Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-27T19:55:42.472Z Has data issue: false hasContentIssue false
  • English
  • Français

Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy

Published online by Cambridge University Press:  28 February 2018

G. B. Chirico ,
A. Pelosi ,
C. De Michele ,
S. Falanga Bolognesi  and
G. D'Urso
G. B. Chirico*
Affiliation:
Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
A. Pelosi
Affiliation:
Department of Civil Engineering, University of Salerno, Fisciano, Italy
C. De Michele
Affiliation:
Ariespace s.r.l., Naples, Italy
S. Falanga Bolognesi
Affiliation:
Ariespace s.r.l., Naples, Italy
G. D'Urso
Affiliation:
Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
*
Author for correspondence: G. B. Chirico, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Irrigation according to reliable estimates of crop water requirements (CWR) is one of the key strategies to ensure long-term sustainability of irrigated agriculture. In southern Mediterranean regions, during the irrigation season, CWR is almost totally controlled by the potential evapotranspiration of the irrigated crop. An innovative system for forecasting crop potential evapotranspiration (ETp) has been implemented recently in the Campania region (southern Italy). The system produces ETp forecasts with a lead time of up to 5 days, by coupling the visible and near-infrared crop imagery with numerical weather prediction outputs of a limited area model. The forecasts are delivered to farmers with a simple and intuitive web app interface, which makes daily real-time ETp maps accessible from desktop computers, tablets and smartphones. Forecast performances were evaluated for maize fields of two farms in two irrigation seasons (2014–2015). The mean absolute bias of the forecasted ETp was <0.3 mm/day and the RMSE was <0.6 mm/day, both for lead times up to 5 days.

Keywords

Crop water requirementsEarth observationforecastingnumerical weather predictionspotential evapotranspiration
Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Themed Issue 5: Themed Issue: Assessment and monitoring of crop water use and productivity under present and future climate , July 2018 , pp. 702 - 710
Check for updates
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, RG, Pereira, LS, Raes, D and Smith, M (1998) Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56. Rome, Italy: FAO.Google Scholar
Allen, RG, Pereira, LS, Howell, TA and Jensen, ME (2011) Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agricultural Water Management 98, 899–920.Google Scholar
Buzzi, A, Fantini, M, Malguzzi, P and Nerozzi, F (1994) Validation of a limited area model in cases of Mediterranean cyclogenesis: surface fields and precipitation scores. Meteorology and Atmospheric Physics 53, 137–153.Google Scholar
Calera, A, Campos, I, Osann, A, D'Urso, G and Menenti, M (2017) Remote sensing for crop water management: from ET modelling to services for the end users. Sensors 17, article no. 1104. 1–25 doi: 10.3390/s17051104.Google Scholar
Chirico, GB, Medina, H and Romano, N (2014) Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: retrieving state profiles with linear and nonlinear numerical schemes. Hydrology and Earth System Sciences 18, 2503–2020.Google Scholar
Clevers, JGPW (1989) The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sensing of Environment 29, 25–37.Google Scholar
Diodato, N, Bellocchi, G, Romano, N and Chirico, GB (2011) How the aggressiveness of rainfalls in the Mediterranean lands is enhanced by climate change. Climate Change 108, 591–599.Google Scholar
D'Urso, G (2010) Current status and perspectives for the estimation of crop water requirements from earth observation. Italian Journal of Agronomy 5, 107–120.Google Scholar
D'Urso, G and Calera Belmonte, A (2006) Operative approaches to determine crop water requirements from Earth observation data: methodologies and applications. AIP Conference Proceedings on Earth Observation for Vegetation Monitoring and Water Management 852, 14–25. https://doi.org/10.1063/1.2349323Google Scholar
D'Urso, G and Menenti, M (1995) Mapping crop coefficients in irrigated areas from Landsat TM images. In Engman, ET, Guyot, G and Marino, CM (eds). Proceedings SPIE 2585, Remote Sensing for Agriculture, Forestry, and Natural Resources. Paris, France: SPIE, pp. 41–47. doi: 10.1117/12.227167.Google Scholar
Furcolo, P, Pelosi, A and Rossi, F (2016) Statistical identification of orographic effects in the regional analysis of extreme rainfall. Hydrological Processes 30, 1342–1353.Google Scholar
GEOSYSTEMS (2017) ATCOR Workflow for IMAGINE 2016 Version 1.0 Step-by-Step Guide. Germering, Germany: Geosystems GmbH. Available at https://www.geosystems.de/fileadmin/redaktion/Software/ATCOR_Workflow/ATCOR_Workflow_for_IMAGINE_Step_by_Step_Guide.pdf (Accessed 5 February 18).Google Scholar
Heinz, I (2008) Co-operative agreements and the EU water framework directive in conjunction with the common agricultural policy. Hydrology and Earth System Sciences Discussions, European Geosciences Union 12, 715–726.Google Scholar
Hornbuckle, JW, Car, NJ, Christen, EW, Stein, TM and Williamson, B (2009) IrriSatSMS. Irrigation Water Management by Satellite and SMS – A Utilisation Framework. CRC for Irrigation Futures Technical Report No. 01/09 and CSIRO Land and Water Science Report No. 04/09. Griffith, NSW, Australia: CSIRO Land and Water.Google Scholar
LI-COR (1992) LAI-2000 Plant Canopy Analyzer Instruction Manual. Lincoln, NE, USA: LiCor Inc. Available at https://www.licor.com/documents/q6hrj6s79psn7o8z2b2s (Accessed 18 January 2018).Google Scholar
Medina, H, Romano, N and Chirico, GB (2014 a) Kalman filters for assimilating near-surface observations into the Richards equation – Part 2: a dual filter approach for simultaneous retrieval of states and parameters. Hydrology and Earth System Sciences 18, 2521–2041.Google Scholar
Medina, H, Romano, N and Chirico, GB (2014 b) Kalman filters for assimilating near-surface observations into the Richards equation – Part 3: retrieving states and parameters from laboratory evaporation experiments. Hydrology and Earth System Sciences 18, 2543–2557.Google Scholar
Montani, A, Cesari, D, Marsigli, C and Paccagnella, T (2011) Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus Series A: Dynamic Meteorology and Oceanography 63, 605–624.Google Scholar
Moran, MS, Inoue, Y and Barnes, EM (1997) Opportunities and limitations for image based remote sensing in precision crop management. Remote Sensing of Environment 61, 319–346.Google Scholar
Pelosi, A and Furcolo, P (2015) An amplification model for the regional estimation of extreme rainfall within orographic areas in Campania region (Italy). Water 7, 6877–6891.Google Scholar
Pelosi, A, Medina, H, Villani, P, D'Urso, G and Chirico, GB (2016) Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system. Agricultural Water Management 178, 106–118.Google Scholar
Pelosi, A, Medina, H, Van den Bergh, J, Vannitsem, S and Chirico, GB (2017) Adaptive Kalman filtering for postprocessing ensemble numerical weather predictions. Monthly Weather Review 145, 4837–4854.Google Scholar
Pereira, LS, Cordery, I and Iacovides, I (2009) Water scarcity concepts. In Pereira, LS, Cordery, I and Iacovides, I (eds). Coping with Water Scarcity: Addressing the Challenges. Dordrecht, The Netherlands: Springer, pp. 7–24.Google Scholar
Preti, F, Forzieri, G and Chirico, GB (2011) Forest cover influence on regional flood frequency assessment in Mediterranean catchments. Hydrology and Earth System Sciences 15, 3077–3090.Google Scholar
Richter, R (1996) A spatially adaptive fast atmospheric correction algorithm. International Journal of Remote Sensing 17, 1201–1214.Google Scholar
Romano, N, Palladino, M and Chirico, GB (2011) Parameterization of a bucket model for soil-vegetation-atmosphere modeling under seasonal climatic regimes. Hydrology and Earth System Sciences 15, 3877–3893.Google Scholar
Stöckle, CO (2001) Environmental Impact of Irrigation: a Review. Pullman, WA, USA: State of Washington Water Research Center, Washington State University.Google Scholar
United Nations World Water Assessment Programme (2016) Water and Jobs. World Water Development Report 2016. Paris, France: UNESCO. Available at http://unesdoc.unesco.org/images/0024/002439/243938e.pdf (Accessed 18 January 2018).Google Scholar
Vanino, S, Pulighe, G, Nino, P, De Michele, C, Falanga Bolognesi, S and D'Urso, G (2015) Estimation of evapotranspiration and crop coefficients of tendone vineyards using multi-sensor remote sensing data in a Mediterranean environment. Remote Sensing 7, 14708–14730.Google Scholar
Vuolo, F, Neugebauer, N, Bolognesi, SF, Atzberger, C and D'Urso, G (2013) Estimation of leaf area index using DEIMOS-1 data: application and transferability of a semi-empirical relationship between two agricultural areas. Remote Sensing 5, 1274–1291.Google Scholar
Vuolo, F, D'Urso, G, De Michele, C, Bianchi, B and Cutting, M (2015) Satellite-based irrigation advisory services: a common tool for different experiences from Europe to Australia. Agricultural Water Management 147, 82–95.Google Scholar
WMO (2012) Guide to Agricultural Meteorological Practices. 2010 Edition, updated 2012. WMO No. 134. Geneva, Switzerland: WMO.Google Scholar
Wriedt, G, Van der Velde, M, Aloe, A and Bouraoui, F (2009) Estimating irrigation water requirements in Europe. Journal of Hydrology 373, 527–544.Google Scholar
Zoccatelli, D, Borga, M, Chirico, GB and Nikolopoulos, EI (2015) The relative role of hillslope and river network routing in the hydrologic response to spatially variable rainfall fields. Journal of Hydrology 531, 349–359.Google Scholar