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
Abstract Views: 22
PDF: 32
Publisher's note
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.
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.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Davide Pognant, Davide Canone, Stefano Ferraris, Actual evaporation estimation from infrared measurement of soil surface temperature , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Vincenzo Bagarello, Vito Ferro, Giuseppe Giordano, ESTIMATING SOIL PARTICLE-SIZE DISTRIBUTION FOR SICILIAN SOILS , Journal of Agricultural Engineering: Vol. 40 No. 3 (2009)
- Flavia Tauro, Andrea Petroselli, Salvatore Grimaldi, Optical sensing for stream flow observations: A review , Journal of Agricultural Engineering: Vol. 49 No. 4 (2018)
- Alvaro Marucci, Danilo Monarca, Massimo Cecchini, Andrea Colantoni, Andrea Cappuccini, Analysis of internal shading degree to a prototype of dynamics photovoltaic greenhouse through simulation software , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Zhiqiang Song, Chuanxing Du, Yongcheng Chen, Dianlei Han, Xinzhong Wang, Development and test of a spring-finger roller-type hot pepper picking header , Journal of Agricultural Engineering: Vol. 55 No. 2 (2024)
- Timothy Denen Akpenpuun, Wook Ho Na, Qazeem Opeyemi Ogunlowo, Anis Rabiu, Misbaudeen Aderemi Adesanya, Kwame Sasu Addae, Hyeon Tae Kim, Hyun-Woo Lee, Effect of glazing configuration as an energy-saving strategy in naturally ventilated greenhouses for strawberry (Seolhyang sp.) cultivation , Journal of Agricultural Engineering: Vol. 52 No. 2 (2021)
- Andrea Rosario Proto, Alois Skoupy, Giorgio Macri, Giuseppe Zimbalatti, Time consumption and productivity of a medium size mobile tower yarder in downhill and uphill configurations: a case study in Czech Republic , Journal of Agricultural Engineering: Vol. 47 No. 4 (2016)
- Salvatore Barbagallo, Giuseppe Luigi Cirelli, Simona Consoli, Vincenzo Tamburino, Attilio Toscano, REMOTE SENSING AND SURFACE ENERGY FLUX MODELS TO DERIVE EVAPOTRANSPIRATION AND CROP COEFFICIENT , Journal of Agricultural Engineering: Vol. 39 No. 2 (2008)
- Stefano Melzi, Edoardo Sabbioni, Michele Vignati, Maurizio Cutini, Massimo Brambilla, Carlo Bisaglia, Eugenio Cavallo, Multibody model of fruit harvesting trucks: comparison with experimental data and rollover analysis , Journal of Agricultural Engineering: Vol. 49 No. 2 (2018)
- Yiming Xiao, Jianhua Wang, Hongyi Xiong, Fangjun Xiao, Renhuan Huang, Licong Hong, Bofei Wu, Jinfeng Zhou, Yongbin Long, Yubin Lan, Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
<< < 11 12 13 14 15 16 17 18 19 20 > >>
You may also start an advanced similarity search for this article.
