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The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

Published online by Cambridge University Press:  09 October 2017

G. Papadavid  and
L. Toulios
G. Papadavid*
Affiliation:
Agricultural Research Institute of Cyprus, Nicosia, Cyprus
L. Toulios
Affiliation:
Hellenic Agricultural Organisation ‘DEMETER’, Industrial & Fodder Crops Institute, Larissa, Greece
*
Author for correspondence: G. Papadavid, E-mail: [email protected]
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Abstract

Remote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.

The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.

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. 599 - 617
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Copyright
Copyright © Cambridge University Press 2017 

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References

Agapiou, A, Papadavid, G and Hadjimitsis, DG (2009) Integration of wireless sensor network and remote sensing for monitoring and determining irrigation demand in Cyprus. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, Proc. of SPIE Vol. 7472 (Eds Neale, C. M. U. & Maltese, A.), Article CID number 74720F. Bellingham, WA: SPIE. doi: 10.1117/12.830554.Google Scholar
Alexandridis, T and Chemin, Y (2001) Irrigation water consumption through remote sensing. Comparison at different scales in zhanghe irrigation system, China. Transactions on Ecology and the Environment 48, 59–68.Google Scholar
Atkinson, PM, Jeganathan, C, Dash, J and Atzberger, C (2012) Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment 123, 400–417.Google Scholar
Atzberger, C (2013) Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sensing 5, 949–981.Google Scholar
Balaghi, R, Tychon, B, Eerens, H and Jlibene, M (2008) Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation 10, 438–452.Google Scholar
Bandara, K. M. P. S. (2006) Assessing irrigation performance by using remote sensing. Doctoral thesis, Wageningen University, Wageningen, The Netherlands.Google Scholar
Bannari, A, Morin, D, Huette, AR and Bonn, F (1995) A review of vegetation indices. Remote Sensing Reviews 13, 95–120.Google Scholar
Baret, F, Hagolle, O, Geiger, B, Bicheron, P, Miras, B, Huc, M, Berthelot, B, Nino, F, Weiss, M, Samain, O, Roujean, JL and Leroy, M (2007) LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: principles of the algorithm. Remote Sensing of Environment 110, 275–286.Google Scholar
Bastiaanssen, WGM (2000) SEBAL-based sensible and latent heat fluxes in the irrigated gediz basin, Turkey. Journal of Hydrology 229, 87–100.Google Scholar
Bastiaanssen, WGM, Menenti, M, Feddes, RA and Holtslag, AAM (1998) A remote sensing surface energy balance algorithm for land (SEBAL), part 1: formulation. Journal of Hydrology 212–213, 198–212.Google Scholar
Bastiaanssen, WGM, Noordman, EJM, Pelgrum, H, David, G, Thoreson, BP and Allen, RG (2005) SEBAL model with remotely sensed data to improve water resources management under actual field conditions. Journal of Irrigation and Drainage Engineering 131, 85–93.Google Scholar
Bradley, BA, Jacob, RW, Hermance, JF and Mustard, JF (2007) A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment 106, 137–145.Google Scholar
Bregaglio, S, Frasso, N, Pagani, V, Stella, T, Francone, C, Cappelli, G, Acutis, M, Balaghi, R, Ouabbou, H, Paleari, L and Confalonieri, R (2015) New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco. Agronomy for Sustainable Development 35, 157–167.Google Scholar
Carlson, TN (1986) Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements. Remote Sensing Reviews 1, 197–247.Google Scholar
Clevers, J. G. P. W. (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
D'Urso, G and Menenti, M (1995) Mapping crop coefficients in irrigated areas from landsat TM images. In Proceedings SPIE 2585: Remote Sensing for Agriculture, Forestry, and Natural Resources (Eds Engman, E. T., Guyot, G. & Marino, C. M.), pp. 41–47. Bellingham, WA: SPIE. doi: 10.1117/12.227167.Google Scholar
Funk, C and Budde, EM (2009) Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sensing of Environment 113, 115–125.Google Scholar
Glenn, EP, Huete, AR, Nagler, PL and Nelson, SG (2008) Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors 8, 2136–2160.Google Scholar
Grains Research and Development Corporation (GRDC) (2013) The Science of Crossing and Crops: How Modern Science is Helping Produce More Food in A Changing Climate. Kingston, Australia: GRDC. Available from: https://grdc.com.au/__data/assets/pdf_file/0031/227848/grdc-science-of-crossing-and-crops-fa.pdf.pdf (verified 21 August 2017).Google Scholar
Haboudane, D, Miller, JR, Pattey, E, Zarco-Tejada, PJ and Strachan, IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352.Google Scholar
Hadjimitsis, DG, Clayton, CRI, Retalis, A and Spanos, K (2000) Investigating the potential of using satellite remote sensing for the assessment of water quality in large dams, and irrigation demand, in Cyprus. In Proceedings of the 26th Annual Conference and Exhibition of the Remote Sensing Society, RSS2000, University of Leicester, UK. CD-ROM. Available from: https://www.researchgate.net/publication/299439730_Investigating_the_potential_of_using_satellite_remote_sensing_for_the_assessment_of_water_quality_in_large_dams_and_irrigation_demand_in_Cyprus (verified 21 August 2017).Google Scholar
Hadjimitsis, DG, Papadavid, G, Themistocleous, K, Kounoudes, A and Toulios, L (2008) Estimating irrigation demand using satellite remote sensing: a case study of paphos district area in Cyprus. In Remote Sensing for Agriculture, Ecosystems, and Hydrology X, Proceedings of SPIE 7104 (Eds Neale, C. M. U., Owe, M. & D'Urso, G.), article CID number 71040I. Bellingham, WA: SPIE. doi: 10.1117/12.800366.Google Scholar
Hayes, MJ and Decker, WL (1996) Using NOAA AVHRR data to estimate maize production in the United States corn belt. International Journal of Remote Sensing 17, 3189–3200.Google Scholar
Hoekstra, AY, Chapagain, AK, Aldaya, MM and Mekonnen, MM (2009) Water Footprint Manual: State of the Art 2009. Enschede, The Netherlands: Water Footprint Network.Google Scholar
Hoekstra, AY, Chapagain, AK, Aldaya, MM and Mekonnen, MM (2011) The Water Footprint Assessment Manual: Setting the Global Standard. London, UK: Earthscan.Google Scholar
Huete, AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.Google Scholar
Kite, GW and Droogers, P (2000) Comparing evapotranspiration estimates from satellites, hydrological models and field data. Journal of Hydrology 229, 3–18.Google Scholar
Lambert, R, Peeters, A and Toussaint, B (1999) LAI evolution of a perennial ryegrass crop estimated from the sum of temperatures in spring time. Agricultural & Forest Meteorology 97, 1–8.Google Scholar
Lei, SG, Bian, ZF, Daniels, JL and Liu, DL (2014) Improved spatial resolution in soil moisture retrieval at arid mining area using apparent thermal inertia. Transactions of Nonferrous Metals Society of China 24, 1866–1873.Google Scholar
Meroni, M, Marinho, E, Sghaier, N, Verstrate, MM and Leo, O (2013) Remote sensing based yield estimation in a stochastic framework – case study of durum wheat in Tunisia. Remote Sensing 5, 539–557.Google Scholar
Metochis, C (1997) Assessment of Irrigation Water Needs of Main Crops of Cyprus. Nicosia, Cyprus: Cyprus Agricultural Research Institute Series, Ministry of Agriculture, Natural Resources and Environment.Google Scholar
Minacapilli, M, Iovino, M and D'Urso, G (2008) A distributed agro-hydrological model for irrigation water demand assessment. Agricultural Water Management 95, 123–132.Google Scholar
Minacapilli, M, Agnese, C, Blanda, F, Cammalleri, C, Ciraolo, G, Urso, GD, Iovino, M, Pumo, D, Provenzano, G and Rallo, G (2009) Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote sensing based surface energy balance models. Hydrology and Earth System Sciences 13, 1061–1074.Google Scholar
NASA (1978) Heat Capacity Mapping Mission User's Guide. Greenbelt, Maryland: Goddard Space Flight Center, NASA.Google Scholar
Papadavid, G (2012) Estimating evapotranspiration for annual crops in Cyprus using remote sensing. Ph.D. Thesis, Department of Civil Engineering and Geomatics, Cyprus University of Technology, Lemesos, Cyprus.Google Scholar
Papadavid, G and Hadjimitsis, D (2014) An image based method for crop yield prediction using remotely sensed and crop canopy data: the case of Paphos district, western Cyprus. In Proceedings of the SPIE vol. 9229, Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014) (Eds Hadjimitsis, D. G., Themistocleous, K., Michaelides, S. & Papadavid, G.), article CID number 92290Z. Bellingham, WA: SPIE. doi: 10.1117/12.2068667.Google Scholar
Papadavid, G and Hadjimitsis, DG (2009) Spectral signature measurements during the whole life cycle of annual crops and sustainable irrigation management over Cyprus using remote sensing and spectro-radiometric data: the cases of spring potatoes and peas. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XI: Proceedings of SPIE vol. 7472 (Eds Neale, C. M. U. & Maltese, A.), Article CID number 747215. Bellingham, WA: SPIE. doi: 10.1117/12.830552.Google Scholar
Papadavid, G, Hadjimitsis, D, Themistocleous, K and Toulios, L (2010) Spectral vegetation indices from field spectroscopy intended for evapotranspiration purposes for spring potatoes in Cyprus. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XII, Proceedings of SPIE vol. 7824 (Eds Neale, C. M. U. & Maltese, A.), article CID number 782410. Bellingham, WA: SPIE. doi: 10.1117/12.865126.Google Scholar
Papadavid, G, Hadjimitsis, D, Toulios, L and Michaelides, S (2011 a). Mapping potatoes crop height and LAI through vegetation indices using remote sensing in Cyprus. Journal of Applied Remote Sensing 5, 053526. DOI: 10.1117/1.3596388.Google Scholar
Papadavid, G, Hadjimitsis, DG, Perdikou, S, Michaelides, S, Toulios, L and Seraphides, N (2011 b). Use of field spectroscopy for exploring the impact of atmospheric effects on Landsat 5 TM/7 ETM+ satellite images intended for hydrological purposes in Cyprus. GIScience and Remote Sensing 48, 280–298.Google Scholar
Papastergiadou, ES, Retalis, A, Apostolakis, A and Georgiadis, T (2008) Environmental monitoring of spatio-temporal changes using remote sensing and GIS in a Mediterranean wetland of northern Greece. Water Resources Management 22, 579–594.Google Scholar
Price, JC (1992) Estimating leaf area index from remotely sensed data. In Proceedings of the 12th Annual International Geoscience & Remote Sensing Symposium, 1992 (IGARSS ‘92). Vol. 2. (Eds Williamson, R. & Stein, T.), pp. 1500–1502.Google Scholar
Qi, J, Kerr, YH, Moran, MS, Weltz, M, Huete, AR, Sorooshian, S and Bryant, R (2000) Leaf area index estimates using remotely sensed data and BRDF models in a semiarid region. Remote Sensing of Environment 73, 18–30.Google Scholar
Qin, J, Yang, K, Lu, N, Chen, Y, Zhao, L and Han, M (2013) Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia. Remote Sensing of Environment 138, 1–9.Google Scholar
Quarmby, NA, Milnes, M, Hindle, TL and Silleos, N (1993) The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing 14, 199–210.Google Scholar
Roerink, GJ, Bastiaanssen, WGM, Chambouleyron, J and Menenti, M (1997) Relating crop water consumption to irrigation water supply by remote sensing. Water Resources Management 11, 445–465.Google Scholar
Romaguera, M, Hoekstra, AY, Su, Z, Krol, MS and Salama, MS (2010) Potential of using remote sensing techniques for global assessment of water footprint of crops. Remote Sensing 2, 1177–1196.Google Scholar
Running, SW and Coughlan, JC (1988) A general model of forest ecosystem processes for regional applications. I. Hydrological balance, canopy gas exchange and primary production processes. Ecological Modelling 42, 125–154.Google Scholar
Sakamoto, T, Yokozawa, M, Toritani, H, Shibayama, M, Ishitsuka, N and Ohno, H (2005) A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment 96, 366–374.Google Scholar
Santos, C, Lorite, IJ, Allen, RG and Tasumi, M (2012) Aerodynamic parameterization of the satellite-based energy balance (METRIC) model for ET estimation in rainfed olive orchards of Andalusia, Spain. Water Resources Management 26, 3267–3283.Google Scholar
Soliman, A, Heck, RJ, Brenning, A, Brown, R and Miller, S (2013) Remote sensing of soil moisture in vineyards using airborne and ground-based thermal inertia data. Remote Sensing 5, 3729–3748.Google Scholar
Spiliotopoulos, M, Loukas, A and Vasiliades, L (2008) Actual evapotranspiration estimation from satellite-based surface energy balance model in Thessaly, Greece. Fifth EGU General Assembly 2008: Geophysical Research Abstracts 10, 08278.Google Scholar
Stancalie, G, Nertan, A and Toulios, L (2010) Satellite based methods for the estimation of leaf area index. In Satellite Data Availability, Methods and Challenges for the Assessment of Climate Change and Variability Impacts on Agriculture (Eds Toulios, L. & Stancalie, G.), pp. 49–74. Larissa, Greece: COST Office, ESF.Google Scholar
Tiktak, A and Van Grinsven, HJM (1995) Review of sixteen forest–soil–atmosphere models. Ecological Modelling 83, 35–53.Google Scholar
Tramutoli, V, Claps, P, Marella, M, Pergola, C and Sileo, C (2000) Feasibility of hydrological application of thermal inertia from remote sensing. In Proceedings of the 2nd Plinius Conference on Mediterranean Storms, Siena, Italy, 16–18 October (Eds Mugnai, A., Guzzetti, F. & Roth, G.), pp. 363–375. Potenza: DIFA-Contrada Macchia Romana.Google Scholar
Watson, DJ (1947) Comparative physiological studies on growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Annals of Botany 11, 41–76.Google Scholar
Zhao, W and Li, ZL (2013) Sensitivity study of soil moisture on the temporal evolution of surface temperature over bare surfaces. International Journal of Remote Sensing 34, 3314–3331.Google Scholar