تخمین تولید محصول یونجه با استفاده از تصاویر ماهواره‌ای Sentinel-2 منطقه مورد مطالعه: شرکت کشاورزی و دام‌پروری مگسال (قزوین)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناس ارشد سنجش از دور، مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران

2 استادیار مرکز مطالعات سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه شهید بهشتی

3 دانشجوی دکتری سنجش از دور، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران

4 دانشجوی دکتری سنجش از دور، دانشکده نقشه برداری (ژئودزی و ژئوماتیک)، دانشگاه صنعتی خواجه نصیرالدین طوسی

چکیده

در طول چند دهه گذشته، شاخص‌های پوشش‌گیاهی متعددی برای تخمین تولید محصولات کشاورزی توسعه داده ‌شده‌اند که هر یک از آن‌ها با توجه به باندهای مورد استفاده و فرمول جبری خود، به مقادیر متفاوتی از تراکم و شاخص سطح برگ گیاهان زراعی حساسیت دارند. مطالعه بعضی از محصولات زراعی چندساله مانند یونجه، که در هر سال به دفعات برداشت می‌شود، بسیار پیچیده بوده و کمتر مورد توجه قرار گرفته است. لذا در این مقاله، از مهم‌ترین شاخص‌های پوشش‌گیاهی توسعه داده‌ شده در برآورد تولید یونجه، توسط تصاویر سری زمانی Sentinel-2 استفاده می‌شود. در این تحقیق، اقدام به جمع‌آوری دوره‌ای 144 نمونه، به شیوه تخریبی از مزارع زیر­کشت محصول یونجه شرکت کشاورزی و دامپروری مگسال (قزوین)، به‌صورت تقریباً نزدیک به زمان گذر ماهواره، شد و سپس کارایی 10 شاخص از معروف‌ترین شاخص‌های پوشش‌گیاهی، مبتنی بر تصاویر Sentinel-2 برای تخمین تولید محصول یونجه، مورد ارزیابی قرار گرفت. نتایج تحقیق حاضر، نشان داد که تولید تخمین زده‌شده یونجه، با استفاده از شاخص  نسبت به سایر شاخص‌ها، دارای بالاترین همبستگی  و کمترین جذر میانگین مربعات  با داده‌های برداشت‌شده میدانی در اواسط مرداد ماه بوده است. به­علاوه در نتایج این تحقیق، نشان داده شد که شاخص‌های لبه قرمز، مشکل اشباع‌شدگی شاخص‌های پوشش‌گیاهی در محصول یونجه را نتوانسته‌اند برطرف کنند و شاخص­های پوشش گیاهی سبز، نسبت به شاخص‌های لبه قرمز جهت تخمین تولید این محصول، توانایی بیشتری را نشان داده‌اند.

کلیدواژه‌ها


عنوان مقاله [English]

Alfalfa yield estimation using Sentinel-2 satellite images- a case study in Magsal Agricultural and Production Company (Qazvin)

نویسندگان [English]

  • Farzaneh hadadi 1
  • Mohsen m_azadbakht 2
  • Maedeh Behifar 3
  • Hamid Salehi Shahrabi 3
  • amir moeinirad 4
1 Remote sensing expert, Iranian Space Research Center
2 Assistant Professor in Remote Sensing, Remote Sensing and GIS Research Center
3 PhD student in Remote Sensing, Remote Sensing and GIS department, School of Geography, University of Tehran
4 PhD student in Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology
چکیده [English]

Over the past several decades, many vegetation indices have been developed for crop yield estimation, each being sensitive to different levels of crop density and leaf area index, based on the bands and the algebraic formulas used in its design. However, the study of some perennial crops such as alfalfa, which are harvested several times annually, is very complicated and has received less attention. Therefore, in this paper, the most important vegetation indices developed to estimate alfalfa yield are using Sentinel-2 time series images. In this research, 144 alfalfa samples were collected periodically in a destructive way from alfalfa farms of Magsal Agricultural and Production Company (Qazvin) near the time of satellite pass, and then the efficiency of 10 of the most famous vegetation indices to estimate alfalfa yield was evaluated based on Sentinel-2 images. The results of this research showed that the estimated alfalfa yield using the  index had the highest correlation () and the lowest root-mean-square-error (RMSE = 0.316 ) compared to the field data collected in the middle of August. In addition, the results showed that the red edge indices did not solve the saturation problem of vegetation indices and that the green vegetation indices were more capable of estimating alfalfa yield than the red edge indices.

کلیدواژه‌ها [English]

  • remote sensing
  • Agriculture
  • Red edge Index
  • Yield Estimation
  • Alfalfa
  • Sentinel-2
  1. آمارنامه وزارت جهاد کشاورزی، 1394-1393.
  2. Al-Gaadi, K.A., et al., 2016, "Prediction of potato crop yield using precision agriculture techniques", PloS one 11(9): e0162219.
  3. Asner, G.P., 1998, Biophysical and biochemical sources of variability in canopy reflectance, Remote sensing of Environment, 64(3), pp.234-253.
  4. Asrar, G.Q., Fuchs, M., Kanemasu, E.T. and Hatfield, J.L., 1984, Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat 1, Agronomy journal, 76(2), pp.300-306.
  5. Badhwar, G. and Henderson, K., 1981, "Estimating Development Stages of Corn from Spectral Data—An Initial Model 1", Agronomy Journal 73(4): 748-755.
  6. Ban, H.Y., Kim, K.S., Park, N.W. and Lee, B.W., 2016. "Using MODIS Data to Predict Regional Corn Yields". Remote Sensing, 9(1), p.16.
  7. Betbeder, J., Fieuzal, R. and Baup, F., 2016. "Assimilation of LAI and dry biomass data from optical and SAR images into an agro-meteorological model to estimate soybean yield". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), pp.2540-2553.
  8. Bolton, D.K. and Friedl, M.A., 2013. "Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics". Agricultural and Forest Meteorology, 173, pp.74-84.
  9. Brakke, T.W. and Kanemasu, E.T., 1981. "Insolation estimation from satellite measurements of reflected radiation". Remote sensing of Environment, 11, pp.157-167.
  10. Carlson, T.N. and Ripley, D.A., 1997. "On the relation between NDVI, fractional vegetation cover, and leaf area index". Remote sensing of Environment, 62(3), pp.241-252.
  11. Chang, J. and Shoshany, M., 2016, Red-edge ratio Normalized Vegetation Index for remote estimation of green biomass, Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International, IEEE.
  12. Dash, J. and Curran, P.J., 2007, April, Relationship between the MERIS vegetation indices and crop yield for the state of South Dakota, USA. In Proc. Envisat Symposium.
  13. Dempewolf, J., Adusei, B., Becker-Reshef, I., Hansen, M., Potapov, P., Khan, A. and Barker, B., 2014. "Wheat yield forecasting for Punjab Province from vegetation index time series and historic crop statistics". Remote Sensing, 6(10), pp.9653-9675.
  14. Dente, L.G., Satalino, F., Mattia and 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.
  15. Delegido, J., Verrelst, J., Alonso, L. and Moreno, J., 2011, Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), pp.7063-7081.Doraiswamy, P.
  16. Doraiswamy, P.C., Sinclair, T.R., Hollinger, S., Akhmedov, B., Stern, A., Prueger, J., 2005, Application of MODIS derived parameters for regional crop yield assessment, Remote sensing of environment, 97(2), pp.192-202.
  17. Duchemin, B., P. Maisongrande, G. Boulet and I. Benhadj (2008). "A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index", Environmental Modelling & Software 23(7): 876-892.
  18. Elachi, C. and Van Zyl, J.J., 2006. "Introduction to the physics and techniques of remote sensing". John Wiley & Sons.
  19. Elvidge, C.D. and Chen, Z., 1995. "Comparison of broad-band and narrow-band red and near-infrared vegetation indices". Remote sensing of environment, 54(1), pp.38-48.
  20. Ferencz, C., Bognar, P., Lichtenberger, J., Hamar, D., Tarcsai, G., Timar, G., Molnar, G., Pasztor, S.Z., Steinbach, P., Szekely, B. and Ferencz, O.E., 2004, Crop yield estimation by satellite remote sensing. International Journal of Remote Sensing, 25(20), pp.4113-4149.
  21. Goswami, S., J. Gamon, S., Vargas and Tweedie, C., 2015, "Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska," PeerJ PrePrints.
  22. Gitelson, A. and Merzlyak, M.N., 1994, "Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves", Journal of Photochemistry and Photobiology B: Biology 22(3): 247-252.
  23. Gitelson, A.A., Kaufman, Y.J. and Merzlyak, M.N., 1996, Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote sensing of Environment, 58(3), pp.289-298.
  24. Gitelson, A.A., Merzlyak, M.N. and Lichtenthaler, H.K., 1996, Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm, Journal of plant physiology, 148(3-4), pp.501-508.
  25. Gitelson, A.A., Viña, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G. and Leavitt, B., 2003, Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophysical Research Letters, 30(5).
  26. Guyot, G. and F. Baret, 1988, Utilisation de la haute resolution spectrale pour suivre l'etat des couverts vegetaux, Spectral Signatures of Objects in Remote Sensing.
  27. Hamar, D., Ferencz, C., Lichtenberger, J., Tarcsai, G. and Ferenczne Arkos, I., 1988, The use of remotely sensed data in yield forecasting. 2. Satellite experiments, Novenytermeles (Hungary).
  28. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote sensing of environment, 83(1-2), pp.195-213.
  29. Huete, A.R. and Tucker, C.J., 1991, Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery, International Journal of Remote Sensing, 12(6), pp.1223-1242.
  30. 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 Geoinformation 52: 65-81.
  31. Jiang, Z., Huete, A.R., Didan, K. and Miura, T., 2008, Development of a two-band enhanced vegetation index without a blue band, Remote sensing of Environment, 112(10), pp.3833-3845.
  32. JURECKA, F., HLAVINKA, P., LUKAS, V., TRNKA, M. and ZALUD, Z., 2016, Crop yield estimation in the field level using vegetation indices, MendelNet 2016, 1, pp.90-95.
  33. Kayad, A.G., Al-Gaadi, K.A., Tola, E.H., Madugundu, R. and Zeyada, A.M., 2015, Performance evaluation of hay yield monitoring system in large rectangular baler, American-Eurasian Journal of Agricultural & Environmental Sciences, 15, pp.1025-1032.
  34. Kogan, F., Salazar, L. and Roytman, L., 2012, Forecasting crop production using satellite-based vegetation health indices in Kansas, USA, International journal of remote sensing, 33(9), pp.2798-2814.
  35. Kogan, F. N., 1995, "Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data", Bulletin of the American Meteorological Society 76(5): 655-668.
  36. Kross, A., H. McNairn, D. Lapen, M. Sunohara and C. Champagne, 2015, "Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops", International Journal of Applied Earth Observation and Geoinformation 34: 235-248.
  37. Leroux, L., C. Baron, B. Zoungrana, S. B. Traore, D. L. Seen and A. Begue, 2016, "Crop monitoring using vegetation and thermal indices for yield estimates: case study of a rainfed cereal in semi-arid west africa", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9(1): 347-362.
  38. Li, H., Zhao, C., Yang, G. and Feng, H., 2015, Variations in crop variables within wheat canopies and responses of canopy spectral characteristics and derived vegetation indices to different vertical leaf layers and spikes, Remote Sensing of Environment, 169, pp.358-374.
  39. Lobell, D.B., Cassman, K.G. and Field, C.B., 2009. Crop yield gaps: their importance, magnitudes, and causes. Annual review of environment and resources, 34, pp.179-204.
  40. Lobell, D. B., D. Thau, C. Seifert, E. Engle and B. Little, 2015, "A scalable satellite-based crop yield mapper", Remote Sensing of Environment 164: 324-333.
  41. Malingreau, J., 1989, The vegetation index and the study of vegetation dynamics. Applications of remote sensing to agrometeorology, Springer: 285-303.
  42. Mkhabela, M. S., M. S. Mkhabela and N. N. Mashinini, 2005, "Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR" Agricultural and Forest Meteorology 129(1-2): 1-9.
  43. Morel, J., P. Todoroff, A. Begue, A. Bury, J.-F., Martine and M. Petit, 2014, "Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: A case study on Reunion Island", Remote Sensing 6(7): 6620-6635.
  44. Myneni, R. B., F. G. Hall, P. J. Sellers and Marshak, A.L., 1995, "The interpretation of spectral vegetation indexes," IEEE Transactions on Geoscience and Remote Sensing 33(2): 481-486.
  45. Pantazi, X. E., Moshou, D., Alexandridis, T., Whetton, R., and Mouazen, A.M., 2016, "Wheat yield prediction using machine learning and advanced sensing techniques" Computers and Electronics in Agriculture 121: 57-65.
  46. Petropoulos, G.P., and Kalaitzidisz, C., 2012, "Multispectral vegetation indices in remote sensing: an overview" Ecol. Model 2: 15-39.
  47. Raes, D., Steduto, P., Hsiao, T.C. and Fereres, E., 2009, "AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description" Agronomy Journal 101(3): 438-447.
  48. Reynolds, C.A., Yitayew, M., Slack, D.C., Hutchinson, C.F., Huete, A. and Petersen, M.S., 2000, Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data, International Journal of Remote Sensing, 21(18), pp.3487-3508.
  49. Robertson, M. J. and Kirkegaard,J. A. 2006, "Water-use efficiency of dryland canola in an equi-seasonal rainfall environment" Australian Journal of Agricultural Research 56(12): 1373-1386.
  50. Rouse Jr, J.W., 1972, "Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation."
  51. Sakamoto, T., A. A., Gitelson and T. J. Arkebauer, 2014, "Near real-time prediction of US corn yields based on time-series MODIS data" Remote Sensing of Environment 147: 219-231.
  52. Sirotenko, O. D., 2001, "Crop Modeling", Agronomy Journal 93(3): 650-a-653.
  53. Schwartz, M.D. and T. R. Karl, 1990, "Spring phenology: Nature's experiment to detect the effect of “green-up” on surface maximum temperatures", Monthly Weather Review 118(4): 883-890.
  54. Shunlin, L., 2004, "Quantitative remote sensing of land surfaces", A John Wiley & Sons. inc., Canada.
  55. Trotter, T.F., Frazier, P.S., Trotter, M.G. and Lamb, D.W., 2008, July, Objective biomass assessment using an active plant sensor (Crop Circle), preliminary experiences on a variety of agricultural landscapes, In Ninth International Conference on Precision Agriculture’. Denver, Colorado.(Ed. R. Khosla.)(Colorado State University: Fort Collins, CO.).
  56. Tucker, C.J., 1979, "Red and photographic infrared linear combinations for monitoring vegetation", Remote Sensing of Environment 8(2): 127-150.
  57. Wang, C., S. Nie, X. Xi, S. Luo and X., Sun, 2016, "Estimating the Biomass of Maize with Hyperspectral and LiDAR Data", Remote Sensing 9(1): 11.
  58. Wiegand, C.L. and A.J., Richardson, 1990, "Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield: I. Rationale", Agronomy Journal 82(3): 623-629.
  59. Wu, J., Wang, D., Bauer, M.E., 2007, Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies, Field crops research, 102(1), pp.3342.
  60. Xin, Q., Gong, P., Yu, C., Yu, L., Broich, M., Suyker, A.E. and Myneni, R.B., 2013. "A production efficiency model-based method for satellite estimates of corn and soybean yields in the Midwestern US". Remote Sensing, 5(11), pp.5926-5943.