برآورد شاخص سطح برگ محصول ذرت با استفاده از تصاویر ماهوارة سنتینل‌ـ 2

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران

2 استادیار مرکز مطالعات سنجش از دور وGIS ، دانشکدة علوم زمین، دانشگاه شهید بهشتی

3 استاد مرکز مطالعات سنجش از دور وGIS ، دانشکدة علوم زمین، دانشگاه شهید بهشتی

4 دانشجوی دکتری مرکز مطالعات سنجش از دور وGIS ، دانشکدة علوم زمین، دانشگاه شهید بهشتی

چکیده

شاخص سطح برگ استخراج‌شده (LAI) از تصاویر سنجش از دور پارامتر مهمی، به‌منظور مدل‌سازی مکانی تولید پوشش گیاهی، محسوب می‌شود. معمولاً شاخص‌های پوشش گیاهی که با بازتاب طول موج‌های قرمز و مادون قرمز نزدیک محاسبه می‌شوند، در برآورد LAI با استفاده از روش‌های آماری، به‌کار می‌روند اما بسیاری از این شاخص‌ها در مقادیر متفاوت LAI به اشباع می‌رسند. برای رفع این محدودیت، بازتاب محدودة لبة قرمز استفاده شده است؛ بنابراین، باید قابلیت شاخص‌های متفاوت پوشش گیاهی استخراج‌شده از داده‌های سنجش از دور، برای برآورد LAI ذرت علوفه‌ای، ارزیابی شود. بدین‌منظور پنج مرحله نمونه‌برداری میدانی، با فاصلة زمانی نزدیک به گذر ماهوارة سنتینل‌ـ 2، از سوی مرکز تحقیقات فضایی پژوهشگاه فضایی ایران، اجرا شد و در مجموع، 234 نمونه از مزارع ذرت علوفه‌ای شرکت کشت و صنعت مگسال قزوین برداشت شد. سپس سیزده شاخص پوشش گیاهی متفاوت، با استفاده از سری زمانی تصاویر سنتینل‌ـ 2، محاسبه شد و برای برآورد آماری مقادیر LAI به‌کار رفت. نتایج نشان داد که شاخص EVI با ضریب همبستگی 76/0 برای برآورد شاخص سطح برگ ذرت علوفه‌ای بهترین عملکرد را داشته است. علاوه‌براین، مقدار RMSE روش‌های رگرسیون غیرخطی بیشتر از روش‌های خطی بوده است.

کلیدواژه‌ها

عنوان مقاله [English]

Analysis of Sentinel- 2 Satellite Images to Estimate Leaf Area Index of Corn Crops

نویسندگان [English]

  • Maedeh Behifar 1
  • Hossein Aghighi 2
  • Aliakbar Matkan 3
  • Hamid Salehi shahrabi 4
1 Space Research Institute, Iranian Space Research Center
2 Assistant Prof. of R.S. & GIS Research Center, Shahid Beheshti University
3 Prof. of R.S. & GIS Research Center, Shahid Beheshti University
4 Ph.D. Student of R.S. & GIS Research Center, Shahid Beheshti University
چکیده [English]

Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize.  For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin.  Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with  outperformed other VIs to estimate LAI of silage maize. Moreover, the  values of non-linear regression models were higher that the liner ones.

کلیدواژه‌ها [English]

  • Corn
  • Remote Sensing
  • Vegetation Index
  • Leaf Area Index

مراجع

وب‌سایت سازمان هواشناسی کشور، وضعیت هوای استان قزوین، http://www.irimo.ir.
Asrar, G., Fuchs, M., Kanemasu, E.T. & 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.
Atzberger, C., Jarmer, T., Schlerf, M., Kötz, B. & Werner, W., 2003, Spectroradiometric Determination of Wheat Bio-Physical Variables: Comparison of Different Empirical-Statistical Approaches, In: Goossens, R. (Eds.), Remote Sensing in Transitions, Proc. 23rd EARSeL symposium, Belgium, 2-5 June 2003, PP. 463-470.
Bannari, A.K.S., Haboudane, D. & Khurshid, K.S., 2006, Sensitivity Analysis of Chlorophyll Indices to Soil Optical Proprieties Using Eld, Airborne and Satellite Hypers- Pectral Data, Remote Sensing of Environment, 4, PP. 120-123.
Baret, F., De Solan, B., Lopez-Lozano, R., Ma, K. & Weiss, M., 2010, GAI Estimates of Row Crops from Downward Looking Digital Photos Taken Perpendicular to Rows at 57.5 Zenith Angle: Theoretical Considerations Based on 3D Architecture Models and Application to Wheat Crops, Agricultural and Forest Meteorology, 150(11), PP. 1393-1401.
Brown, L., Chen, J.M., Leblanc, S.G. & Cihlar, J., 2000, A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis, Remote Sensing of Environment, 71(1), PP. 16-25.
Chen, J.M. & Black, T.A., 1992, Defining Leaf Area Index for Non-Flat Leaves, Plant Cell and Environment, 15, PP. 421-429.
Chen, J.M., Liu, J., Cihlar, J. & Goulden, M.L., 1999, Daily Canopy Photosynthesis Model through Temporal and Spatial Scaling for Remote Sensing Applications, Ecological Modelling, 124(2), PP. 99-119.
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.
Cho, M.A., Skidmore, A., Corsi, F., van Wieren, S.E. & Sobhan, I., 2007, Estimation of Green Grass/Herb Biomass from Airborne Hyperspectral Imagery Using Spectral Indices and Partial Least Squares Regression, International Journal of Applied Earth Observation and Geoinformation, 9 (2007-4), PP. 375-391.
Clevers, J.G., Kooistra, L. & van den Brande, M.M., 2017, Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop, Remote Sensing, 9(5), P. 405.
Delegido, J., Verrelst, J., Alonso, L. & 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.
Deng, F., Chen, J.M., Plummer, S., Chen, M. & Pisek, J., 2006, Algorithm for Global Leaf Area Index Retrieval Using Satellite Imagery, IEEE Transactions on Geoscience and Remote Sensing, 44(8), PP. 2219-2229.
Gao, B.C., 1995, Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space, In Imaging Spectrometry (Vol. 2480, PP. 225-237). International Society for Optics and Photonics.
 
GCOS-138, 2010, Implementation Plan for the Global Observing System for Climate in Support to UNFCCC (2010 Update), WMO-TD/No. 1523. GCOS.
Geladi, P. & Kowalski, B.R., 1986, Partial Least-Squares Regression: A Tutorial, Analytica Chimica Acta, 185, PP. 1-17.
Gitelson, A.A., Gritz, Y. & Merzlyakc, M.N., 2003, Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves, Journal of plant physiology, 160(3), PP. 271-282.
Gitelson, A.A., Kaufman, Y.J. & Merzlyak, M.N., 1996, Use of a Green Channel in the Remote Sensing of Global Vegetation from EOS-MODIS, Remote Sensing of Environment, 58, PP. 289-298.
Gitelson, A. & 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), PP. 247-252.
Gitelson, A.A., Merzlyak, M.N. & 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.
Gong, P., Pu, R. & Miller, J.R., 1992, Correlating Leaf Area Index of Ponderosa Pine with Hyperspectral CASI Data, Canadian Journal of Remote Sensing, 18(4), PP. 275-282.
Hansen, P.M. & Schjoerring, J.K., 2003, Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression, Remote Sensing of Environment, 86(4), PP. 542-553.
Huete, A.R., 1988, A Soil-Adjusted Vegetation Index (SAVI), Remote Sensing of Environment, 25(3), PP. 295-309.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & 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.
Kaufman, Y.J. & Tanre, D., 1992, Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS, IEEE Transactions on Geoscience and Remote Sensing, 30(2), PP. 261-270.
Liu, J., Pattey, E. & Admiral, S., 2013, Assessment of in Situ Crop LAI Measurement Using Unidirectional View Digital Photography, Agricultural and Forest Meteorology, 169, PP. 25-34.
Liu, B., Yue, Y.M., Li, R., Shen, W.J. & Wang, K.L., 2014, Plant Leaf Chlorophyll Content Retrieval Based on a Field Imaging Spectroscopy System, Sensors, 14(10), PP. 19910-19925.
Louhaichi, M., Borman, M.M. & Johnson, D.E., 2001, Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat, Geocarto International, 16(1), PP. 65-70.
Louis, J., Debaecker, V., Pflug, B., Main-Korn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E. & Gascon, F., 2016, Sentinel-2 Sen2Cor: L2A Processor for Users, In Living Planet Symposium (Vol. 740, P. 91).
Major, D.J., Baret, F. & Guyot, G., 1990, A Vegetation Index Adjusted for Soil Brightness, International Journal of Remote Sensing, 11, PP. 727-740.
Mutanga, O. & Skidmore, A.K., 2004, Narrow Band Vegetation Indices Overcome the Saturation Problem in Biomass Estimation, International Journal of Remote Sensing, 25(19), PP. 3999-4014.
Neinavaz, E., Skidmore, A.K., Darvishzadeh, R. & Groen, T.A., 2016, Leaf Area Index Retrieved from Thermal Hyperspectral Data, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, PP. 99-105.
Propastin, P. & Erasmi, S., 2010, A Physically Based Approach to Model LAI from MODIS 250m Data in a Tropical Region, International Journal of Applied Earth Observation and Geoinformation, 12(1), PP. 47-59.
Rivera Caicedo, J.P., 2014, Optimized and Automated Estimation of Vegetation Properties: Opportunities for Sentinel-2, Universitat de València (España) en 2014.
Rondeaux, G., Steven, M. & Baret, F., 1996, Optimization of Soil-Adjusted Vegetation Indices, Remote Sensing of Environment, 55(2), PP. 95-107.
Rouse Jr., J., Haas, R.H., Schell, J.A. & Deering, D.W., 1974, Monitoring vegetation systems in the Great Plains with ERTS, NASA, Washington, D.C.
Tucker, C.J., 1979, Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8(2), PP. 127-150.
Verrelst, J., Rivera, J.P., Veroustraete, F., Muñoz-Marí, J., Clevers, J.G., Camps-Valls, G. & Moreno, J., 2015, Experimental Sentinel-2 LAI Estimation Using Parametric, Non-Parametric and Physical Retrieval Methods–A Comparison, ISPRS Journal of Photogrammetry and Remote Sensing, 108, PP. 260-272.
Wilhelm, W.W., Ruwe, K. & Schlemmer, M.R., 2000, Comparison of Three Leaf Area Index Meters in a Corn Canopy, Crop Science, 40(4), PP. 1179-1183.
Zheng, G. & Moskal, L.M., 2009, Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors, Sensors, 9(4), PP. 2719-2745. 

فایل ها

هم رسانی

ارجاع به این مقاله

آمار