Application of Satellite Data in Simulating Water Productivity in Rice Fields

Document Type : Full Length Article

Authors

1 Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization, Rasht, Iran

2 Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran

3 Water Engineering Department, Lahijan Branch, Islamic Azad University, Lahijan, Iran

4 Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran

Abstract

Background and Objectives
So far, many researches have been conducted with the aim of reducing water consumption and evaporanspiration and increasing water productivity in rice cultivation, which have led to the introduction of alternative irrigation methods such Alternating wetting and drying. Determining the best irrigation method in each region and evaluating different scenarios of agricultural factors on water productivity in rice cultivation requires expensive and time-consuming field research in the same region. Researchers have suggested the use of Crop Growth Models to solve this problem. The ORYZA2000 model has been specially developed for use in rice research. The DSSAT model is also one of the most famous and widely used crop growth models, which has a special program for simulating rice. The results of researches using these models showed that the ORYZA2000 model has acceptable accuracy at the filed level, but its accuracy decreases on a large scale. The proposed solution to solve this problem is to extract vegetation indices from satellite images according to the region and land cover. Vegetation indices obtained from remote sensing images can be used in leaf Area Index (LAI) estimation and crop yield modeling. Since most of the research conducted in the field of paddy yield modeling has been done at the field level and in completely homogeneous conditions, the generalization of these results to wider levels due to the loss of homogeneity in the conditions of agricultural and irrigation management and soil type, requires the use of innovative methods in determining paddy yield. Therefore, the aim of the current research is the feasibility of using remote sensing and satellite images in order to improve the accuracy of paddy yield simulation by the ORYZA2000 and DSSAT models on a large scale (paddy fields of Some-Sara city, Guilan province).
Methodology
All the information needed for the calibration and validation of the ORYZA2000 and DSSAT models was obtained from the previous research conducted in the lands of the Rice Research Institute of Iran during the years. In order to evaluate the performance of the model on a large scale, 44 farms were selected from the farms of Some-Sara city of Gilan province. Agronomic management information was collected by surveillance from farmers in the form of a questionnaire including: nursery day, No. of days in nursery, No. of seedlings per hill, No. of hills per m2, transplanting day, flowering of 50%, maturity day, fertilizer use and planting date. Finally, paddy yield was sampled and measured in these fields. Water use information was obtained from the Regional Water Corporation of Guilan province and entered in the model. The model was implemented in each of these fields and the performance value predicted by the model was compared with its measured value. Remote sensing data were obtained through the images of LANDSAT 5 and LANDSAT 7 satellites on the dates corresponding to the rice crop stages, including the flowering and harvest stages, and the LAI was extracted and provided to the model to increase its simulation accuracy. After correcting the model using satellite images, the response of paddy yield and water productivity in the region with the scenarios of changing the height of irrigation water (regardless of puddling operation before hand-transplanting) including 250, 300, 400 and 500 mm were considered and the irrigation program in all the selected farms of Some-Sara region was entered into the model based on these scenarios. Finally, paddy yield and WP in these fields based on them was estimated.
Findings
The results of running the models in field scale showed that the NRMSE values for the ORYZA2000 and DSSAT models were 11 and 9 percent, respectively, in calibration and 9 and 8 percent, respectively, in validation. The results obtained from the estimation of paddy yield in 44 farms on a large scale by models and comparing with the measured data showed that the maximum value of the yield compared to its minimum is much lower than the actual value, but the average simulated yield by the model is very close to the average yield observed in the region. Correcting model coefficients by combining Leaf Area Index data obtained from satellite images with the models has significantly improved yield estimation (from 23.2 to 10.8 percent in the ORYZA2000 model and from 21 to 12.7 percent in the DSSAT model). Although the average simulated yield after correcting the model is slightly more different than the condition of not using satellite images, the use of satellite images has led to the minimum and maximum simulated yield when using satellite images in the form significantly improved. The results of applying the irrigation scenarios showed that the maximum water productivity is achieved in the amount of water consumed of 300 mm, but this means about 17% reduction in yield. Also, water consumption equal to 350 mm results in a 10% decrease in performance; But since the water consumption of 400 mm during the growing season did not show any decrease in yield and more water consumption did not increase the yield, therefore the recommendation of using 400 mm of water consumption during the growing season is more acceptable to It seems.
Conclusion
The results of the present study showed that after recalibrating the Oryza2000 and DSSAT models at the field level, theirs extension to a wider area using remote sensing data through LAI brings the simulation results of the model closer to the real conditions and can be used to evaluate different scenarios of increasing water productivity.

Keywords

Main Subjects

References

Aalaee Bazkiaee, P., Kamkar, B., Amiri, E., Kazemi, H. & Rezaei, M. (2021). Effect of irrigation management and planting date on yield and productivity of rice (Oryza sativa L.). Journal of Crop Production, 12(4), 157-170. [In Persian]. doi:10.22069/ejcp.2020.16513.2231
Aalaee Bazkiaei, P., Kamkar, B., Amiri, E., Kazemi, H., & Rezaei, M. (2020). Evaluation of ORYZA2000 model in yield simulation and production productivity of rice under crop managements. Journal of Water and Soil Conservation, 27(1), 49-69. [In Persian]. doi: 10.22069/jwsc.2020.16036.3126
Abdi, A., Asadi Kapourchal, S., Vazifedoust, M., Rezaei, M. and Egdernezhad, A. (2021). Investigation capability of updated SWAP model with satellite images and AquaCrop model in simulating the Hashemi rice yield in Guilan province. Water Management in Agriculture, 8(1), 89-102. [In Persian]. doi: 20.1001.1.24764531.2021.8.1.8.6
Aggarwal, P.K., and Mall, R.K. (2002). Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Climatic Change, 52(3), 331-343. doi: 10.1023/A:1013714506779
Ahmad, S., Ahmad, A., Soler, C.M.T., Ali, H., Zia-Ul-Haq, M., Anothai, J., and Hasanuzzaman, M. (2012). Application of the CSM-CERES-Rice model for evaluation of plant density and nitrogen management of fine transplanted rice for an irrigated semiarid environment. Precision Agriculture, 13, 200-218. doi: 10.1007/s11119-011-9238-1
Ali, A. M., & Aboelghar, M. (2019). Comparative analysis of different methods of leaf area index estimation of Strawberry under Egyptian condition. International Journal of Advanced Remote Sensing and GIS, 8(1), 2963-2970 doi: https://doi.org/10.23953/cloud.ijarsg.405
Amiri, E. & Rezaei, M. (2013). Evaluation of Water Balance Components and Water Productivity of Rice under Interval Irrigation and Nitrogen Fertilizer Conditions. Iranian Journal of lrrigation and Drainage. 4 (6): 306-315. [In Persian].
Amiri, E., Rezaei, M. & Bannayan Awal, M. (2011). Evaluation of the Rice Growth Model ORYZA2000 under Nitrogen and Water Limited Conditions (Calibration and Validation). Water and Soil, 25(4), 757-769. [In Persian]. doi: 10.22067/jsw.v0i0.10206
Amiri, E., Rezaei, M., Bannayan, M. & Soufizadeh, S. (2013). Calibration and Evaluation of CERES Rice Model under Different Nitrogen and Water Management Options in Semi-Mediterranean Climate Condition. Communications in Soil Science and Plant Analysis, 44:1814–1830.
Amiri, E., Rezaei, M., Rezaei, E. E., & Bannayan, M. (2014). Evaluation of Ceres-Rice, Aquacrop and Oryza2000 models in simulation of rice yield response to different irrigation and nitrogen management strategies. Journal of Plant Nutrition, 37(11), 1749-1769. doi: 10.1080/01904167.2014.888750
Arora, V.K. (2006). Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment. Agricultural Water Management, 83: 51–57. doi: 10.1016/j.agwat.2005.09.004.
Badsar, M., Kamkar, B., Soltani, A. & Abdi, O. (2017). Yield gap estimation in wheat-grown fields using GIS and RS approach and SSM model (A case study: Qaresso basin, Gorgan, Iran). Cereal Research, 7(2), 195-215. [In Persian]. doi: 10.22124/c.2017.2547
Bai, L., Long, D., & Yan, L. (2019). Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55(2), 1105-1128. doi: 10.1029/2018WR024162
Bouman, B.A., Humphreys, E., Tuong, T.P., & Barker, R. (2007). Rice and water. Advances in agronomy, 92, 187-237. doi: 10.1016/S0065-2113(04)92004-4
Cai, X. & Rosegrant, M.W. (2003). World water productivity: Current situation and future options. In Water Productivity in Agriculture: Limits and Opportunities for Improvement; Kijne, J.W., Barker, R., Molden, D., Eds.; CABI: Wallingford, UK; International WaterManagement Institute (IWMI): Colombo, Sri Lanka, pp. 163–178.
Cheyglinted, S., Ranamukhaarachchi, S. L., & Singh, G. (2001). Assessment of the CERES-Rice model for rice production in the Central Plain of Thailand. The Journal of Agricultural Science, 137(3), 289-298. doi: 10.1017/S0021859601001319
Cui, Z., Zhang, H., Chen, X., Zhang, C., Ma, W., Huang, C., & Dou, Z. (2018). Pursuing sustainable productivity with millions of smallholder farmers. Nature, 555(7696), 363-366. doi: 10.1038/nature25785
Davatgar, N. (2010). Predicting rice plant performance under water limitation conditions using plant growth and development simulation models at a regional scale. PhD thesis in soil science, Faculty of Agriculture, University of Tabriz. 250 pages. [In Persian].
Dente, L., Satalino, G., Mattia, F., & Rinaldi, M. (2008). Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote sensing of Environment, 112(4), 1395-1407. doi: 10.1016/j.rse.2007.05.023
Fatimah, K. (2018). Evaluation of Agricultural Subsidies and the Welfare of Farmers; Malaysia Agricultural Subsidies Report 2018; IDEAS Policy Research Berhad: Kuala Lumpur, Malaysia: pp. 1–64.
Huai, H., Zhang, Q., Li, Z., Liang, L., & Tang, X., (2024). Analysis of Crop Irrigation Water Requirements and Water Scarcity Footprint in the Beijing–Tianjin–Hebei Region Based on the GeoSim–AquaCrop Model. Agronomy, 14 (192): 1-12.  doi: 10.3390/agronomy14010192
IRRI. (2008). Background Paper: The Rice Crisis: What Needs to Be Done? IRRI, Los Baños, Philippines, www.irri.org/12pp
Ishfaq, M., Farooq, M., Zulfiqar, U., Hussain, S., Akbar, N., Nawaz, A., & Anjum, S. A. (2020). Alternate wetting and drying: A water-saving and ecofriendly rice production system. Agricultural Water Management, 241, 106363. doi: org/10.1016/j.agwat.2020.106363
Jin, N., Tao, B., Ren, W., He, L., Zhang, D., Wang, D., & Yu, Q. (2022). Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data. Agricultural water management, 266, 107583.
Kawakita, S., Yamasaki, M., Teratani, R., Yabe, S., Kajiya-Kanegae, H., Yoshida, H., Fushimi, E. & Nakagawa, H. (2023). Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models. Agricultural and Forest Meteorology. 334, 109821. doi: 10.1016/j.agrformet.2023.109821
Li, T., Humphreys, E., Gill, G., & Kukal, S. S. (2011). Evaluation and application of ORYZA2000 for irrigation scheduling of puddled transplanted rice in North West India. Field Crops Research, 122(2), 104-117. https://doi.org/10.1016/j.fcr.2011.03.004
Liu, H. L., Yang, J. Y., Drury, C. A., Reynolds, W. D., Tan, C. S., Bai, Y. L., & Hoogenboom, G. (2011). Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutrient cycling in agroecosystems, 89, 313-328. doi: 10.1007/s10705-010-9396-y
Mojid, M. A., & Mainuddin, M. (2021). Water-saving agricultural technologies: Regional hydrology outcomes and knowledge gaps in the eastern Gangetic Plains-A review. Water, 13(5), 636. doi: 10.3390/w13050636
Nain, A. S., & Kersebaum, K. C. (2007). Calibration and validation of CERES model for simulating water and nutrients in Germany Ajeet Singh Nain¹ and Kurt Christian Kersebaum. In Modelling water and nutrient dynamics in soil-crop systems: Applications of different models to common data sets-Proceedings of a workshop held 2004 in Müncheberg, Germany (p. 161). Springer Science & Business Media.
Nisar, S., & Arora, V. K. (2018). Analysing dry-seeded rice responses to planting time and irrigation regimes in a subtropical environment using ORYZA2000 model. Agricultural Research, 7, 424-431. doi: 10.1007/s40003-018-0331-9
Peng, J., Liu, T., Chen, J., Li, Z., Ling, Y., De Wulf, A., & De Maeyer, P. (2023). The conflicts of agricultural water supply and demand under climate change in a typical arid land watershed of Central Asia. Journal of Hydrology: Regional Studies, 47, 101384. doi: 10.1016/j.ejrh.2023.101384
Piao, C., Peng, L., Zhou, L., Ma, D., Friedlingstein, L., Tan, Y., & Zhang, F. (2010). The impacts of climate change on water resources and agriculture in China. Nature, 467(7311), 43-51. doi:10.1038/nature09364
Prathumchai, K., Nagai, M., Tripathi, N. K., & Sasaki, N. (2018). Forecasting transplanted rice yield at the farm scale using moderate-resolution satellite imagery and the AquaCrop model: a case study of a rice seed production community in Thailand. ISPRS International. Journal of Geo-Information, 7(2), 73. doi: 10.3390/ijgi7020073
Rao, N.R., Garg, P.K., & Ghosh, S.K. (2006). Estimation and comparison of leaf area index of agricultural crops using IRS LISS-III and EO-1 Hyperion images. Journal of the Indian Society of Remote Sensing, 34(1): 69-78.
Rezaei, M. (2008). The final report of the research project on the effect of intermittent irrigation and different amounts of nitrogen fertilizer on the yield of Hashemi local variety rice. Publications of Rice Research Institute of Iran. [In Persian].
Rezaei, M. & Noori, M. (2002). Effect of irrigation interval on water use and rice yield in Guilan. 11th Seminar of Iranian National Committee on Irrigation and Drainage. Iran, Tehran. [In Persian].
Rezaei, M., Shahnazari, A., Raeini Sarjaz, M., & Vazifedoust, M. (2016). Improving agricultural management in a large-scale paddy field by using remotely sensing data in the CERES-Rice model. Irrigation and drainage, 65(2): 224-228. doi: 10.1002/ird.1961
Richards, M. & Sander, B. (2014). Alternate wetting and drying in irrigated rice. IRRI CCAFS and Info Notes. http://www.agritech.tnau.ac.in/agriculture/pdf/csa_pdf/Alternate_wetting_and_drying_in_irrigated_rice_InfoNote.pdf
Saadati, Z., Pirmoradian, N. & Rezaei, M. (2011). Calibration and validation of AquaCrop model in rice growth simulation under different irrigation managements. ICID 21st International Congress on Irrigation and Drainage, Tehran, Iran.
Sandhu, R., & Irmak, S. (2019). Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation. Agricultural Water Management. 97 (11), 1838–1846. doi: 10.1016/j.agwat.2019.105687
Soltani, A., & Hoogenboom, G. (2007). Assessing crop management options with crop simulation models based on generated weather data. Field Crops Research, 103(3), 198-207. doi: 10.1016/j.fcr.2007.06.003
Timsina, J., & Humphreys. E. (2006). Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review. Agricultural Systems, 90: 5–31. doi: 10.1016/j.agsy.2005.11.007
Wang, H., Zhu, Y., Li, W., Cao, W., & Tian, Y. (2014). Integrating remotely sensed leaf area index and leaf nitrogen accumulation with Rice Grow model based on particle swarm optimization algorithm for rice grain yield assessment. Journal of Applied Remote Sensing, 8(1), 083674-083674. doi: 10.1117/1.JRS.8.083674
Wikarmpapraharn, C. & Kositsakulchai E. (2010). Evaluation of ORYZA2000 and CERES-Rice Models under Potential Growth Condition in the Central Plain of Thailand. Thai Journal of Agricultural Science. 43: 17-29
Xue, C., Yang, X., Bouman, B.A.M., Deng, W., Zhang, Q., Yan, W., Zhang, T., Rouzi, A. & Wang, H. (2008). Optimizing yield, water requirements, and water productivity of aerobic rice for the North China Plain. Irrigation Science, 26, 459–474. doi: 10.1007/s00271-008-0107-2
Yadav, S., Li, T., Humphreys, E., Li, T., Gill, G. and Kukal S.S. (2012). Evaluation of tradeoffs in land and water productivity of dry seeded rice as affected by irrigation schedule. Field Crops Research. 128. 180-190. doi: 10.1016/j.fcr.2012.01.005
Yu, Q., & Cui, Y. (2022). Improvement and testing of ORYZA model water balance modules for alternate wetting and drying irrigation. Agricultural Water Management, 271, 107802. doi: 10.1016/j.agwat.2022.107802
Yuan, S., Peng, S., & Li, T. (2017). Evaluation and application of the ORYZA rice model under different crop managements with high-yielding rice cultivars in central China. Field Crops Research, 212, 115-125. doi: 10.1016/j.fcr.2017.07.010
Zhao, Y., Chen, S. & Shen, S. (2013). Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation. Ecological modelling. 270, 30–42.
Zhuo, W., Fang, S., Gao, X., Wang, L., Wu, D., Fu, S., & Huang, J. (2022). Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation, 106, 102668.
Hydraulics and Water Science
Volume 35, Issue 2
June 2025
Pages 1-13

Files

Share

How to cite

Statistics