##plugins.themes.bootstrap3.article.main##

نعیم لویمی اسداله اکرم نیکروز باقری علی حاجی احمد

چکیده

سنجش از دور و به‌کارگیری تصاویر ماهواره‌ها به‌علت سرعت کار و گستردگی سطح پوشش بسیار مورد توجه قرار گرفته است. کلزا به‌دلیل گل‌های زرد آن دارای رنگ پوشش گیاهی متفاوتی با سایر محصولات است و تحقیقات کمی در زمینه ارزیابی شاخص‌های طیفی به‌منظور پیش‌بینی عملکرد آن انجام گردیده است. در سال زراعی 96-95 با هدف پیش‌بینی عملکرد کلزا ده شاخص طیفی سنجنده سنتینل-2، مورد ارزیابی قرار گرفت. این تحقیق به شکل پیکسل‌مبنا در سه مزرعه انجام شد و محدوده شبکه‌ای پیکسل‌های مزارع با کمک سیستم موقعیت‌یابی جهانی سینماتیک زمان واقعی (RTKGPS) تعیین گردید. در این تحقیق مدل‌های رگرسیونی خطی ساده و چند متغیره و نیز شبکه عصبی به‌کار رفت. نتایج نشان داد براساس مدل رگرسیون خطی ساده، بین مراحل مختلف رشد، بیشترین ضریب تبیین (R2) در هر یک از شاخص‌های گیاهی در یکی از دو مرحله اوج گل‌دهی و رسیدگی سبز رخ می‌دهد. براساس این مدل، در مرحله اوج گل‌دهی، شاخص تفاضل نرمال شده زردی (NDYI) با 73 درصد بیشترین ضریب تبیین را نسبت به سایر شاخص‌ها احراز کرد. با به‌کارگیری مدل رگرسیون خطی چند متغیره گام به گام با ورودی چهار باند، سه باند مرئی و باند مادون قرمز نزدیک، بهترین مدل در مرحله اوج گل‌دهی با ضریب تبیین 76 درصد و اعتبارسنجی 73 درصد با ریشه میانگین مربعات خطا (RMSE) به‌میزان 641/0 به‌دست آمد. همچنین با استفاده از مدل شبکه عصبی و ورود چهار باند مذکور نیز بهترین مدل در مرحله اوج گل‌دهی با ضریب تبیین 92 درصد (آموزش) و اعتبارسنجی (آزمون) 77 درصد با RMSE به‌میزان 612/0 احراز شد.

جزئیات مقاله

کلمات کلیدی

پیش‌بینی عملکرد, سنجش از دور, سنجنده سنتینل-2, شاخص گیاهی, شاخص NDYI

مراجع
1. Ahamed, T., L. Tian, Y. Zhang, and K. C. Ting. 2011. A review of remote sensing methods for biomass feedstock production. Biomass and Bioenergy 35: 2455-2469.
2. Alavipanah, S. K. 2016. Fundamentals of modern remote sensing and interpretation of Satellite images and aerial photos. University of Tehran. Tehran. (In Farsi).
3. Alizadeh Rabie, H. 2014. Remote Sensing (Principles and Application). Samt Press. Tehran.
4. Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal 92 (1): 83-91.
5. Ash, M. 2017. Canola Production and Processing. Available at: https://www.ers.usda.gov/topics/crops/soybeans-oil-crops/canola.aspx. Accessed 27 March 2018.
6. Basnyat, P., B. McConkey, G. P. Lafond, A. Moulin, and Y. Pelcat. 2004. Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies. Canadian Journal of Plant Science 84 (1): 97-103.
7. Birth, G. S., and G. R. McVey. 1968. Measuring color of growing turf with a reflectance spectrophotometer. Agronomy Journal 60: 640-649.
8. Boschetti, M., S. Bocchi, and P. A. Brivio. 2007. Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information. Agriculture, Ecosystems & Environment 118: 267-272.
9. Buschmann, C., and E. Nagel. 1993. In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing 14: 711-722.
10. Clevers, J. G. P. W., G. W. A. M. Van Der Heijden, S. Verzakov, and M. E. Schaepman. 2007. Estimating grassland biomass using SVM band shaving of hyperspectral data. Data Photogrammetric Engineering & Remote Sensing 73 (10): 1141-1148.
11. Cowley, R. B., D. J. Luckett, J. S. Moroni, and S. Diffey. 2014. Use of remote sensing to determine the relationship of early vigour to grain yield in canola (Brassica napus L.) germplasm. Crop & Pasture Science 65: 1288-1299.
12. Dominguez, J. A., J. Kumhalova, and P. Novak. 2017. Assessment of the relationship between spectral indices from satellite remote sensing and winter oilseed rape yield. Agronomy Research 15 (1): 055-068.
13. Gallego, J., E. Carfagna, and B. Baruth. 2010. Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. PP 193-211 in R. Benedetti., M. Bee., G. Espa and F. Piersimoni eds. Agricultural Survey Methods. John Wiley & Sons Inc., New York.
14. Gitelson, A. A., Y. J. Kaufman, R. Stark, and D. Rundquist. 2002. Novel algorithms for remote estimation of vegetative fraction. Remote Sensing of Environment 80: 76-87.
15. Goel, P. K., S. O. Prasher, J. A. Landry, R. M. Patel, A. A. Viau, and J. R. Miller. 2003. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Transactions of the ASAE 46 (4): 1235-1246.
16. Haboudane, D., J. R. Miller, E. Pattey, P. J. Zarco Tejada, and I. B. Strachan. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90: 337-352.
17. Huete, A. R., C. Justice, and W. Van Leeuwen. 1996. MODIS vegetation index (mod13). Algorithm theoretical basis document. Version 2. NASA Goddard Space Flight Center. Washington D. C.
18. Jago, R. A., M. E. J. Cutler, and P. J. Curran. 1999. Estimating canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment 68 (3): 217-224.
19. Jiang, Z., A. R. Huete, K. Didan, and T. Miura. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112: 3833-3845.
20. Jin, X., L. Kumar, Z. Li, X. Xu, G. Yang, and J. Wang. 2016. Estimation of winter wheat biomass and yield by combining the aquacrop model and field hyperspectral data. Remote Sensing 8. Available at: http://https://doi.org/10.3390/rs8120972. Accessed 4 March 2019.
21. Johnson, D. M. 2016. A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products. International Journal of Applied Earth Observation and Geo information 52: 65-81.
22. Johnson, L., F. Roczen, D. Youkhana, R. Nemani, and D. Bosch. 2003. Mapping vineyard leaf area with multispectral satellite imagery. Computers and Electronics in Agriculture 38: 33-44.
23. Kaab, A., M. Sharifi, H. Mobli, A. Nabavi-Pelesaraei, and K. Chau. 2019. Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production. Science of the Total Environment 664: 1005-1019.
24. Kazem, M., S. Mirzaei, and B. Maadi. 2016. Canola cultivation. Taak Press. Tehran. (In Farsi).
25. Khalili, S. M., A. Rezaee, and A. Haji Ahmad. 2016. Ripe detection and estimation of rapeseed crop yield based on remote sensing image processing. Thesis of Master of Science. University of Tehran. (In Farsi).
26. Languille, F., A. Gaudel1, B. Vidal, R. Binet, V. Poulain, and T. Trémas, 2017. Sentinel-2B Image Quality commissioning phase results and Sentinel2 constellation performances. Conference on Sensors, Systems, and Next-Generation Satellites XXI Location: Warsaw, POLAND Date: SEP 11-14, 2017.
27. Lee, K. S., W. B. Cohen, R. E. Kennedy, T. K. Maiersperger, and S. T. Gower. 2004. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment 91: 508-520.
28. Li, A., S. Liang, A. Wang, and J. Qin. 2007. Estimating Crop Yield from Multi-temporal Satellite Data Using Multivariate Regression and Neural Network Techniques. Photogrammetric Engineering & Remote Sensing 73 (10): 1149-1157.
29. Li, F., M. L. Gnyp, L. Jia, Y. Miaoa, Z. Yua, W. Koppe, G. Bareth, X. Chen, and F. Zhang. 2008. Estimating N status of winter wheat using a handheld spectrometer in the North China Plain. Field Crops Research 106 (1): 77-85.
30. Loveimi, N., A. Akram, N. Bagheri, and A. Haji Ahmad. 2019. Prediction of canola yield in some of growth stages by using Landsat satellite, OLI sensor. Journal of Iran Biosystems Engineering 50 (1): 101-113. (In Farsi).
31. Mkhabela, M. S., P. Bullock, S. Raj, S. Wang, and Y. Yang. 2011. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology 151: 385-393.
32. Mcbratney, A., B. Whelan, T. Ancev, and J. Bouma. 2005. Future Directions of Precision Agriculture. Journal of Precision Agriculture 6 (1): 7-23.
33. Norgholipor, F., H. Rezaei, K. Mirzashahi, and H. Haghighatnia. 2014. Integrative management instruction of soil fertility and canola feeding. Soil and Water Research Institute. Tehran. (In Farsi).
34. Panda, S. S., D. P. Ames, and S. Panigrahi. 2010. Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques. Remote Sensing 2: 673-696.
35. Piekarczyk, J. 2011. Winter oilseed-rape yield estimates from hyperspectral radiometer measurements. Journal of Quaestiones Geographicae 30 (1): 77-84.
36. Pratt, S. 2013. Satellite crop estimate too low: Analysts. The Western Producer. Available at: https://www.producer.com/2013/10/satellite-crop-estimate-too-low-analysts. Accessed 28 March 2018.
37. Raun, W. R., J. B. Solie, M. L. Stone, E. V. Lukina, W. E. Thomason, and J. S. Schepers. 2001. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal 93: 131-138.
38. Rezaei, A., and S. A. Mirmohammadi. 2011. Statistics and probability, application in agriculture. Jahad Daneshgahi Sanati Esfahan Press. Tehran. (In Farsi).
39. Rischbeck, R., S. Elsayed, B. Mistele, G. Barmeier, K. Heil, and U. Schmidhalter. 2016. Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. European Journal of Agronomy 78: 44-59.
40. Roodi, D., S. Rahmanpoor, and F. Javidfar. 2004. Cultivation of canola. Seed Breeding Research Institute. Tehran. (In Farsi).
41. Rostami, M. A., and H. Afzali Gorouh. 2017. Remote sensing of residue management in farms using Landsat 8 sensor imagery. Journal of Agricultural Machinery 7 (2): 388-400. (In Farsi).
42. Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, NASA SP-351 I: 309-317. Washington D. C. USA.
43. Sanaeinejad, H., M. Nassiri Mahallati, H. Zare, N. Salehnia, and M. Ghaemi. 2014. Wheat yield estimation using Landsat images and observation. Journal of Plant Production 20 (4): 45-63. (In Farsi).
44. Schwalbert, R. A., T. J. C. Amado, L. Nieto, S. Varela, G. M. Corassa, T. A. N. Horbe, C. W. Rice, N. R. Peralta, and I. A. Ciampitti. 2018. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystems Engineering 171: 179-192.
45. Shanahan, J. F., J. S. Schepers, D. D. Francis, G. E. Varvel, W. W. Wilhelm, J. M. Tringe, M. R. Schlemmer, and D. J. Major. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal 93: 583-589.
46. Sicre, C. M., J. Inglada, R. Fieuzal, F. Baup, S. Valero, J. Cros, M. Huc, and V. Demarez. 2016. Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series. Remote Sensing 8 (7): 591. Available at: http://doi:10.3390/rs8070591. Accessed 3 March 2019.
47. Sulik, J. J., and D. S. Long. 2016. Spectral considerations for modeling yield of canola. Remote Sensing of Environment 184: 161-174.
48. Vigneau, N., M. Ecarnot, G. Rabatel, and P. Roumet. 2011. Potential of field hyperspectral imaging as a nondestructive method to assess leaf nitrogen content in wheat. Field Crops Research 122: 25-31.
49. Weber, V. S., J. L. Araus, J. E. Cairns, C. Sanchez, A. E. Melchinger, and E. Orsini. 2012. Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Research 128: 82-90.
50. Yamamoto, K., W. Guo, Y. Yoshioka, and S. Ninomiya. 2014. On plant detection of intact tomato fruits using image analysis and machine learning methods. Journal of Sensors (Basel) 14 (7): 12191-12206.
51. Zahirnia, A., H. R. Matinfar, and M. Zinvand. 2016. Prediction of canola yield base on Landsat-8 in west south of Khouzestan province. 4th International conference on applied research in agricultural sciences. Tehran. (In Farsi).
52. Zhang, X., and He, Y. 2013. Rapid estimation of seed yield using hyperspectral images of oilseed rape leaves. Journal of Industrial Crops and Products 42: 416-420.
53. Zou, X. B., J. Y. Shi, L. M. Hao, J. W. Zhao, H. P. Mao, Z. W. Chen, Y. X. Li, and M. Holmes. 2011. In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Journal of Analytica Chimica Acta 706: 105-112.
ارجاع به مقاله
لویمین., اکرما., باقرین., & حاجی احمدع. (2019). ارزیابی چند شاخص طیفی برای برآورد عملکرد کلزا با استفاده از تصاویر سنجنده سنتینل-2. ماشین‌های کشاورزی, 11(2), 447-464. https://doi.org/10.22067/jam.v11i2.80232
نوع مقاله
مقاله علمی- پژوهشی