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برآورد عملکرد کلزا با استفاده از داده های زمینی و سنجش از دور (مطالعه موردی: اراضی منتخب تحت کشت شبکه آبیاری دشت قزوین) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 23، شماره 88، آذر 1401، صفحه 126-148 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2023.360853.1530 | ||
| نویسندگان | ||
| مهدی یونسی1؛ افشین یوسف گمرکچی* 2 | ||
| 1کارشناسی ارشد آبیاری و زهکشی پردیس ابوریحان | ||
| 2استادیار پژوهشی، بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان قزوین، سازمان تحقیقات، آموزش و ترویج کشاورزی، قزوین، ایران | ||
| چکیده | ||
| از کاربردهای مهم سنجش از دور در فعالیت های کشاورزی میتوان به برآورد سطح زیرکشت گیاهان زراعی، تهیۀ نقشۀ کشت، تعیین عوامل مؤثر بر عملکرد محصول و برآورد آن اشاره کرد. در این راستا، مدل هایی که برای برآورد عملکرد محصول ارائه شده اند عموماً مبتنی هستند بر محاسبۀ شاخص های پوشش گیاهی که با استفاده از این شاخصها و با الگوریتمی مشخص میزان محصول برآورد می شود. در این پژوهش، عملکرد محصول کلزا با استفاده از قابلیت‌های سنجش از دور ماهواره لندست8 (سنجنده OLI و TIRS) و سامانه گوگل ارث انجین در اراضی تحت کشت شبکۀ آبیاری دشت قزوین در فصل زراعی 1399-1398 بررسی گردید. داده های میدانی از 12 مزرعۀ تحت کشت کلزا ثبت و اندازه گیری شد. در این زمینه، پس از تصحیح تصویرهای ماهواره لندست8 مربوط به دورۀ رشد، اطلاعات باندهای طیفی آن استخراج و بر اساس مدل باستیانسن عملکرد محصول برآورد گردید. نتایج تحلیل ضریب همبستگی پیرسون نشان داد عملکرد محصول همبستگی معنیداری با شاخص سطح برگ در دو مرحلۀ توسعهای و میانی رشد کلزا دارد که بالاترین میزان همبستگی مرتبط با مرحلۀ میانی رشد محصول با ضریب همبستگی 0/847 بوده است. به سخنی دیگر، طولانیترین زمان ممکن پیش از برداشت که امکان برآورد دقیقتر عملکرد محصول کلزا با استفاده از قابلیت های سنجش از دور وجود داشته، مرتبط با دوره آغاز گلدهی محصول بوده است. نتایج نشان داد مدل باستیانسن در محدودۀ مورد بررسی، قابلیت برآورد عملکرد محصول کلزا با ضریب تبیین 0/91 و RMSE برابر با 444/06 کیلوگرم بر هکتار را دارد. در رویکردی کلی، نتایج پژوهش نشان داد مدل های مبتنی بر قابلیت های سنجش از دور امکان برآورد عملکرد محصول را با دقت مناسب دارند. | ||
| کلیدواژه‌ها | ||
| تصاویر ماهواره‌ای؛ شاخص‌های پوشش گیاهی؛ لندست 8؛ مدل باستیانسن | ||
| عنوان مقاله [English] | ||
| Estimation of rapeseed yield using Geo-data and remote sensing (Case study: Selected Lands cultivated in Qazvin plain irrigation network) | ||
| نویسندگان [English] | ||
| mahdi Younesi1؛ afshin uossef gomrokchi2 | ||
| 1Master of Irrigation and Drainage, Aburihan Campus | ||
| 2Assistant Professor., Agricultural Engineering Research Department, Qazvin Agricultural and Natural Resources Research and Education Center, AREEO, Qazvin, Iran. | ||
| چکیده [English] | ||
| Extended Abstract Introduction Among the applications of remote sensing in agriculture, we can mention the estimation of crop yield, the preparation of the cultivation map, the factors affecting the crop yield. The models presented to estimate the crop yield are generally based on the calculation of vegetation indicators, which are used to estimate the amount of production using these indicators and with a specific algorithm. Researchers have used other methods (in addition to the direct use of vegetation indices) to estimate crop yield. In this regard, we can refer to Bastianssen and Ali's research (Bastianssen and Ali, 2003). This model (Bastiansen model) is a combination of the Monteith model to calculate the absorbed photosynthetic active radiation, the Stanford model to determine the absorbed energy efficiency, and the SEBAL model to describe the spatial-temporal changes of evapotranspiration. Methodology This research was carried out in rapeseed fields in the cultivated lands of Qazvin plain irrigation network. In this research, the fields were selected to cover soil texture, soil salinity, different crop management, irrigation water salinity and different irrigation methods. In order to be able to analyze the leaf area index in the process of crop performance modeling, rapeseed cultivars were the same in all the selected fields. In this research, a hybrid model was used to estimate crop yield, including the Monteith model to calculate the absorbed photosynthetically active radiation (APAR), the Stanford model to determine the light consumption efficiency (LUE), and the surface energy balance algorithm (SEBAL). In order to evaluate the crop yield prediction model, Pearson's correlation coefficient was used between the data to analyze the correlation of yield and leaf area index in different stages of growth. Results and Discussion The analysis of the leaf area index in the studied fields showed that the date of cultivation was one of the most important factors influencing the process of plant phenological growth and consequently the difference in crop yield in the fields. Considering that the potential yield of rapeseed in the Qazvin Plain irrigation network is estimated at 4000 kg/ha, none of the farms have reached the maximum leaf area index, and considering the direct effect of the leaf area index in the flowering stage on the crop yield, the maximum yield potential in the selected farms is not available. Therefore, the leaf area index in the flowering stage is considered a suitable criterion for estimating the yield reduction of rapeseed. The results of Pearson's correlation coefficient analysis showed that crop yield had a significant correlation with leaf area index in development and middle stages of rapeseed growth, and the highest correlation was related to the middle stage of crop growth. The results of Pearson's correlation coefficient analysis showed that there was a significant correlation at the 1% probability level between the field recorded data and yield estimation values. Also, the values of explanation coefficient (R2), root mean square error (RMSE), mean bias error (MBE), mean absolute error (MAE) were equal to 0.91, 444.06, 41.23, 433.03 kg/ha respectively. Is. Also, the results of the correlation coefficient analysis of yield values and calculated evapotranspiration based on the SEBAL method showed that there was no significant correlation. Conclusions Several factors are effective in product performance, but modeling by simplifying the relationships related to a phenomenon, justifies the mutual relationships between independent and dependent variables by spending the least amount of time and money. The results of the research regarding yield estimation using vegetation indices, evapotranspiration algorithms and hybrid models show that it is possible to make an acceptable estimate of crop yield by using Remote Sensing techniques. For example, the results of present research showed that by preparing the selected image of Landsat 8 satellite (OLI and TIRS) related to the beginning of rapeseed flowering period in the following years and extracting the leaf area index in the middle period of growth, the yield of field can be predicted with reasonable accuracy. Also, the leaf area index in the rapeseed flowering stage is a suitable measure to estimate the yield gap of the rapeseed crop. The important point is that the accuracy of predicting crop performance by satellite images is still reported was average. The accuracy of field measurements, the low spatial resolution of satellite images, as well as the presence of clouds, fog, gas, and suspended particles, along with the complexities related to plant growth modeling, have an effect on reducing the accuracy of yield prediction and the validity of models. Although these researches are expected to improve and expand with the variety of satellite images and the entry of cloud computing into the field of complex computing. | ||
| کلیدواژه‌ها [English] | ||
| Bastianssen Model, Landsat 8, Satellite Images, Vegetation Indices | ||
| مراجع | ||
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Alizadeh, A. (2014). Soil, Water, Plant Relationship. Mashhad, Iran: Sajjad University of Technology. (in Persian) 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., Tasumi, M. & Trezza, R. (2002). SEBAL (Surface Energy Balance Algorithms for Land) Advanced Training and User’s Manual. Idaho Implementation, Version 1.0. Awad, M. M. (2019). An Innovative Intelligent System Based on Remote Sensing and Mathematical Models for Improving Crop Yield Estimation. Information Processing in Agriculture, 6(3), 316-325. Bastianssen, W. G. M., & Ali, S. (2003). A New Crop Yield Forecasting Model Based on Satellite Measurements Applied Across the Indus Basin, Pakistan. Agriculture, Ecosystems and Environment; (94), 321-340. Boegh, E., Soegaard, H., Broge, N., Hasager, C.B., Jensen, N.O., Schelde, K., and Thomsen, A. (2002). Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment; 81: 179-193. Field, C. B., Randerson, J. T., & Malmström, C. M. (1995). Global Net Primary Production: Combining Ecology and Remote Sensing. Remote Sensing Environment, 51, 74-88. Food and Agriculture Organization Statistical Data (FAOSTAT). 2021. FAO Statistical Data. (Available at: http://faostat3.fao.org/faostat-gateway/go/to/home/E) Liaqat, M. U., Cheema, M. J. M., Huang, W., Mahmood, T., Zaman, M. & Khan, M. M., (2017). Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin. Computers and Electronics in Agriculture, (138), 39-47. (in Persian) Lobell, D. B., Asner, G. P., Ortiz-Monasterio, I., & Benning, T. L. (2003). Remote Sensing of Regional Crop Production in the Yaqui Valley, Mexico: Estimates and Uncertainties. Agriculture, Ecosystem and Environment, (94), 205-220. Loveimi, N., Akram, A., Bagheri, N., & Hajiahmad, A. (2019). Prediction of Canola Yield in Some of Growth Stages by Using Landsat Satellite, OLI Sensor. Iranian Journal of Biosystems Engineering, 50(1), 101-113. (in Persian) Marofi, S. Mousavi, R. & Nasiri Gheidari, O. (2017). Investigation of Spatial and Temporal Variation of Water Requirement of Ghazvin Desert, Using METRIC Algorithm and Landsat Images. Geographical Researches, 32(2), 80-92. (in Persian) Moayeri, M. (2019). Determination of water requirement and comprehensive irrigation management of canola farms. Karaj: Agricultural Engineering Research Institute. (in Persian) Monteith, J. L. (1972). Solar Radiation and Productivity in Tropical Ecosystems. Journal of Applied Ecology, 9(3), 747-766. Moran, M. S., Maas, S. J., & Pinter, P. J. (1995). Combining Remote Sensing and Modeling for Estimating Surface Evaporation and Biomass Production. Remote Sensing Reviews, 12(3-4), 335-353. Panek, E. & Gozdowski, D., (2021). Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries. Agronomy, 11(2), 340. Panek, E., Gozdowski, D., Stępień, M., Samborski, S., Ruciński, D. & Buszke, B., (2020). Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale. Agronomy, 10(11), 1842. Pratt, S. (2013). Satellite crop estimate too low: Analysts. The Western Producer. Retrieved March 28, 2018, from https://www.producer.com/2013/10/satellite- crop-estimate-too-low-analysts. Rembold, F., Atzberger, C., Savin, I., & Rojas, O., (2013). Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sensing, 5(4), 1704-1733. Rouse, J., Haas, R., Schell, J., and Deering, D. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium NASA; 309-317. Roznik, M., Boyd, M., & Porth, L. (2022). Improving crop yield estimation by applying higher resolution satellite NDVI imagery and high-resolution cropland masks. Remote Sensing Applications: Society and Environment 25, (100693). Uossef Gomrokchi, A. (2021). Estimation of Potential Yield and Yield Gap of major crops in Qazvin irrigation network. Water Resources Engineering, 14(50), 75-88. (in Persian) Valashjerdi, M., Hamzeh, S., Moghadasi, M. & Shini Dashtgol. (2019). Modeling the sugarcane crop yield by using a composite model based on remote sensing data. Journal of Water and Soil Conservation, 25(6), 141-158. (in Persian) Wahap, N., Shafri, H.Z. (2020). Utilization of Google Earth Engine (GEE) for land cover monitoring over Klang Valley, Malaysia. In: IOP Conference Series: Earth and Environmental Science, vol 1. IOP Publishing, pp 012003. Weber, V.S., Araus, J.L., Cairns, J.E., Sanchez, C., Melchinger, A.E. & Orsini, E. (2012). Prediction Younesi, M. (2022). Feasibility of Using Precision Irrigation Process in Improving Agricultural Water Productivity Index in Wheat Crop (M.Sc. Thesis), Tehran University, Iran. (in Persian) Younesi, M., Mashal, M. & Yousef Gomrokchi, A. (2022). Estimation of Real Evapotranspiration of Wheat and Rapeseed Using SEBAL Algorithm (Case Study: Esmaeil Abad Agricultural Research Station in Qazvin Province). Iranian Journal of ECOHYDROLOGY, 9(3), 475-487. (in Persian) Zahirnia, A.R. & Matinfar, H.R. (2016). Evaluate the yield of irrigated wheat fields on the basis of data obtained from Landsat 8 in the southwestern province of Khuzestan. First National Conference Zamani-Noor, N., Feistkorn, D. (2022). Monitoring Growth Status of Winter Oilseed Rape by NDVI and NDYI Derived from UAV-Based Red–Green–Blue Imagery. Agronomy, 12, 1-16. Zare khormizi, H., Tavili, A., & Ghafarian Malamiri, H. R. (2021). Estimation of Actual Evapotranspiration Using SEBAL Algorithm and Comparison with Improved FAO 56 Standard Evapotranspiration with KC-NDVI Relationship. Iranian Journal of Remote Sensing & GIS, 13(3), 73-92. (In Persian) Zhang, H., Chen, H., & Zhou, G. (2012). The model of wheat yield forecast based MODIS-NDVI-A case study of Xinxiang. International Society for Photogrammetry and Remote Sensing Conference, Melbourne, Australia. Zhu, W., Pan, H., He, H., Yu, D., & Hu, H. (2006). Simulation of Maximum Light Use Efficiency for some Typical Vegetation Types in China. Chinese Science Bulletin, 51(4), 457-463.
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