Document Type : Original Article


1 Ph.D. of Systems Management, Manager of Development of Space Technologies and Advanced Transportation

2 M.Sc of Business Management


Remote sensing is the science of obtaining information from the surface of the earth without explicit
contact with the components studied. Commercialization is a set of activities that converts an
innovation into a product or service that brings economic benefits. Given the widespread use for
measurement and the high importance of its application in agriculture, commercialization of this
technology in agriculture has been a top priority and investigated in this study. The target population
of this research is active and passive companies in this field to use their experience to provide suitable
field for cultivation of remote sensing technology through in-depth interviewing and snowball
sampling. The catch is used. In this research, using product and technology life cycle diagrams,
examining the challenges of technology and infrastructure commercialization, commercialization
elements, types of software used in the world agricultural industry, remote sensing investment charts
and analysis The viability of remote sensing in agriculture as a business has been scrutinized. As a
result, the best way to commercialize the product is to reduce constraints for active companies, build
the necessary infrastructure, especially timely data, and be independent in deploying this technology
to allow users to use a variety of business methods. Provide.


تاریخچة سنجش از دور، سازمان فضایی ایران، قابل دسترسی در:
تجاری‌سازی، پارک علمی ‌و فنّاوری دانشگاه تهران. دسترسی در:
ناصحی‌فر، و.، رحمت‌آبادی، ی.، 1397، 63 فرمان در تحقیقات بازاریابی، تهران: ترمه، چاپ اول (پاییز 1397).
Abdulridha, J., Ampatzidis, Y., Kakarla, S.C. & Roberts, P., 2019, Detection of Target Spot and Bacterial Spot Diseases in Tomato Using UAV-Based and Benchtop-Based Hyperspectral Imaging Techniques, Precis. Agric., 21, PP. 955-978.
Ali, A. & Imran, M.M., 2020, Evaluating the Potential of Red Edge Position (REP) of Hyperspectral Remote Sensing Data for Real Time Estimation of LAI & Chlorophyll Content of Kinnow Mandarin (Citrus Reticulata) Fruit Orchards, Sci. Hortic. Amst., 267, P. 109326.
Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G. & Bochtis, D., 2019, Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review, Remote Sens., 11, P. 676.
Babaeian, E., Sidike, P., Newcomb, M.S., Maimaitijiang, M., White, S.A., Demieville, J., Ward, R.W., Sadeghi, M., LeBauer, D.S., Jones, S.B. et al., 2019, A New Optical Remote Sensing Technique for High Resolution Mapping of Soil Moisture, Front. Big Data, 2, P. 37.
Bishop, B., 2009, European Union Law for International Business: An Introduction, Cambridge University Press, Cambridge.
Carry, Chin, 2011, A Study on the Commercialization of Space Based Remote Sensing in the 21st Century and It's Implications to United States National Security, California, Monterey, Naval Postgraduate School.
Castro, C., Armario, E. & Ruiz, D., 2014, The Influence of Employee Organizational Citizenship Behavior on Customer Loyalty, International Journal of Service Industry Management, 15(1).
Chalmers, D., 2010, European Union Law: Cases and Materials, 2nd ed. Cambridge University Press, Cambridge.
Chang, A., Chiang, H. & Han, T., 2015, Investigating the Dual-Route Effects of Corporate Branding on Brand Equity, Asia Pacific Management Review, 20(2).
Chang, C.Y. Zhou, R. Kira, O. Marri, S. Skovira, J. Gu, L. Sun, Y., 2020, An Unmanned Aerial System (UAS) for Concurrent Measurements of Solar Induced Chlo-rophyll Fluorescence and Hyperspectral Reflectance Toward Improving Crop Monitoring, Agric. For. Meteorol., 294, PP. 1-15.
Chang, H. & Wu, L., 2014, An Examination of Negative E-WOM Adoption: Brand Commitment as a Moderator, Decision Support Systems, 56(2).
Chen, S., She, D., Zhang, L., Guo, M. & Liu, X., 2019, Spatial Downscaling Methods of Soil Moisture Based on Multisource Remote Sensing Data and Its Application, Water, 11, P. 1401.
DadrasJavan, F., Samadzadegan, F., Pourazar, S.H.S. & Fazeli, H., 2019, UAV-Based Multispectral Imagery for Fast Citrus Greening Detection, J. Plant Dis. Protect., 126, PP. 307-318.
Dashwood, A., 2012, European Union Law, Hart, Oxford.
De Lara, A., Longchamps, L. & Khosla, R., 2019, Soil Water Content and High-Resolution Imagery for Precision Irrigation, Maize yield. Agron. J., 9, P. 174.
Delgado, J., Short, N.M., Roberts, D.P. & Vandenberg, B., 2019, Big Data Analysis for Sustainable Agriculture, FSUFS, 3, P. 54.
Dodge, M.S., 2009, Sovereignty and the Delimitation of Airspace: A Philosophical and Historical Survey Supported by the Resources of the Andrew G. Haley Archive, 35 J. Space L. 5(1).
Dong, T., Liu, J., Shang, J., Qian, B., Ma, B., Kovacs, J.M., Walters, D., Jiao, X., Geng, X. & Shi, Y., 2019, Assessment of Red-Edge Vegetation Indices for Crop Leaf Area Index Estimation, Remote Sens. Environ, 222, PP. 133-143.
Elbert, B., 2004, The Satellite Communication Applications Handbook, London, Artech House, 2nd ed.
Erkmen, E. & Hancer, M., 2015, Linking Brand Commitement and Brand Citizenship Behavior of Airline Employees: "The Role of Trust", Journal of Air Transport Management, 42(1).
Filippi, P., Jones, J.E., Niranjan, S., Wimalathunge, N.S., Somarathna, D.S.N.P., Liana, E., Pozza, L.E., Ugbaje, S.U., Jephcott, T.G., Paterson, S.E. et al., 2019, An Approach to Forecast Grain Crop Yield Using Multi-Layered, Multi-Farm Data Sets and Machine Learning, Precis. Agric., 20, PP. 1-16.
Fisher, J.B., Lee, B., Purdy, A.J., Halverson, G.H., Dohlen, M.B., Cawse-Nicholson, K., Wang, A., Anderson, R.G., Aragon, B., Arain, M.A. et al., 2020, ECOSTRESS: NASA’s Next Generation Mission to Measure Evapotranspiration from the International Space Station, Water Resour. Res., 56, P. e2019WR026058.
Habibi, M., Laroche, M. & Richard, M., 2014, The Roles of Brand Community and Community Engagement in Building Brand Trust on Social Media, Computers in Human Behavior, 37(1).
Han, S., Nguyen, B. & Lee, T., 2015, Consumer-Based Chain Restaurant Brand Equity, Brand Reputation, and Brand Trust, International Journal of Hospitality Management, 50(4).
Hao, Z., Zhao, H., Zhang, C., Wang, H. & Jiang, Y., 2019, Detecting Winter Wheat Irrigation Signals Using SMAP Gridded Soil Moisture Data, Remote Sens., 11, 2390.
Hartley, T., 2010, The Foundations of European Union Law, Oxford University Press, Oxford.
Hashimoto, N., Saito, Y., Maki, M. & Homma, K., 2019, Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields, Remote Sens., 11(18).
Hassan, M.A., Yang, M., Rasheed, A., Yang, G., Reynolds, M., Xia, X., Xiao, Y. & He, Z., 2019, A Rapid Monitoring of NDVI Across the Wheat Growth Cycle for Grain Yield Prediction Using a Multi-Spectral UAV Platform, Plant Sci., 282, PP. 95-103.
Hendricks, G.S., Shukla, S., Roka, F.M., Sishodia, R.P., Obreza, T.A., Hochmuth, G.J. & Colee, J., 2019, Economic and Environmental Consequences of Overfertilization Under Extreme Weather Conditions, J. Soil Water Conserv., 74(2), PP. 160-171.
Huang, H., Lan, Y., Yang, A., Zhang, Y., Wen, S. & Deng, J., 2020, Deep Learning Versus Object-Based Image Analysis (OBIA) in Weed Mapping of UAV Imagery, Int. J. Remote Sens., 41, PP. 3446-3479.
Ihuoma, S.O. & Madramootoo, C.A., 2019, Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants, Comput. Electron. Agric., 163, P. 104860.
Jha, K., Doshi, A., Patel, P. & Shah, M., 2019, A Comprehensive Review on Automation in Agriculture Using Artificial Intelligence, Artif. Intell. Agric., 2, PP. 1-12.
Jin, S. & Phua, J., 2015, The Moderating Effect of Computer Users’ Autotelic Need for Touch on Brand Trust, Perceived Brand Excitement, and Brand Placement Awareness in Haptic Games and In-Game Advertising (IGA), Computers in Human Behavior, 43(3).
Jung, N., Kim, S. & Kim, S., 2014, Influence of Consumer Attitude to Ward Online Brand Communit on Revisit Intention and Brand Trust, Journal of Retailing and Consumer Services, 21(3).
Kaczorowska, A., 2010, European Union Law, Routledge-Cavendish, New
Kao, D., 2016, The Moderating Roles of Ad Claim Type and Rhetorical Style in the Ads of Competitor Brands for Diluting the Consumers' Brand Commitment to the Existing Brands, Asia Pacific Management Review, 21(2).
Khan, M.S., Semwal, M., Sharma, A. & Verma, R.K., 2020, An Artificial Neural Network Model for Estimating Mentha Crop Biomass Yield Using Landsat 8 OLI, Precis. Agric., 21, PP. 18-33.
Khosravirad, M., Omid, M., Sarmadian, F. & Hosseinpour, S., 2019, Predicting Sugarcane Yields in Khuzestan Using a Large Time-Series of Remote Sensing Imagery Region, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42, PP. 645-648.
Knipper, K.R., Kustas, W.P., Anderson, M.C., Alfieri, J.G., Prueger, J.H., Hain, C.R., Gao, F., Yang, Y., McKee, L.G., Nieto, H. et al., 2019, Evapotranspiration Estimates Derived Using Thermal-Based Satellite Remote Sensing and Data Fusion for Irrigation Management in California Vineyards, Irrig. Sci., 37, PP. 431-449.
Lassoued, R. & Hobbs, J., 2015, Consumer Confidence in Credence Attributes: The Role of Brand Trust, Food Policy, 52(1).
Lin, Y., 2015, Innovative Brand Experience's Influence on Brand Equity and Brand Satisfaction, Journal of Business Research, 68(2).
Liu, C. & Chou, S., 2016, Tourism Strategy Development and Facilitation of Integrative Processes among Brand Equity, Marketing and Motivation, Tourism Management, 54(1).
Londono, J., Elms, J. & Davies, K., 2016, Conceptualising and Measuring Consumer-Based Brand–Retailer–Channel Equity, Journal of Retailing and Consumer Services, 29(2).
Lyall, F. & Larsen, P., 2009, Space Law: A Treatise, Ashgate, Surrey/Burlington.
Madry, S. & Pelton, J., 2010, Satellites in the Service to Humanity, in The Farthest Shore: A 21st Century Guide to Space, Apogee Press, Burlington.
Maes, W.H., 2019, Steppe, K. Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture, Trends Plant Sci., 24, PP. 152-154.
Mohammed, G.H., Colombo, R., Middleton, E.M., Rascher, U., van der Tole, C., Nedbald, L., Goulas, Y., Pérez-Priego, O., Damm, A., Meroni, M. et al., 2019, Remote Sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in Vegetation: 50 Years of Progress, Remote Sens. Environ., 231, PP. 1-39.
Mondello, C., Hepner, G. & Williamson, R., 2004, 10-Year Industry Forecast. Phase I-III – Study Documentation, Photogrammetric Engineering & Remote Sensing, 70(1).
Mudereri, B.T., Dube, T., Adel-Rahman, E.M., Niassy, S., Kimathi, E., Khan, Z. & Landmann, T., 2019, A Comparative Analysis of PlanetScope and Sentinel-2 Space-Borne Sensors in Mapping Striga Weed Using Guided Regularised Random Forest Classification Ensemble, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42, PP. 701-708.
Nagasubramanian, K., Jones, S., Singh, A.K., Sarkar, S., Singh, A. & Ganapathy-subramanian, B., 2019, Plant Disease Identification Using Explainable 3D Deep Learning on Hyperspectral Images, Plant Methods, 15, PP. 1-10.
Nyadzayo, M., Matanda, M. & Ewing, M., 2016, Franchisee-Based Brand Equity: The Role of Brand Relationship Quality and Brand Citizenship Behavior, Industrial Marketing Management, 52(2).
Olbrich, R., Jansen, H. & Hundt, M., 2016, Effects of Pricing Strategies and Product Quality on Private Label and National Brand Performance, Journal of Retailing and Consumer Services, 85(1).
Oliviera, M., Silveira, C. & Luce, F., 2015, Brand Equity Estimation Model, Journal of Business Research, 68(3).
Pelton, J.  Madry, S. & Camacho, L., 2013, Handbook of Satellite Applications, New York, Springer, 2nd ed.
Porricelli, M., Yurova, Y., Abratt, R. & Bendixen, M., 2014, Antecedents of Brand Citizenship Behavior in Retailing, Journal of Retailing and Consumer Services, 21(2).
Pourazar, H., Samadzadegan, F. & Javan, F.D., 2019, Aerial Multispectral Imagery for Plant Disease Detection: Radiometric Calibration Necessity Assessment, Eur. J. Remote Sens., 52, PP. 17-31.
Rajendra, P., Sishodia, L. & Sudhir K., 2020, Applications of Remote Sensing in Precision Agriculture: A Review, Journal of Remote Sens, 12, PP. 1-31.
Ranjan, R., Chandel, A.K., Khot, L.R., Bahlol, H.Y., Zhou, J., Boydston, R.A. & Miklas, P.N., 2019, Irrigated Pinto Bean Crop Stress and Yield Assessment Using Ground Based Low Altitude Remote Sensing Technology, Inf. Process. Agric., 6, PP. 502-514.
Siegfried, J., Longchamps, L. & Khosla, R., 2019, Multisectral Satellite Imagery to Quantify In-Field Soil Moisture Variability, J. Soil Water Conserv., 74, PP. 33-40.
Slomanson, W., 2011, Fundamental Perspectives on International Law, Boston, USA: Wadsworth.
Tan, C., Zhang, P., Zhou, X., Wang, Z., Xu, Z., Mao, W., Li, W., Huo, Z., Guo, W. & Yun, F., 2020, Quantitative Monitoring of Leaf Area Index in Wheat of Different Plant Types by Integrating nDVi and Beer-Lambert Law, Sci. Rep., 10, P. 929.
Towers, P.C., Strever, A. & Poblete-Echeverría, C., 2019, Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting, Remote Sens., 11, P. 1073.
Venancio, L.P., Mantovani, E.C., do Amaral, C.H., Neale, C.M.U., Gonçalves, I.Z., Filgueiras, R. & Campos, I., 2019, Forecasting Corn Yield at the Farm Level in Brazil Based on the FAO-66 Approach and Soil-Adjusted Vegetation Index (SAVI), Agric. Water Manag., 225, P. 105779.
Weiss, M., Jacob, F. & Duveillerc, G., 2020, Remote Sensing for Agricultural Applications: A Meta-Review, Remote Sens. Environ., 236, P. 111402.
Xie, L., Peng, J. & Huang, T., 2014, Crafting and Testing a Central Precept in Service-Dominant Logic: Hotelemployees’ Brand-Citizenship Behavior and Customers’ Brand Trust, International Journal of Hospitality Management, 42(1).
Zhang, J., Huang, Y., Pu, R., Gonzalez-Moreno, P., Yuan, L., Wu, K. & Huang, W., 2019, Monitoring Plant Diseases and Pests through Remote Sensing Technology: A Review, Comput. Electron. Agric., 165, P. 104943.
Zhang, P., Zhou, X., Wang, Z., Mao, W., Li, W., Yun, F., Guo, W. & Tan, C., 2020, Using HJ-ccD Image and pLS Algorithm to Estimate the Yield of Field-Grown Winter Wheat, Sci. Rep., 10, P. 5173.
Zhang, S., Zhao, G., Lang, K., Su, B., Chen, X., Xi, X. & Zhang, H., 2019, Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage, Sensors, 19, P. 1485.
Zhen, Z., Chen, S., Qin, W., Yan, G., Gastellu-Etchegorry, J.P., Cao, L., Murefu, M., Li, J. & Han, B., 2020, Potentials and Limits of Vegetation Indices with Brdf Signatures for Soil-Noise Resistance and Estimation of Leaf Area Index, IEEE Trans. Geosci. Remote Sens., 58, P. 5092-5108.