| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,996 |
| تعداد مقالات | 33,553 |
| تعداد مشاهده مقاله | 44,160,051 |
| تعداد دریافت فایل اصل مقاله | 20,533,730 |
ارزیابی قابلیت داده های طیفی لندست هشت و طیف سنج آزمایشگاهی در پیش بینی عملکرد ذرت (مطالعه موردی: کشت و صنعت مغان) | ||
| پژوهش های کاربردی زراعی | ||
| دوره 33، شماره 3 - شماره پیاپی 128، آذر 1399، صفحه 117-134 اصل مقاله (742.94 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/aj.2020.125832.1393 | ||
| نویسندگان | ||
| حمید رضا متین فر* 1؛ وحید سروی2 | ||
| 1عضو هیئت علمی گروه علوم و مهندسی خاک دانشگاه لرستان | ||
| 2دانشجوی دکتری -گروه علوم و مهندسی خاک - دانشگاه لرستان | ||
| چکیده | ||
| شاخصهای پوشش گیاهی طیفی (SVIs) ترکیبی کمی از میزان جذب و پراکندگی طیفی گیاهان در محدوده های مختلف طیف الکترومغناطیسی می باشند و برای اندازه گیری ویژگی های محصول استفاده می شود.هدف این تحقیق پیش بینی عملکرد ذرت مبتنی بر داده های طیفی می باشد. بدین منظور ، تصاویر ماهواره ای Landsat-8 در طول چهار مرحله رشد ذرت تهیه گردید و همچنین همزمان با تاریخ تصویر‌برداری ماهواره لندست از منطقه مورد مطالعه، طیف سنجی نمونه‌های گیاهی با استفاده از طیف سنج Field Espect-3 انجام گرفت و شاخص گیاهی تفاضلی نرمال شده NDVI) )، شاخص گیاهی تعدیل کننده اثرخاک (SAVI )، شاخص گیاهی تفاضلی نرمال شده تغییر یافته (MNDVI) و شاخص گیاهی تعدیل کننده اثرخاک (OSAVI) مبتنی بر داده های ماهواره‌ای و طیف سنج آزمایشگاهی محاسبه گردید. بررسی نتایج مدلسازی و شاخصهای آماری رگرسیونی نشان می‌دهد که در هر دو حالت استفاده از تصاویر ماهواره‌ای لندست هشت و طیف سنج آزمایشگاهی در مرحله ظهورگل نسبت به سایر مراحل نمونه‌برداری ضریب تبیین شاخصها برای محاسبه شاخص سطح برگ و عملکرد با میزان همبستگی 54 الی 72 درصد از قابلیت بیشتری برخوردار می‌باشد. همچنین شاخصهای محاسبه شده از نتایج طیف سنجی و استفاده از داده‌های طیفی و مقایسه بین این دو نشان می‌دهد که دو شاخص NDVI و SAVI با ضریب تبیین 70 درصد بعنوان شاخصهای موثر در محاسبه شاخصها با استفاده از تصاویر ماهواره‌ای می‌باشند. درحالیکه شاخصهای MNDVI و OSAVI به ترتیب با ضریب تبیین 72 و 69 درصد مناسب‌ترین شاخص برای محاسبه شاخصها براساس نتایج طیف سنجی آزمایشگاهی می باشند . | ||
| کلیدواژه‌ها | ||
| شاخص سطح برگ؛ شاخص گیاهی؛ طیف سنج؛ عملکرد | ||
| عنوان مقاله [English] | ||
| Evaluation of Landsat 8 Spectral Data Capabilities and Laboratory Spectroradiometer for predicting corn yield (Case study: Moghan Agro-Industry) | ||
| نویسندگان [English] | ||
| Hamid Reza Matinfar1؛ Vahid Sarvi2 | ||
| 1Department of soil science, Lorestan University | ||
| 2Department of soil science | ||
| چکیده [English] | ||
| Introduction: Monitoring the growth stages and yield of crops in agricultural areas is essential for food security and farmers' income forecasts. Progress in remote sensing has greatly contributed to the process of monitoring of various developmental stages of agricultural crops and the evaluation of their yield (Anastasiou et al., 2018; Shi & Mo, 2011). Remote sensing (RS) and global positioning systems (GPS) can be used to evaluate the changes in crop dynamics, including its yield and spatial diversity (Dadhwall & Ray, 2000). Spectral vegetation indices (SVIs) are a combination of the spectral absorption and spatial distribution of plants in different electromagnetic spectral range and are used to measure the characteristics of a product. SVI provides a simple method for measuring spectral responses of plants throughout the season, which uses fundamental differences between soil and plants, and often as a kind of relationship between the energy of electromagnetism reflected in red and infrared wavelengths the red near (NIR) is expressed. Green healthy plants exhibit relatively low reflections in the visible range of the electromagnetic spectrum (high absorption of light for photosynthesis); however, its reflection is usually high in the near infrared region (Al-gaadi et al., 2016). Therefore, in this study, the remote sensing method spectrometric data were used to predict the yield of corn in the Moghan plant in northern Ardebil province. Materials and Methods: Landsat-8 satellite images were prepared during four growth stages of corn, and simultaneously at the dates when the satellite images of the study area were taken, spectroscopy of the plant samples was performed using the Field Espect-3 spectrometer. In this study, 30 corn fields were selected in the Moghan Plain to estimate the yield of corn. First, using the GPS device, the position of the farms was determined. Then, at different growth stages e.g. four-leaf stage, growth differentiation stage, flower emergence stage and physiological maturity, soil and plant samples were prepared according to standard methods and then the specimens were measured. Vegetation indices of NDVI, SAVI, MNDVI and OSAVI were calculated based on satellite data and laboratory spectrometers. Results and Discussion: The results in both cases- the use of Landsat 8 satellite images and laboratory spectrometer at flower emergence stage- showed that the correlations for coefficient of determination for leaf area index and yield were from 54% to 72%, which were more robust as compared to other growth stages. On the other hand, the evaluation of the indices obtained from the spectrophotometric results and the use of spectral data and the comparison between the two showed that the correlation for coefficient of determination for NDVI and SAVI was 70%, which were determined as effective indices for estimating leaf area index and yield using satellite imagery. While MNDVI and OSAVI indices were 72% and 69%, respectively, they were found to be the most suitable indicators for estimating leaf area index and yield based on the results of laboratory spectroscopy. Therefore, using satellite images, these indices are more likely to be present, while for MNDVI and OSAVI indices, particular wavelengths are studied, and given the fact that the laboratory spectrometer shows the slightest variations in each wavelength, these indicators can also be considered as robust. Conclusion: The results of the study showed that among the growth stages of the plant, flower emergence stage was the best for predicting the yield of the crop, and on the other hand, NDVI and SAVI indices resulting from Landsat 8's satellite imagery were found to be the most robust in predicting crop yield and MNDVI and OSAVI indices were found to be the best predictors of crop yield based on the results of spectral laboratory data. | ||
| کلیدواژه‌ها [English] | ||
| Leaf Area Index, Spectrometer, Vegetation Index, Yield | ||
| مراجع | ||
|
Al-gaadi, K.H., Hassaballa, A., Tola, E., Kayad, A., Madugundu, R., Alblewi, B., and Assiri, F. 2016. Prediction of potato crop yield using precision agriculture techniques. Plosone Journal, pp: 1-16. Analytical Spectral Devices Inc. 1997. Field SpecTM User’s guide. Analytical Spectral Devices Inc., Boulder, CO. Anastasiou, E., Balafoutis, A., Darra, N., Psiroukis, V. 2018. Satellite and proximal sensing to estimate the yield and quality of table grapes. Agriculture Journal, 8: 94. Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., and Royo, C. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agronomy Journal, 92(1). 83-91. Armenta, S., Garrigues, S., and Guardia, M. 2007. Partial least squares-near infrared determination of pesticides in commercial formulations. Vibrational spectroscopy, 44: 273-278. Asner, G.P., and Martin, R.E. 2008. Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote sensing of Environment, 112: 3958-3970. Behrens,T., Muller, J., and Diepenbrock, W. 2006. Utilization of canopy reflectance to predict properties of oilseed rape (Brassica napus L.) and barley (Hordeum vulgare L.) during ontogenesis. Europa Journal of Agronomy, 25:345-355. Bocco, M., Sayago, S., and Willington, E. 2014. Neural network and crop residue index multiband models for estimating crop residue cover from Landsat TM and ETM+ images. International Journal of Remote Sensing, 35(10). 3651-3663. Borgogno-Mondino, E., Lessio, A., Tarricone, L., Novello, V., and Palma, L.A. 2018. Comparison between multispectral aerial and satellite imagery in precision viticulture. Precise Agriculture, 19:195–217. Choukan, R. 2012. Maize and maize properties. Ministry of jihad-e-Agriculture, Agricultural research, education and extension Organization, Seed and Plant Improvement Institute. Dadhwall, V.K., and Ray, S.S. 2000. Crop assessment using remote sensing—Part II: Crop condition and yield assessment. Indian Journal of Agricultural Economics, 2(1947). 55–67. Elhag, M. 2016. Evaluation of different soil salinity mapping using remote sensing techniques in arid ecosystems (Saudi Arabia). International Journal of Remote Sensing, 14 (8).1495–1515. Haig L.A.S. 2003. Crop yield estimation: Integrating RS, GIS, management and land factors. A case study of Birkoor and Kortigiri Mandals- Nizamabad district, India. Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfillment of the requirements for the degree of Master of Science in Geoinformation Science and Earth Observation; Sustainable Agriculture. Holzman, M.E., Rivas, R., and Piccolo, M.C. 2014. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index. International Journal of Applied Earth Observation and Geoinformation, 28: 181-192. Johnson, B., Tateishi, R., and Kobayashi, T. 2012. Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers. Remote Sensing journal, 4: 2619-2634 Junges, A.H., Fontana, D.C., Anzanello, R., and Bremm, C. 2017. Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil. Ciênc. Agrotecnology journal, 41: 543–553. Martens, H., and Naes, T. 1989. Multivariate calibration. Second ed., John Wiley and Sons Ltd, Chichester, UK. 419 pages. Nahry, E.l., Ali, R.R., and Baroudy, A.A. 2011. An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques. Journal of Agricultural Water Management, 98: 517- 531. Panda, S.S., Ames, D.P., Panigrahi, S. 2010. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing Journal, 2: 673–696. Prasad, A., Chai, L., Singh, R., and Kafatos, M. 2006. Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1). 26-33. Garrigues, N.V., Shabanovb, K., Swanson, J.T., Morisette, F., and Baret, R.B. 2008. Intercomparison and sensitivity analysis of leaf area index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands, Agricultural and Forest Meteorology, 148: 1193–1209. Sadooghi, L., Homaee, M., Noroozi, A., and Asadi, S. 2016. Estimating rice yield using VSM model and satellite images in Guilan province. Cereal Research, 6(3): 397-410. Sanaeinejad, H., Nassiri, M., Zare, H., Salehnia, N., and Ghaemi, M. 2014. Wheat yield estimation using landsat images and field observation:A case study in Mashhad . Journal of Plant Production, 20 (4). 43-63. Shanahan, J.F., Schepers, J.S., Francis, D.D., Varvel, G.E., Wilhelm, W.W., Tringe, J.M., Schlemmer, M.R., and Major, D.J. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agronomy journal, 93: 583-589. Shi, H., and Mo, X. 2011. Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Journal of Ecological Modelling, 2)22(:2530- 2541. Thenkabail, P.S., Smith, R.B., DePauw, E. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing Environment Journal, 71: 158–182. Varvani, H., Bansoleh, B., and Sharifi, M.A. 2019. Evaluation of vegetation indices based on remote sensing at different growth stages for estimation Corn biomass. Journal of Crop Production, 11(3): 29-41. | ||
|
آمار تعداد مشاهده مقاله: 415 تعداد دریافت فایل اصل مقاله: 729 |
||