https://doi.org/10.4081/jae.2024.1623
Satellite remote sensing for monitoring citrus orchards water requirements at the irrigation district scale
Published: 31 October 2024
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In Italy, irrigated agriculture is often managed by Reclamation Consortia, but they generally lack the proper tools for monitoring the irrigation water requirements (IWR), forbidding the required sustainability level by the European Water Framework Directive. In this context, the study aims at monitoring the IWR of citrus orchards of a Sicilian irrigation district by implementing a satellite-based methodological approach, during the irrigation seasons 2019 and 2020. Firstly, neural networks were implemented to map the citrus orchards, by using Normalized Difference Vegetation Index time series as input, and obtaining accuracy values of 94% and 87%, in 2019 and 2020, respectively. Then, the satellite-based ArcDualKc model was used to estimate crop evapotranspiration (ETc) over the identified citrus orchards. The estimated ETc rates were validated at the farm scale in terms of irrigation volumes and compared to those obtained by the traditional FAO-56 approach. Then, the spatially distributed irrigation volumes estimated at district level were compared with those declared by the Reclamation Consortium, obtaining absolute error values of 3.28 Mm3 (294%) and 7.08 Mm3 (647%) for the years 2019 and 2020, respectively. The results of the study confirmed the usefulness of the satellite-based methodological approach for determining spatial distributed IWR estimates.
Allen, R., Pereira, L., Raes, D., Smith, M. 1998. FAO irrigation and drainage paper No. 56, Food and Agriculture Organization of the United Nations. Tech. Rep. 56.
Bolognesi, F.S., Pasolli, E., Belfiore, O.R., De Michele, C., D’Urso, G., 2020. Harmonized landsat 8 and sentinel-2 time series data to detect irrigated areas: An application in Southern Italy. Remote Sensing 12:1275
DOI: https://doi.org/10.3390/rs12081275
Consoli, S., Caggia, C., Russo, N., Randazzo, C.L., Continella, A., Modica, G., et al. 2023. Sustainable use of citrus waste as organic amendment in orange orchards. Sustainability 15:2482.
DOI: https://doi.org/10.3390/su15032482
Consoli S., O’Connell N., Snyder R. 2006. Measurements of light interception by navel orange orchard canopies: Case study of Lindsay, California. J. Irrig. Drain. Eng. 132:9-20
DOI: https://doi.org/10.1061/(ASCE)0733-9437(2006)132:1(9)
Consoli, S., Vanella, D. 2014) Mapping crop evapotranspiration by integrating vegetation indices into a soil water balance model. Agr. Water Manage. 143:71-81.
DOI: https://doi.org/10.1016/j.agwat.2014.06.012
Döll, P., Siebert, S. 2002. Global modeling of irrigation water requirements. Water Resour. Res. 38:8-1-8-10.
DOI: https://doi.org/10.1029/2001WR000355
Howell, T.A. 2003. Irrigation efficiency. In: B.A. Stewart, T.A. Howell (eds.), Encyclopedia of water science. New York, Marcel-Dekker, inc. pp. 467-472.
Hsiao, T.C., Steduto, P., Fereres, E. 2007. A systematic and quantitative approach to improve water use efficiency in agriculture. Irrig. Sci. 25:209-231.
DOI: https://doi.org/10.1007/s00271-007-0063-2
Htitiou, A., Boudhar, A., Lebrini, Y., Hadria, R., Lionboui, H., Elmansouri, L., et al. 2019. The performance of random forest classification based on phenological metrics derived from Sentinel-2 and Landsat 8 to map crop cover in an irrigated semi-arid region. Remote Sens. Earth Syst. Sci. 2:208-224.
DOI: https://doi.org/10.1007/s41976-019-00023-9
Longo-Minnolo, G., Vanella, D., Consoli, S., Intrigliolo, D.S., Ramírez-Cuesta, J.M. 2020. Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard. Agr. Water Manage. 231:105967.
DOI: https://doi.org/10.1016/j.agwat.2019.105967
Magidi, J., Nhamo, L., Mpandeli, S., Mabhaudhi, T., 2021. Application of the random forest classifier to map irrigated areas using google earth engine. Remote Sens. 13:876.
DOI: https://doi.org/10.3390/rs13050876
Mancosu, N., Snyder, R.L., Kyriakakis, G., Spano, D. 2015. Water scarcity and future challenges for food production. Water (Basel) 7:975-992.
DOI: https://doi.org/10.3390/w7030975
Pappalardo, S., Chiaradia, E.A., Longo-Minnolo, G., Vanella, D., Consoli, S. 2022. Monitoring and predicting irrigation requirements of tree crops in eastern Sicily as a tool for sustainability. Proc. Conf. Italian Society of Agricultural Engineering. Cham, Springer. pp. 143-151.
DOI: https://doi.org/10.1007/978-3-031-30329-6_15
Pelosi, A., Bolognesi, S.F., D’Urso, G., Chirico, G.B. 2021. Assessing crop evapotranspiration by combining ERA5-Land meteorological reanalysis data and visible and near-infrared satellite imagery. Proc. IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). pp. 285-289.
DOI: https://doi.org/10.1109/MetroAgriFor52389.2021.9628640
Ramírez-Cuesta, J.M., Mirás-Avalos, J.M., Rubio-Asensio, J.S., Intrigliolo, D.S. 2018. A novel ArcGIS toolbox for estimating crop water demands by integrating the dual crop coefficient approach with multi-satellite imagery. Water (Basel) 11:38.
DOI: https://doi.org/10.3390/w11010038
Rouse, J.W. 1974. Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, type III, Final report, Greenbelt, MD, 371.
Singh, S., Boote, K.J., Angadi, S.V., Grover, K.K. 2017. Estimating water balance, evapotranspiration and water use efficiency of spring safflower using the CROPGRO model. Agr. Water Manage. 185:137-144.
DOI: https://doi.org/10.1016/j.agwat.2017.02.015
Vanella, D., Ferlito, F., Torrisi, B., Giuffrida, A., Pappalardo, S., Saitta, D., Longo-Minnolo, G., Consoli, S. 2021. Long-term monitoring of deficit irrigation regimes on citrus orchards in Sicily. J. Agric. Eng. 52:1193.
DOI: https://doi.org/10.4081/jae.2021.1193
Vanella, D., Ramírez-Cuesta, J. M., Longo-Minnolo, G., Longo, D., D’Emilio, A., Consoli, S. 2023. Identifying soil-plant interactions in a mixed-age orange orchard using electrical resistivity imaging. Plant Soil 483:181-197.
DOI: https://doi.org/10.1007/s11104-022-05733-6
Wriedt, G., Van der Velde, M., Aloe, A., Bouraoui, F. 2009. Estimating irrigation water requirements in Europe. J. Hydrol. 373:527-544.
DOI: https://doi.org/10.1016/j.jhydrol.2009.05.018
Zucaro, R., Pontrandolfi, A., Gallinoni, C., Vollaro, M., Dodaro, G.M., Pacicco, C.L., Maurano, L. 2014. Atlas of Italian irrigation systems. Rome, Istituto Nazionale di Economia Agraria.
How to Cite
Longo-Minnolo, G. (2024) “Satellite remote sensing for monitoring citrus orchards water requirements at the irrigation district scale”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1623.
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