یک مدل فیزیکی مقیاس پذیر مبتنی بر سنجش از دور در برآورد عملکرد محصول مزارع برنج
الموضوعات :احسان آسمار 1 , محمد حسن وحید نیا 2 , مجتبی رضایی 3 , ابراهیم امیری 4
1 - دانشجوی دکتری گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
2 - استادیار گروه سنجش از دور و GIS، دانشکده منابع طبیعی و محیط زیست، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران. *(مسوول مکاتبات)
3 - استادیار موسسه تحقیقات برنج کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، رشت، ایران.
4 - استاد گروه مهندسی آب، واحد لاهیجان، دانشگاه آزاد اسلامی، لاهیجان، ایران.
الکلمات المفتاحية: مقیاس پذیر, مدل عملکرد برنج, توسعه پایدار کشاورزی, سنجش از دور, گوگل ارث انجین (GEE),
ملخص المقالة :
زمینه و هدف: گیاه برنج یکی از مهمترین محصولات استراتژیکی در کشور ایران محسوب می‌شود. از دیگر سو، کشاورزی طیف گسترده ای از امکانات و در عین حال مشکلات زیست محیطی را ایجاد می‌کند. درنتیجه، تحقیقاتی که به تولید و توسعه پایدار در این حیطه کمک کنند، حائز اهمیت هستند. هدف اصلی از این پژوهش طراحی و توسعه مدل مقیاس پذیر عملکرد برنج مبتنی بر سنجش از دور و پردازش داده های ماهواره ای است.روش بررسی: در این مطالعه، از چندین تصویر مختلف، موجود در سامانه گوگل ارث انجین (Google Earth Engine) برای تخمین عملکرد برنج در مقیاس های مختلف زمانی (فصول رشد مختلف) و مکانی (وضوح 30 متر تا مقیاس های منطقه ای) استفاده شد. سپس یک مدل کارایی استفاده از نور (LUE) مبتنی بر سنجش از دور را اجرا نموده و در آن تنش‌های محیطی غیرزنده را وارد نمودیم. این مدل فیزیکی در برابر داده‌های عملکرد اندازه‌گیری شده زمینی، در سال‌های زراعی 1395، 1396 و 1398 در 691 مزرعه برنج در استان گیلان ارزیابی شد.یافته ها: نتایج، همبستگی مثبت و تطابق قابل توجهی بین مقادیر محاسباتی و مشاهداتی نشان داد، بطوریکه در سال‌های زراعی مورد مطالعه، میانگین ضریب همبستگی (R) و شاخص توافق (d) برابر با55/0 بدست آمد. میانگین RMSE برابر با 500 کیلوگرم در هکتار، میانگین MAE برابر با 440 کیلوگرم در هکتار، و میانگین NRMSE برابر با 0.12، حاکی از دقت مناسب مدل در برآورد عملکرد محصول در این مکان‌ها و سال‌هاست. مدل ارائه شده، تغییرپذیری مناسب مقادیر عملکرد را در مقیاس مزرعه نشان داد.بحث و نتیجه گیری: بهره‌گیری از سنجش از دور در محیط GEE به عنوان ابزاری مفید جهت تخمین عملکرد محصول در مقیاس‌های مختلف زمانی و مکانی، مورد تایید قرار گرفت. مدل حاضر می‌تواند در طیف وسیعی از کاربردها مانند مدیریت کشاورزی و بیمه محصولات کشاورزی مورد استفاده قرار گیرد.
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Clevers JG. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote sensing of Environment. 1997 Aug 1;61(2):221-8.
Lobell DB, Ortiz‐Monasterio JI, Asner GP, Naylor RL, Falcon WP. Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal. 2005 Jan;97(1):241-9.
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Báez‐González AD, Chen PY, Tiscareño‐López M, Srinivasan R. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop science. 2002 Nov;42(6):1943-9.
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Lobell DB. The use of satellite data for crop yield gap analysis. Field Crops Research. 2013 Mar 1;143:56-64.
Khaki, S.;Wang, L. Crop yield prediction using deep neural networks. Front. Plant Sci. 2019, 10, 621.
Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf. 2015, 34, 235–248.
Scharf, P.C.; Lory, J.A. Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agron. J. 2002, 94, 397–404.
Bastiaanssen,W.G.; Molden, D.J.; Makin, I.W. Remote sensing for irrigated agriculture: Examples from research and possible applications. Agric. Water Manag. 2000, 46, 137–155.
Mahlein, A.K.; Oerke, E.-C.; Steiner, U.; Dehne, H.W. Recent advances in sensing plant diseases for precision crop protection. Eur. J. Plant Pathol. 2012, 133, 197–209.
Lobell DB, Thau D, Seifert C, Engle E, Little B. A scalable satellite-based crop yield mapper. Remote Sensing of Environment. 2015 Jul 1;164:324-33.
Lichtenberg E. Agriculture and the environment. Handbook of agricultural economics. 2002 Jan 1;2:1249-313.
Khan, A.; Stöckle, C.O.; Nelson, R.L.; Peters, T.; Adam, J.C.; Lamb, B.; Chi, J.; Waldo, S. Estimating Biomass and Yield Using METRIC Evapotranspiration and Simple Growth Algorithms. Agron. J. 2019, 111, 536–544.
Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Rötter, R.P.; Cammarano, D.; et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Chang. 2013, 3, 827.
Monteith, J. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9, 747–766.
Prince SD. A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing. 1991 Jun 1;12(6):1313-30.
Goetz SJ, Prince SD. Modelling terrestrial carbon exchange and storage: evidence and implications of functional convergence in light-use efficiency. InAdvances in ecological research 1999 Jan 1 (Vol. 28, pp. 57-92). Academic Press.
Heinsch FA, Reeves M, Votava P, Kang S, Milesi C, Zhao M, Glassy J, Jolly WM, Loehman R, Bowker CF, Kimball JS. Gpp and npp (mod17a2/a3) products nasa modis land algorithm. MOD17 User's Guide. 2003:1-57.
Turner DP, Urbanski S, Bremer D, Wofsy SC, Meyers T, Gower ST, Gregory M. A cross‐biome comparison of daily light use efficiency for gross primary production. Global Change Biology. 2003 Mar;9(3):383-95.
Jones, C.A. CERES-Maize: A Simulation Model of Maize Growth and Development; Texas A&M University Press: College Station, TX, USA, 1986.
Williams, J.R.; Jones, C.A.; Dyke, P.T. The EPIC model and its application. In Proceedings of the International Symposium on Minimum Data Sets for Agrotechnology Transfer, Patancheru, India, 21–26 March 1983; pp. 111–121.
Jones, J.W.; Hoogenboom, G.; Porter, C.H.; Boote, K.J.; Batchelor, W.D.; Hunt, L.;Wilkens, P.W.; Singh, U.; Gijsman, A.J.; Ritchie, J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18, 235–265.
Daughtry, C.; Gallo, K.; Goward, S.; Prince, S.; Kustas, W. Spectral estimates of absorbed radiation and phytomass production in corn and soybean canopies. Remote Sens. Environ. 1992, 39, 141–152.
Kumar, M. Remote Sensing of Crop Growth. In Plants and the Daylight Spectrum: Proceedings of the First International Symposium of the British Photobiology Society, Leicester, UK, 5–8 January 1981; Smith, H., Ed.; Academic Press: Cambridge, MA, USA, 1981; Volume 1, pp. 133–144.
Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, Hansen M. Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project. Remote Sensing. 2010 Jun 18;2(6):1589-609.
MacDonald RB, Hall FG. Global crop forecasting. Science. 1980 May 16;208(4445):670-9.
Boschetti, M.; Stroppiana, D.; Confalonieri, R.; Brivio, P.A.; Crema, A.; Bocchi, S. Estimation of rice production at regional scale with a Light Use Efficiency model and MODIS time series. Ital. J. Remote Sens. Riv. Ital. Di Telerilevamento 2011, 43, 63–81.
Bastiaanssen, W.G.; Ali, S. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ. 2003, 94, 321–340.
Clevers JG. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote sensing of Environment. 1997 Aug 1;61(2):221-8.
Lobell DB, Ortiz‐Monasterio JI, Asner GP, Naylor RL, Falcon WP. Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape. Agronomy Journal. 2005 Jan;97(1):241-9.
Moulin S, Bondeau A, Delecolle R. Combining agricultural crop models and satellite observations: from field to regional scales. International Journal of Remote Sensing. 1998 Jan 1;19(6):1021-36.
Báez‐González AD, Chen PY, Tiscareño‐López M, Srinivasan R. Using satellite and field data with crop growth modeling to monitor and estimate corn yield in Mexico. Crop science. 2002 Nov;42(6):1943-9.
Shanahan JF, Schepers JS, Francis DD, Varvel GE, Wilhelm WW, Tringe JM, Schlemmer MR, Major DJ. Use of remote‐sensing imagery to estimate corn grain yield. Agronomy Journal. 2001 May;93(3):583-9.
Gallego J, Carfagna E, Baruth B. Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. Agricultural survey methods. 2010 Apr 16:193-211.
Field, C.B.; Randerson, J.T.; Malmström, C.M. Global net primary production: Combining ecology and remote sensing. Remote Sens. Environ. 1995, 51, 74–88.
De Oliveira Ferreira Silva, C.; Lilla Manzione, R.; Albuquerque Filho, J.L. Large-Scale Spatial Modeling of Crop Coefficient and Biomass Production in Agroecosystems in Southeast Brazil. Horticulturae 2018, 4, 44.
Casanova, D.; Epema, G.; Goudriaan, J. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crop. Res. 1998, 55, 83–92.
Christensen, S.; Goudriaan, J. Deriving light interception and biomass from spectral reflectance ratio. Remote Sens. Environ. 1993, 43, 87–95.
Garcia, R.; Kanemasu, E.T.; Blad, B.L.; Bauer, A.; Hatfield, J.L.; Major, D.J.; Reginato, R.J.; Hubbard, K.G. Interception and use efficiency of light in winter wheat under different nitrogen regimes. Agric. For. Meteorol. 1988, 44, 175–186.
Rochette, P.; Desjardins, R.L.; Pattey, E.; Lessard, R. Crop net carbon dioxide exchange rate and radiation use efficiency in soybean. Agron. J. 1995, 87, 22–28.
Richards, R.; Townley-Smith, T. Variation in leaf area development and its effect on water use, yield and harvest index of droughted wheat. Aust. J. Agric. Res. 1987, 38, 983–992.
Varlet-Grancher, C.B.; Bonhomme, R.; Chartier, M.; Artis, P. Efficience de la conversion de l’énergie solaire par un couvert végétal. Acta Oecologica Oecologia Plantarum 1982, 3, 3–26.
Das, D.; Mishra, K.; Kalra, N. Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices. Int. J. Remote Sens. 1993, 14, 3081–3092.
Bueno CS, Lafarge T. Higher crop performance of rice hybrids than of elite inbreds in the tropics: 1. Hybrids accumulate more biomass during each phenological phase. Field Crops Research. 2009 Jun 26;112(2-3):229-37.
Calera, A.; González-Piqueras, J.; Melia, J. Monitoring barley and corn growth from remote sensing data at field scale. Int. J. Remote Sens. 2004, 25, 97–109.
Hatfield, J.; Asrar, G.; Kanemasu, E.T. Intercepted photosynthetically active radiation estimated by spectral reflectance. Remote Sens. Environ. 1984, 14, 65–75.
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