MAPPING AGRICULTURAL WATER PRODUCTIVITY FOR CENTER PIVOT IRRIGATION SYSTEMS BASED ON SATELLITE DATA | ||||
| Misr Journal of Agricultural Engineering | ||||
| Article 2, Volume 39, Issue 4, October 2022, Page 509-524 PDF (1.49 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjae.2022.150651.1081 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Mahmoud A. Badr  1; Mohammed A. El-Shirbeny2; Mohammed Y. El-Ansary3; Montaser A. awad4
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| 1PhD Stud., Ag. Eng. Dept., Fac. of Ag., Benha U., Egypt. | ||||
| 2Assoc. Prof. of Ag. Eng., NARSS, Egypt. | ||||
| 3Prof. of Ag. Eng., Ag. Eng. Dept., Fac. of Ag., Benha U., Egypt. | ||||
| 4Assoc. Prof. of Ag. Eng., Ag. Eng. Dept., Fac. of Ag., Benha U., Egypt. | ||||
| Abstract | ||||
| Developing and launching many remote sensing satellites with varying spatial-temporal accuracy, have provided various types of data at spatial, spectral, radiometric, and temporal scales, therefore, mapping agricultural ecosystems' physical and ecological characteristics with high accuracy became available by integrating remote sensing models with meteorological data. The primary goal of this research is to determine the agricultural water productivity under center pivot irrigation systems based on satellite data analysis. Landsat 8 images, agrometeorological stations, and the use of the light Use Efficiency model of Monteith (LUE) and SSEB (Simplified Surface Energy Balance) models were used to calculate the amount of crop production biomass (BIO) and The amount of water consumed, represented by the actual evapotranspiration (ET), respectively, and then, based on ET, the water productivity was determined. (WP = BIO/ET). The average ET, BIO, and WP values in 2021 summer season crops, ranged from 3.01± 1.73 to 4.1 ± 2.35 mm day-1; 96.4 ± 55.4 to 191.6 ± 110.2 kg ha-1 day-1; and 1.64 ± 0.94 to 2.43 ± 1.4 kg m-3, respectively. The average water productivity values for the crops cultivated in the research region ranged from 1.1 to 1.4 Kg m−3 for watermelon, 1.1 to 1.6 Kg m−3 for peanut, 3 to 3.3 Kg m−3 for Alfalfa, and 1.3 to 2.1 Kg m−3 for maize. These findings demonstrate that water productivity estimates derived from remote sensing data may be utilized as an indication for increasing water rationalization via improved land and water management methods. | ||||
| Keywords | ||||
| Evapotranspiration; Biomass; Light Use Efficiency (LUE); Remote sensing; Pivot Irrigation System | ||||
| References | ||||
|
Abuzaid, A. S., & Jahin, H. S. (2021). Implications of irrigation water quality on shallow groundwater in the Nile Delta of Egypt: A human health risk prospective. Environmental Technology & Innovation, 22, 101383.
Acharya, B., & Sharma, V. (2021). Comparison of Satellite Driven Surface Energy Balance Models in Estimating Crop Evapotranspiration in Semi-Arid to Arid Inter-Mountain Region. Remote Sensing, 13(9), 1822.
Adak, T., Kumar, G., Chakravarty, N. V. K., Katiyar, R. K., Deshmukh, P. S., & Joshi, H. C. (2013). Biomass and biomass water use efficiency in oilseed crop (Brassica juncea L.) under semi-arid microenvironments. biomass and bioenergy, 51, 154-162.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of irrigation and drainage engineering, 133(4), 380-394.
Antônio, H., Hernandez, F. B., & Lopes, H. L. (2012, October). Application of Landsat images for quantifying the energy balance under conditions of land use changes in the semi-arid region of Brazil. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV (Vol. 8531, p. 85310P). International Society for Optics and Photonics.
Bastiaanssen, W. G., & Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, ecosystems & environment, 94(3), 321-340.
Bastiaanssen, W. G., Thiruvengadachari, S., Sakthivadivel, R., & Molden, D. J. (1999). Satellite remote sensing for estimating productivities of land and water. International Journal of Water Resources Development, 15(1-2), 181-194.
Beillouin, D., Schauberger, B., Bastos, A., Ciais, P., & Makowski, D. (2020). Impact of extreme weather conditions on European crop production in 2018. Philosophical Transactions of the Royal Society B, 375(1810), 20190510.
Bhattacharjee, R., Choubey, A., Das, N., Ohri, A., Dwivedi, S. B., & Gaur, S. (2021). Analysis of the groundwater scenario with respect to the crop water productivity for the Betwa–Dhasan river basin, Bundelkhand using remote sensing techniques. Journal of Earth System Science, 130(4), 1-17.
El-Shirbeny, M. A., Ali, A., Savin, I., Poddubskiy, A., & Dokukin, P. (2021). Agricultural water monitoring for water management under pivot irrigation system using spatial techniques. Earth Systems and Environment, 5(2), 341-351.
El-Shirbeny, M. A., Mohamed, E. S., & Negm, A. (2018). Estimation of crops water consumptions using remote sensing with case studies from Egypt. Conventional water resources and agriculture in Egypt, 451-469.
El-Shirbeny, M. A., Saleh, N. H., & Ali, A. M. (2014). Estimation of potential crop evapotranspiration using remote sensing techniques. In Proceedings of the 10th International Conference of AARSE (pp. 460-468).
FAO, 2019. The State of Food Security and Nutrition in the World, Safeguarding Against Economic Slowdowns and Downturns. Food and Agriculture Organization of the United Nations,, Rome.
Fonseca, A., Andrade, C., & Santos, J. A. (2022). Agricultural Water Security under Climate Change in the Iberian Peninsula. Water, 14(5), 768.
Gabr, M. E., & Fattouh, E. M. (2021). Assessment of Irrigation Management Practices Using FAO-CROPWAT 8, Case Studies: Tina Plain and East South El-Kantara, Sinai, Egypt. Ain Shams Engineering Journal, 12(2), 1623-1636.
Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., ... & Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1), 28-41.
Gardner, F. P., Pearce, R. B., & Mitchell, R. L. (2017). Physiology of crop plants. Scientific publishers.
Jacovides, C. P., Tymvios, F. S., Asimakopoulos, D. N., Theofilou, K. M., & Pashiardes, S. (2003). Global photosynthetically active radiation and its relationship with global solar radiation in the Eastern Mediterranean basin. Theoretical and Applied Climatology, 74(3), 227-233.
Jiang, Y., Xu, X., Huang, Q., Huo, Z., Huang, G., (2015). Assessment of irrigation performance and water productivity in irrigated areas of the middle Heihe River basin using a distributed agro-hydrological model. Agric. Water Manag. 147, 67–81. Li, Z.L., Tang, R., Wan, Z., Bi, Y., Zhou, C., Tang, B., Yan, G., Zhang, X., (2009). A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors. 9, 3801–3853.
Liou, Y. A., & Kar, S. K. (2014). Evapotranspiration estimation with remote sensing and various surface energy balance algorithms—A review. Energies, 7(5), 2821-2849.
Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. Journal of applied ecology, 9(3), 747-766.
Norman, J. M., Divakarla, M., & Goel, N. S. (1995). Algorithms for extracting information from remote thermal-IR observations of the earth's surface. Remote Sensing of Environment, 51(1), 157-168.
Sadras, V.O., Cassman, K.G., Grassini, P., Hall, A.J., Bastiaanssen, W.G.M., Laborte, A.G., Milne, A.E., Sileshi, G., Steduto, P., (2015). Yield Gap Analysis of Field Crops, Methods and Case Studies (FAO Water Reports 41). FAO, Rome.
Senay, G. B., & Verdin, J. (2003). Characterization of yield reduction in Ethiopia using a GIS-based crop water balance model. Canadian Journal of Remote Sensing, 29(6), 687-692. Swinnen, E., Van Hoolst, R., (2018). Gio Global Land Component - Lot I “Operation of the Global Land Component”- Framework Service Contract N° 388533 (JRC). Algorithm Theoretical Basis Document, Dry Matter Productivity. Collection 300 m, Version 1. VITO, EC Copernicus Global Land, Brussels.
Teixeira, A. D. C., & Bassoi, L. H. (2009 a). Crop water productivity in semi-arid regions: from field to large scales. Embrapa Semiárido-Artigo em periódico indexado (ALICE). Teixeira, A. D. C., Bastiaanssen, W. G., Ahmad, M., & Bos, M. G. (2009 b). Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Sao Francisco River basin, Brazil: Part A: Calibration and validation. Agricultural and forest meteorology, 149(3-4), 462-476.
Teixeira, A.H. de C., Simão, F.R., Leivas, J.F., Gomide, R.L., Reis, J.B.R.S., Kobayashi, M.K., Oliveira, F.G., (2018). Water productivity modeling by remote sensing in the semiarid region of Minas Gerais State, Brazil. In: Arman, H., Yuksel, I. (Eds.), Arid Environments and Sustainability, 1st ed. InTec, London, pp. 94–108.
Teixeira, D. C., Antônio, H., Scherer-Warren, M., Hernandez, F. B., Andrade, R. G., & Leivas, J. F. (2013). Large-scale water productivity assessments with MODIS images in a changing semi-arid environment: a Brazilian case study. Remote Sensing, 5(11), 5783-5804.
Valor, E., & Caselles, V. (1996). Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote sensing of Environment, 57(3), 167-184.
Verger, A., Baret, F., Weiss, M., (2017). Algorithm theoretical basis document: leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of green vegetation cover (FCover), collection 1km, version 2. Issue 1, 41.
Wang, J., Ma, J., Clarke-Sather, A., & Qu, J. (2018). Estimating Changes in the Green Water Productivity of Cropping Systems in Northern Shaanxi Province in China’s Loess Plateau. Water, 10(9), 1198.
Yuan, M., Zhang, L., Gou, F., Su, Z., Spiertz, J. H. J., & Van Der Werf, W. (2013). Assessment of crop growth and water productivity for five C3 species in semi-arid Inner Mongolia. Agricultural Water Management, 122, 28-38.
Zwart, S. J., Bastiaanssen, W. G., de Fraiture, C., & Molden, D. J. (2010). WATPRO: A remote sensing based model for mapping water productivity of wheat. Agricultural water management, 97(10), 1628-1636. | ||||
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