پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,963,308 |
| تعداد دریافت فایل اصل مقاله | 89,636,336 |
برآورد عملکرد برنج و تعیین بهره‌وری آب اراضی شالیزاری با استفاده از سنجش از دور و داده‌های لایسیمتر (مورد مطالعه: شمال شهرستان ساری) | ||
| تحقیقات آب و خاک ایران | ||
| دوره 52، شماره 10، دی 1400، صفحه 2555-2567 اصل مقاله (1.67 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2021.326556.669012 | ||
| نویسندگان | ||
| فاطمه جعفری صیادی1؛ محمد علی غلامی سفیدکوهی* 2؛ همت اله پیردشتی3؛ مجتبی خوش روش2 | ||
| 1گروه مهندسی آب- دانشکده مهندسی زراعی- دانشگاه علوم کشاورزی و منابع طبیعی ساری- استان مازندران- ایران | ||
| 2گروه مهندسی آب، دانشکده مهندسی زارعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران. | ||
| 3عضو هیات علمی گروه زراعت دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| چکیده | ||
| با توجه به نقش کلیدی محصول برنج در تأمین امینت غذایی و اشتغال‌زایی در کشور، دستیابی به اطلاعات به‌هنگام عملکرد و بهره‌وری زمین‌های شالیزاری می‌تواند راهبردهای مهمی را به‌منظور برنامه‌ریزی فعالیت‌هایی مانند برداشت، ذخیرهسازی، بازاریابی و مدیریت منابع و نهاده‌ها فراهم نماید. هدف پژوهش حاضر، برآورد عملکرد و تعیین بهره‌وری آب شالیزارهای شمال شهرستان ساری با استفاده از داده‌های ماهواره لندست 8 و لایسمتر نوع N است. بهاین منظور، پس از انجام تصحیح‌های اتمسفریک و رادیومتریک تصاویر ماهواره‌ای در دوره رشد برنج، شاخص‌های گیاهی NDVI، SAVI و RGVI به دست آمد. با استفاده از این شاخص‌ها رابطه رگرسیونی مناسب با عملکرد برنج ایجاد شد. همچنین، با پایش مداوم شالیزارها و نصب لایسیمتر نوع N داده های مربوط به آب مصرفی و تبخیر- تعرق برنج اندازه‌گیری شد. در نهایت، نقشه بهره‌وری آب برنج در منطقه مورد مطالعه با تلفیق داده‌های سنجش از دور (عملکرد) و مزرعه‌ای (آب مصرفی و تبخیر-تعرق) بهدست آمد. نتایج نشان داد، شاخص‌های گیاهی در مرحله پنجه‌زنی بیشترین همبستگی را با میزان عملکرد گیاهی برنج دارند و در صورتی‌که، برآورد عملکرد با استفاده از داده‌های سنجش از دور مدنظر باشد، شاخص‌های گیاهی در مرحله پنجه‌زنی باید مورد استفاده قرار گیرد. در میان شاخص‌های گیاهی، شاخص SAVI بهترین همبستگی (94/0=r) را با عملکرد داشته و نقشه عملکرد حاصل از این شاخص گیاهی برای تهیه نقشه بهره‌وری آب بر مبنای آب مصرفی شالیزار و تبخیر- تعرق گیاه برنج مورد استفاده قرار گرفت. میانگین بهره‌وری با استفاده از شاخص SAVI، 63/0 کیلوگرم بر مترمکعب و میانگین بهره‌وری اندازه‌گیری‌شده 68/0 کیلوگرم بر مترمکعب بود. یافته‌ها نشان می‌دهد سنجش از دور حاوی اطلاعات مفیدی برای تهیه نقشه عملکرد گیاهی و بهره‌وری آب در اراضی شالیزاری بوده و از پتانسیل خوبی برای استفاده درکشاورزی دقیق و هوشمند برخوردار است. | ||
| کلیدواژه‌ها | ||
| آب مصرفی برنج؛ لندست 8؛ شاخص‌های گیاهی | ||
| عنوان مقاله [English] | ||
| Estimating the Rice Yield and Determining Water Productivity of Paddy Fields with Remote Sensing and Lysimeter Data (The Studied Case: North of Sari) | ||
| نویسندگان [English] | ||
| Fatemeh Jafari Sayadi1؛ Mohammad Ali Gholami Sefidkouhi2؛ Hemmatollah Pirdashti3؛ Mojtaba Khoshravesh2 | ||
| 1Department of irrigation. Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University (SANRU), Mazandaran. Iran | ||
| 2Department of Water Engineering, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran. | ||
| 3Scientific staff, sari agricultural sciences and natural resources University | ||
| چکیده [English] | ||
| Due to the key role of rice crops in food security and employment in Iran, access to on-time information of productivity and water productivity in paddy fields can provide important strategies for planning activities such as harvesting, storage, marketing, and management of resources and inputs. This study aimed to estimate the yield and determine water productivity of paddy fields in the north of Sari city using Landsat 8 satellite data and N type lysimeter. For this purpose, NDVI, SAVI, and RGVI indices were extracted from the images. Using these indices, a suitable regression relationship was created with rice yield. With continuous monitoring of paddy fields and installation of type N lysimeter, water consumption and evapotranspiration of rice data were measured. Finally, the study area's rice water productivity map was obtained by incorporating remote sensing data (yield) and field data (water consumption and evapotranspiration). The results showed that plant indices in the tillering stage have the highest correlation with rice crop yield, and if yield estimation using remote sensing data is considered, plant indices in tillering stage should be used. Among the plant indices, the SAVI index had the best correlation (r=0.94) with yield, and the yield map obtained from this plant index was used to prepare a water productivity map based on water consumption and rice evapotranspiration. Evapotranspiration-based water productivity map provided more realistic data than water consumption-based productivity map, so the SAVI index average productivity was 0.63 kg/m3, and the average measured productivity was 0.68 kg/m3. Findings showed that remote sensing provides useful information for mapping crop yield and water productivity in paddy fields and has good potential for precision and smart agriculture. | ||
| کلیدواژه‌ها [English] | ||
| Rice water consumption, Landsat 8, Vegetation indices | ||
| مراجع | ||
|
Badiehneshin, A., Noory, H. and Vazifedoust, M. (2015). Improving Crop Yield Estimation through SWAP Model Using Satellite Data. Iranian Journal of Soil and Water Research. 45 (4), 379-388. 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 Geo-information, 10, 438–452. Bastiaanssen, W. G. and Steduto, P. (2016). The water productivity score (WPS) at global and regional level: Methodology and first results from remote sensing measurements of wheat, rice and maize. Science of the Total Environment. 575, 1-17. http://dx.doi.org/10.1016/j.scitotenv.2016.09.032. Bastiaanssen, W.G.M. and Ali, S. (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture Ecosystems & Environment, 94, 321–340. Farahza, M. N., Nazari, B., Akbari, M. R., Naeini, M. S. and Liaghat, A. (2020). Assessing the physical and economic water productivity of annual crops in Moghan Plain and analyzing the relationship between physical and economic water productivity. Journal of Irrigation and Water Engineering, 11 (42), 166-179. (In Farsi) Ferencz, C., Bognar, P., Lichtenberger, J., Hamar, D., Tarscai, G., et al. (2004) Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25. 4113–4149. Foster, T. Brozovic, N., Butler, A. P., Neale, C. M. U. Raes, D., Steduto, P. and Hsiao, T. C. (2017). AquaCrop- OS: An open source version of FAO's crop water productivity model. Agricultural Water Management, 181, 18-22. Funk, C. and Budde, M.E. (2009). Phonologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sensing of Environment, 113, 115–125. Gemechu, M.G., Huluka, T.A., Steenbergen, F.V., Wakjira, Y.C., Chevalking, S. and Bastiaanssen, S.W. (2020). Analysis of spatial-temporal variability of water productivity in Ethiopian sugar estates: using open access remote sensing source. Annals of GIS. https://doi.org/10.1080/19475683.2020.1812716. Huang, J.F., Wang, F.M. and Wang, X.Z. (2010). Hyper-spectral experiment for paddy rice remote sensing; Huang JQ, Chen JY, editors. Hangzhou: Zhejiang University Press. 315 p. (in Chinese with English abstract). Huete, A.R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309. Jalali Koutenaei, N. 2009. Basic and criteria survey, design and execution of land consolidation in the paddy fields. Haraz Extension and Technology Development Center. 212pp. Kamali, L., Kaviani, A., Nazari, B. and Liaghat, A. M. (2018). Wheat yield estimate by satellite imageris Landsat 8 (Case study: Moghan Plain). Iranian Journal of Soil and Water Research. 49 (5), 1031-1042. Karthikeyan, L., Chawla, I. and Mishra, A.K. (2020). A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses. Journal of Hydrology, 586, 1-22. Kastens, J.H., Kastens, T.L., Kastens, D.L.A., Price, K.P., Martinko, E.A., et al. (2005). Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sensing of Environment, 99, 341–356. Loveimi, N., Akram, N., Bagheri, N. and Hajiahmad, A. (2021). Evaluation of several spectral indices for estimation of Canola yield using Sentinel- 2 sensor image. Journal of Agricultural Machinery, 11 (2), 447- 464. (In Farsi) Mahmoud Soltani, S. and Abbasian, A. (2021). Simultaneous appliacation of rice husk biochare and zinc sulfate fertilizer on yield, yield components of rice (Oryza sativa L.) Hashemi cultivar and some soil chemical properties. Iranian Journal of Soil and Water Research. 52 (3), 707-719. Manjunath, K.R., Potdar, M.B. and Purohit, N.L. (2002). Large area operational wheat yield model development and validation based on spectral and meteorological data. International Journal of Remote Sensing, 23, 3023–3038. Maselli, F. and Rembold, F. (2001). Analysis of GAC NDVI data for cropland identification and yield forecasting in Mediterranean African countries. Photogrammetric Engineering and Remote Sensing, 67, 593–602. Maselli, F., Romanelli, S., Bottai, L. and Maracchi, G. (2000). Processing of GAC NDVI data for yield forecasting in the Sahelian region. International Journal of Remote Sensing, 21, 3509–3523. Mcbratney, A., Whelan, B., Ancev, T. and Bouma, J. (2005). Future directions of precision agriculture. Journal of Precision Agriculture, 6 (1), 7-23. Meier, U. 2001. Growth stages of mono-and dicotyledonous plants, BBCH Monograph. Federal Biological Research Center for Agriculture and Forestry. Mkhabela, M.S., Bullock, P., Raj, S., Wang, S. and Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151, 385–393. Mkhabela, M.S. and Mashinini, N.N. 2005. Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAAs- AVHRR. Agricultural and Forest Meteorology, 129, 1–9. Mika, J., Kerenyi, J., Rimoczi-Paal, A., Merza, A., Szinell, C., et al. (2002). On correlation of maize and wheat yield with NDVI: Example of Hungary (1985-1998) In: Fellous JL, LeMarshall JF, Choudhury BJ, Menenti M, Paxton LJ et al., editors. Earth’s Atmosphere, Ocean and Surface Studies, 2399–2404. Mo, X., Liu, S., Lin, Z., Xu, Y., Xiang, Y. and McVicar, T.R. (2005). Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling, 183, 301-322. Mosleh M.K., Hassan Q.K. and Chowdhury E.H. (2015). Application of Remote Sensing in Mapping Rice Area and Forecasting Its Production. Sensors, 15, 769-791. Nazari, B., Liaghat, A., Akbari, M. R. and Keshavarz, M. (2018). Irrigation water management in Iran: Implications for water use efficiency improvement. Agricultural Water Management, 208, 7-18. Noureldin, N.A., Aboelghar, M.A., Saudy, H.S. and Ali, A.M. (2013). Rice yield forecasting models using satellite imagery in Egypt. The Egyptian Journal of Remote Sensing and Space Sciences, 16, 125-131. Nuarsa, I.W., Nishio, F. and Hongo, C. (2012). Rice yield estimation using Landsat ETM+ data and field observation. Journal of Agricultural Science, 4 (3), 45- 56. Prasad, A.K., Singh, R.P., Tare, V. and Kafatos, M. (2007). Use of vegetation index and meteorological parameters for the prediction of crop yield in India. International Journal of Remote Sensing, 28, 5207–5235. Ren, J.Q., Chen, Z.X., Zhou, Q.B. and Tang, H.J. (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geo-information, 10, 403–413. Sanaeinejad, H., Nassiri Mahallati, M., Zare, H., Salehnia, N. and Ghaemi, M. (2014). Wheat yield estimation using Landsat images and field observation: A case study in Mashhad. Journal of Plant Production, 20 (4), 45- 63. (In Farsi) Tian, F., Zhang, Y. and Saihong, L. (2020). Spatial-temporal dynamics of cropland ecosystem water-use efficiency and the responses to agricultural water management in the Shiyang River Basin, northwestern China. Agricultural Water Management, 237, 1-12. Virnodkar, S.S., Pachghare, V.K., Paril, V.C. and Jha, S.K. (2020). Remote sensing and machine learning for crop water stress determination in various crops: a critical review. Precision agriculture. https://doi.org/10.1007/s11119-020-09711-9. Wannebo, A. and Rosenzweig, C. (2003). Remote sensing of US cornbelt areas sensitive to the El Ni@o-Southern Oscillation. International Journal of Remote Sensing, 24 (10), 2055-2067. Wang, R.C, Huang, J.F. (2002). Rice yield estimation using remote sensing data. Beijing. China Agriculture Press, 287 p. (in Chinese with English abstract). Weissteiner, C. and Ku¨hbauch, W. (2005). Regional Yield Forecasts of Malting Barley (Hordeum vulgare L.) by NOAA-AVHRR Remote Sensing Data and Ancillary Data. Journal of Agronomy and Crop Science, 191, 308–320. Wendroth, O., Reuter, H.I. and Kersebaum, K.C. (2003). Predicting yield of barley across a landscape: a state-space modeling approach. Journal of Hydrology, 272, 250–263. Zandsalimi, Z., Sima, S. and Mousivand, A.J. (2021). Evaluating the Performance of Global Land Cover Maps in Agricultural Land Delineation (Case Study: Lake Urmia Basin). Iranian Journal of Soil and Water Research. 52 (3), 795-810. Zhang, F., Wu, B.F. and Luo, Z.M. (2004). Winter wheat yield predicting for America using remote sensing data. Journal of Remote Sensing, 8, 611–617. (In Chinese with English abstract).
| ||
|
آمار تعداد مشاهده مقاله: 398 تعداد دریافت فایل اصل مقاله: 295 |
||