ارزیابی قابلیت تصاویر ماهواره‌ای Sentinel-2 در تخمین میزان بایومس محصول ذرت علوفه‌ای منطقه مورد مطالعه: شرکت کشاورزی و دامپروری مگسال (قزوین)

نوع مقاله : علمی - پژوهشی

نویسندگان

1 کارشناس مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران

2 استادیار مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی

چکیده

استفاده از داده‌های ماهواره‌ای، در برآورد دقیق مقدار بایومس محصول به‌عنوان یکی از مهم‌ترین چالش‌های سنجش از دور محیطی محسوب می‌شود. اگرچه، به طور سنتی از شاخص‌های طیفی پوشش‌گیاهی استخراج شده از باندهای قرمز (R) و مادون قرمز نزدیک (NIR) برای برآورد آماری بایومس محصول استفاده شده است، اما بیشتر این شاخص‌ها در مقادیر خاصی از شاخص سطح برگ اشباع می‌شوند. لذا به‌منظور غلبه بر محدودیت اشباع­شدگی، اخیرا مطالعات زیادی بر روی استفاده از بازتابندگی طیفی در محدوده لبه قرمز انجام شده است. برای ارزیابی عملکرد شاخص‌های مختلف پوشش‌گیاهی در برآورد بایومس محصول، پنج نوبت نمونه برداری از ویژگی‌های بیوفیزیکی ذرت علوفه‌ای در طول دوره رشد این محصول در اراضی زراعی شرکت کشت و صنعت مگسال، قزوین انجام شد و جمعا 182 نمونه میدانی جمع‌آوری شد. سپس 10 شاخص طیفی از سری زمانی تصاویر Sentinel-2 که همزمان با نوبت‌های نمونه برداری میدانی در سال 2017 اخذ شده بودند، محاسبه شده و با استفاده از آنها بایومس ذرت علوفه‌ای برآورد شد. بایومس ذرت علوفه‌ای با اندازه‌گیری‌های میدانی مورد ارزیابی قرار گرفت. نتایج نشان داد شاخص  با ضریب تعیین   و کمترین ریشه میانگین مربعات خطا ، بهترین شاخص برای تخمین بایومس ذرت علوفه‌ای است. علاوه بر این، تحقیق حاضر نشان داد که تصاویر ماهواره‌ای Sentinel-2 با توان تفکیک مکانی بالا و محدوده لبه قرمز، قابلیت تخمین مقدار بایومس محصول ذرت علوفه‌ای را با دقت مناسب دارد. 

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Sentinel-2 imagery for the estimation of Silage maize biomass: A case study of Magsal Animal Husbandry & Agriculture, Qazvin, Iran

نویسندگان [English]

  • Farzaneh Hadadi 1
  • Hossain Aghighi 2
  • Ayoub Moradi 1
1 Remote sensing expert, Iranian Space Research Center
2 Assistant Professor in Remote Sensing, Remote Sensing and GIS Research Center
چکیده [English]

The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that  index with  and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that  index with  and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. 

کلیدواژه‌ها [English]

  • remote sensing
  • Time Series Analysis
  • Red edge Index
  • Biomass estimation
  • Silage maize

مراجع

  1. عاشورلو، د.، متکان، ع.، میرباقری، ب. و شهری، م.، استخراج توده زنده گندم با استفاده از داده های ماهواره ای و رگرسیون وزنی مکانی.
  2. آمارنامه وزارت جهاد کشاورزی، 1395-1394.
  3. Bao, Y., Gao, W. & Gao, Z., 2009, Estimation of winter wheat biomass based on remote sensing data at various spatial and spectral resolutions, Frontiers of earth science in China, 3(1), p.118.
  4. Bannari, A., Morin, D., Bonn, F. & Huete, A.R., 1995, A review of vegetation indices, Remote sensing reviews, 13(1-2), pp.95-120.
  5. Chang, J. & 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.
  6. Foody, G.M., Boyd, D.S. & Cutler, M.E., 2003, Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions, Remote sensing of environment, 85(4), pp.463-474.
  7. Gitelson, A. and M. N. Merzlyak, 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.
  8. 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.
  9. Gitelson, A.A., Viña, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G. & Leavitt, B., 2003, Remote estimation of leaf area index and green leaf biomass in maize canopies, Geophysical Research Letters, 30(5).
  10. Guyot, G. & Baret, F., 1988, Utilisation de la haute resolution spectrale pour suivre l'etat des couverts vegetaux, Spectral Signatures of Objects in Remote Sensing.
  11. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J. and Strachan, I.B., 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(3), pp.337-352.
  12. Hatfield, J.L. and J.H. Prueger, 2010, Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices, Remote Sensing 2(2): 562-578.
  13. Huete, A. & Tucker, C., 1991, Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery, International journal of remote sensing 12(6): 1223-1242.
  14. Im, J. & Jensen, J.R., 2005, A change detection model based on neighborhood correlation image analysis and decision tree classification, Remote Sensing of Environment 99(3): 326-340.
  15. Jiang, Z., Huete, A.R., Didan, K. & Miura, T., 2008, Development of a two-band enhanced vegetation index without a blue band, Remote sensing of Environment, 112(10), pp.3833-3845.
  16. Liu, J., Pattey, E., Miller, J.R., McNairn, H., Smith, A. & Hu, B., 2010, Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model, Remote Sensing of Environment, 114(6), pp.1167-1177.
  17. Mutanga, O. and A. K. Skidmore, 2004, Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions, International Journal of Applied Earth Observation and Geoinformation 5(2): 87-96.
  18. Nelson, R.F., Kimes, D.S., Salas, W.A. & Routhier, M., 2000, Secondary forest age and tropical forest biomass estimation using thematic mapper imagery: single-year tropical forest age classes, a surrogate for standing biomass, cannot be reliably identified using single-date tm imagery, Bioscience, 50(5), pp.419-431.
  19. Nguy-Robertson, A., Gitelson, A., Peng, Y., Viña, A., Arkebauer, T. & Rundquist, D., 2012, Green leaf area index estimation in maize and soybean: Combining vegetation indices to achieve maximal sensitivity, Agronomy Journal, 104(5), pp.1336-1347.
  20. Nicolas, T., Philippe, V. & HUANG, W.J., 2010, New index for crop canopy fresh biomass estimation, Spectroscopy and Spectral Analysis, 30(2), pp.512-517.
  21. Popescu, S.C., Wynne, R.H. & Scrivani, J.A., 2004, Fusion of small-footprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in Virginia, USA, Forest Science, 50(4), pp.551-565.
  22. Prabhakara, K., Hively, W.D. & McCarty, G.W., 2015, Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States, International Journal of Applied Earth Observation and Geoinformation, 39, pp.88-102.
  23. Reid, J.S., Koppmann, R., Eck, T.F. & Eleuterio, D.P., 2005, A review of biomass burning emissions part II: intensive physical properties of biomass burning particles, Atmospheric Chemistry and Physics, 5(3), pp.799-825.
  24. Rouse Jr, J.W., 1972, Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation.
  25. Roy, P. & Ravan, S.A., 1996, Biomass estimation using satellite remote sensing data-an investigation on possible approaches for natural forest, Journal of biosciences 21(4): 535-561.
  26. Steininger, M., 2000, Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia, International journal of remote sensing 21(6-7): 1139-1157.
  27. Stoskopf, N. C., 1981, Understanding crop production, Reston Publishing Company, Inc.
  28. Tilly, N., Hoffmeister, D., Schiedung, H., Hütt, C., Brands, J. and Bareth, G., 2014, Terrestrial laser scanning for plant height measurement and biomass estimation of maize, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
  29. Vina, A., Gitelson, A.A., Rundquist, D.C., Keydan, G., Leavitt, B. & Schepers, J., 2004, Monitoring maize (Zea mays L.) phenology with remote sensing, Agronomy Journal, 96(4), pp.1139-1147.
  30. Weiser, R., et al., 1986, Assessing grassland biophysical characteristics from spectral measurements, Remote Sensing of Environment 20(2): 141-152.
  31. Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J. & Ryu, S.R., 2004, Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA, Remote sensing of environment, 93(3), pp.402-411.
نشریه سنجش از دور و GIS ایران
دوره 10، شماره 4 - شماره پیاپی 4
اردیبهشت 1397
صفحه 99-120

فایل ها

هم رسانی

ارجاع به این مقاله

آمار