and food technology, Volos Greece, pp 887–895

  • Bampzelis D, Pytharoulis I, Tegoulias I, Zanis P, Karacostas T (2014) Rainfall characteristics and drought conditions interconnected to the potentiality and applicability of the “DAPHNE” rain enhancement project in Thessaly. Conference Proceedings, 10th International Congress of the Hellenic Geographical Society, Tessaloniki, Greece, pp 1–9

  • Blaney HF, Criddle WD (1950) Determining water requirements in irrigated areas from climatological and irrigation data. USDA Soil Conservation Service Technical Paper, 96, pp 48

  • Climate Change Watcher (2017) https://watchers.news/category/climate-change/

  • Dalezios NR (2011) Climatic change and agriculture: impacts-mitigation-adaptation. Scientific Journal of GEOTEE 27:13–28

    Google Scholar 

  • Dalezios NR, Loukas A, Vasiliades L, Liakopoulos H (2000) Severity-duration-frequency analysis of droughts and wet periods in Greece. Hydrol Sci J 45(5):751–769

    Article  Google Scholar 

  • Dalezios NR, Gkagkas Z, Domenikiotis C, Kanellou E, Mplanta Α (2009) Climate change and water for agriculture: impacts-mitigation-adaptation. Proceedings, EWRA conference on water resources conservancy and risk reduction under climatic instability, Techn. Univ. of Cyprus (TUC), sponsored by EWRA and TUC, Limassol, Cyprus

  • Dalezios NR, Mplanta A, Domenikiotis C (2011) Remotely sensed cotton evapotranspiration for irrigation water management in vulnerable agriculture of central Greece. J of Information Technology Agriculture 4(1):1–14

    Google Scholar 

  • Dalezios NR, Mplanta A, Spyropoulos NV (2012a) Assessment of remotely sensed drought features in vulnerable agriculture. NHESS 12:3139–3150

    Google Scholar 

  • Dalezios NR, Spyropoulos NV, Mplanta A, Stamatiades S (2012b) Agrometeorological remote sensing of high resolution for decision support in precision agriculture. 11th international conference on meteorology, climatology and atmospheric physics. Athens, pp 51–56

  • Dalezios NR, Mplanta A, Spyropoulos NV, Tarquis AM (2014) Risk identification of agricultural drought in sustainable agroecosystems. NHESS 14:2435–2448

    Google Scholar 

  • Dalezios NR, Dercas N, Spyropoulos NV, Psomiadis E (2017) Water availability and requirement for precision agriculture in vulnerable agroecosystems. Proc. EWRA2017, NTUA, Athens Greece, pp 1715–1722

  • Dalezios NR, Dercas N, Eslamian S (2018) Water scarcity management: part 2: satellite-based composite drought analysis. IJGEI 17(2/3):267–295

    Google Scholar 

  • Dercas N, Spyropoulos NV, Dalezios NR, Psomiadis E, Stefopoulou A, Madonanakis G, Tserlikakis N (2017) Cotton evapotranspiration using very high spatial resolution WV-2 satellite data and ground measurements for precision agriculture. WIT Trans Ecol Environ 220:101–107. Water Resources Management 2017, WIT press. https://doi.org/10.2495/WRM170101

  • Hansen JW (2002) Realizing the potential benefits of climate prediction to agriculture: issues, approaches, challenges. Agric Syst 74:309–330. https://doi.org/10.1016/S0308-521X(02)00043-4

    Article  Google Scholar 

  • Heim RR (2002) A review of twentieth-century drought indices used in the United States. Bull Am Meteorol Soc 83(8):1149–1165

    Article  Google Scholar 

  • Kanellou E, Domenikiotis C, Dalezios NR (2008) Description of conventional and satellite drought indices. In: Tsakiris G (ed) Proactive management of water systems to face drought and water scarcity in islands and coastal areas of the Mediterranean (PRODIM) – Final Report to EC, 448p, CANaH Publication 6/08, Athens, pp 23–57

  • Kogan FN (1995) Application of vegetation index and brightness temperature for drought detection. Adv Space Res 15:91–100

    Article  Google Scholar 

  • Kogan FN (2001) Operational space technology for global vegetation assessment. Bull Am Meteorol Soc 82:1949–1964

    Article  Google Scholar 

  • Lanzl F, Richter R (1991) A fast atmospheric correction algorithm for small swath angle satellite sensors. ICO topical meeting on atmospheric, volume, and surface scattering and propagation, Florence, Italy

  • Mulla DJ (2013) Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114(4):358–371

    Article  Google Scholar 

  • Niemeyer S (2008) New drought indices, options Méditerranéennes. Série A Séminaires Méditerranéens 80:267–274

    Google Scholar 

  • Olesen JE, Bindi M (2002) Consequences of climate change for European agricultural productivity, land use and policy. Eur J Agron 16:239–262

    Article  Google Scholar 

  • Psomiadis E, Dercas N, Dalezios NR, Spyropoulos NV (2016) The role of spatial and spectral resolution on the effectiveness of satellite-based vegetation indices. Proc SPIE 9998:99981L-1-13. https://doi.org/10.1117/12.2241316

    Article  Google Scholar 

  • Psomiadis E, Dercas N, Dalezios RN, Spyropoulos N (2017) Evaluation and cross-comparison of vegetation indices for crop monitoring from Sentinel-2 and WorldView-2 images. Proc SPIE 10421:104211B. https://doi.org/10.1117/12.2278217

    Article  Google Scholar 

  • Rafn EB, Contor B, Ames DP (2008) Evaluation of a method for estimating irrigated crop-evapotranspiration coefficients from remotely sensed data in Idaho. J Irrig Drain Eng 134:722–729

    Article  Google Scholar 

  • Rouse JW, JrHaas RH, Deering DW, Schell JA, Harlan JC (1974) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, greenbelt. MD NASA/GSFC Type III Final Report, pp 371

  • Salehi B, Zhang Y, Zhong M (2012) The effect of four new multispectral bands of Worldview-2 on improving urban land cover classification. ASPRS annual conference, Sacramento, Ca, pp 7

  • Sultan B, Gaetani M (2016) Agriculture in West Africa in the twenty-first century: climate change and impacts scenarios, and potential for adaptation. Front Plant Sci 7:1262. https://doi.org/10.3389/fpls.2016.01262

    Article  Google Scholar 

  • Thenkabail PS, Gamage MSD, Smakhtin VU (2004) The use of remote sensing data for drought assessment and monitoring in Southwest Asia, research report. International Water Management Institute 85:1–25

    Google Scholar 

  • Tsakiris G, Vangelis H (2005) Establishing a drought index incorporating evapotranspiration. European Water 9-10:3–11

    Google Scholar 

  • Tsakiris G, Pangalou D, Vangeis H (2007) Regional drought assessment based on the reconnaissance drought index (RDI). Water Resour Manag 21(5):821–833

    Article  Google Scholar 

  • Updike T, Comp C (2010) Radiometric use of WorldView-2 imagery. Technical Note, DigitalGlobe, 1601 Dry Creek Drwangive Suite 260 Longmont, Colorado, USA, 80503

  • Varella CAA, Gleriani JM, dos Santos RM (2015) Chapter 9 – Precision agriculture and remote sensing. Sugarcane, pp 185–203. https://doi.org/10.1016/B978-0-12-802239-9.00009-8

  • Calera A, Campos I, Osann A, D’Urso G, Menenti M (2017) Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users. Sensors 17(5):1104

    Article  Google Scholar 

Download references

Acknowledgements

A previous shorter version of the paper has been presented in the 10th World Congress of EWRA “Panta Rei” Athens, Greece, July 2017. The meteorological data were acquired by the Hellenic National Meteorological Service. Earth Observation data were provided by NASA. Research was funded by the INTERREG Illb PRODIM project, EU FP6 PLEIADES project and by HORIZON2020 FATIMA project. The authors would like to thank the editor and the reviewers for their constructive comments and valuable suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Emmanouil Psomiadis.

Ethics declarations

Conflict of Interest

None.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dalezios, N.R., Dercas, N., Spyropoulos, N.V. et al. Remotely Sensed Methodologies for Crop Water Availability and Requirements in Precision Farming of Vulnerable Agriculture. Water Resour Manage 33, 1499–1519 (2019). https://doi.org/10.1007/s11269-018-2161-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-018-2161-8

Keywords

Navigation