سامانه مدیریت نشریات علمی دانشگاه شهید بهشتی

گودرزدشتی, سلمان, سیفی, محمد, کهندل, مهشید, عاشورلو, داود, عقیقی, حسین. (1404). شناسایی مزارع سیب‌زمینی برمبنای شاخص فنولوژی و الگوریتم ماشین بردار پشتیبان با استفاده از سامانۀ رایانش ابری Google Earth Engine. , 17(1), 1-22. doi: 10.48308/gisj.2023.103148
سلمان گودرزدشتی; محمد سیفی; مهشید کهندل; داود عاشورلو; حسین عقیقی. "شناسایی مزارع سیب‌زمینی برمبنای شاخص فنولوژی و الگوریتم ماشین بردار پشتیبان با استفاده از سامانۀ رایانش ابری Google Earth Engine". , 17, 1, 1404, 1-22. doi: 10.48308/gisj.2023.103148
گودرزدشتی, سلمان, سیفی, محمد, کهندل, مهشید, عاشورلو, داود, عقیقی, حسین. (1404). 'شناسایی مزارع سیب‌زمینی برمبنای شاخص فنولوژی و الگوریتم ماشین بردار پشتیبان با استفاده از سامانۀ رایانش ابری Google Earth Engine', , 17(1), pp. 1-22. doi: 10.48308/gisj.2023.103148
گودرزدشتی, سلمان, سیفی, محمد, کهندل, مهشید, عاشورلو, داود, عقیقی, حسین. شناسایی مزارع سیب‌زمینی برمبنای شاخص فنولوژی و الگوریتم ماشین بردار پشتیبان با استفاده از سامانۀ رایانش ابری Google Earth Engine. , 1404; 17(1): 1-22. doi: 10.48308/gisj.2023.103148

شناسایی مزارع سیب‌زمینی برمبنای شاخص فنولوژی و الگوریتم ماشین بردار پشتیبان با استفاده از سامانۀ رایانش ابری Google Earth Engine

نشریه سنجش از دور و GIS ایران
دوره 17، شماره 1 - شماره پیاپی 65، 1404، صفحه 1-22    اصل مقاله (1.23 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.48308/gisj.2023.103148
نویسندگان
سلمان گودرزدشتی* ؛ محمد سیفی؛ مهشید کهندل؛ داود عاشورلو؛ حسین عقیقی
مرکز مطالعات سنجش از دور و GIS، دانشکدۀ علوم زمین، دانشگاه شهید بهشتی، تهران، ایران
چکیده
سابقه و هدف: سیب‌زمینی چهارمین محصول کشت‌شده در جهان است. با توجه به اهمیت استراتژیک این محصول در تأمین امنیت غذایی، تهیۀ نقشه‌های دقیق از سطوح زیرکشت آن اطلاعات ضروری برای تخمین و پیش‌بینی میزان عملکرد محصول در مقیاس‌های متفاوت را فراهم می‌کند. اگرچه تا کنون رویکردهای متفاوت سنجش از دور، مبتنی‌بر سنجنده‌های اپتیکی یا مایکروویو، به‌طور گسترده برای پایش مزارع گوناگون (شامل سطح زیرکشت محصولات، شرایط و پیش‌بینی عملکرد آنها) به کار رفته، با استفاده از داده‌های سنجش از دور و یادگیری ماشین کمتر برای شناسایی مزارع سیب‌زمینی اقدام شده‌ است. در این راستا، پژوهش حاضر به شناسایی و نگاشت محصول سیب‌زمینی در قطب تولید آن در کشور پرداخته است و سعی در مهیا‌سازی اطلاعات دقیق سطوح زیرکشت این محصول، برای حوزۀ مدیریت کلان کشاورزی را دارد.
مواد و روش‌ها: ازآنجاکه بیشتر محصولات کشاورزی، در طول دورۀ کشت، ویژگی‌های طیفی‌ـ‌ زمانی منحصربه‌فردی دارند، این پژوهش با استفاده از تصاویر سری زمانی و بدون آستانه‌گذاری صریح، روشی را برای تمایز دادن مزارع سیب‌زمینی از سایر محصولات مطرح کرده است. طبق این روش، با استفاده از لایه‌های مبتنی‌بر فنولوژی محصول سیب‌زمینی و نیز یادگیری ماشین، به شناسایی این محصول روی آورده شد. به‌منظور بهینه‌سازی پارامترهای داخلی الگوریتم، براساس داده‌های زمینی نوع محصول در سایت مورد مطالعه که مجموعاً شامل 1648 نمونه از مزارع سیب‌زمینی و سایر محصولات می‌شود، آموزش و ارزیابی مدل انجام شد. این داده‌ها با استفاده از گیرندۀ GPS دستی نمونه‌برداری شد. در این پژوهش، نگاشت مزارع سیب‌زمینی با استفاده از تصاویر ماهوارۀ سنتینل‌ـ 2 و الگوریتم ماشین‌ بردار‌ پشتیبان انجام شد. با تهیۀ لایه‌های ورودی مناسب که شامل شاخص فنولوژیکی محصول سیب‌زمینی و شاخص آماری میانۀ‌ NDVI (سری زمانی تصاویر ماهوارۀ سنتینل‌ـ 2) در بازه‌های مشخص می‌شود، مزارع سیب‌زمینی با دقت شناسایی شد. دراِدامه، این لایه‌ها به‌منزلۀ ورودی‌های ماشین‌ بردار ‌پشتیبان به کار رفت. به‌منظور آموزش مدل بهینه برای ماشین‌ بردار ‌پشتیبان با استفاده از کرنل RBF، مقادیر gamma و C با روش 5-fold cross validation بهینه‌سازی شد. سپس این مقادیر، در فرایند اجرای الگوریتم، با استفاده از سامانۀ رایانش ابری گوگل ارث انجین به کار رفت. کارآیی روش پیشنهادی در شهرستان‌های همدان و بهار که بیشترین میزان کشت این محصول را در ایران دارند، ارزیابی شد.
نتایج و بحث: براساس نتایج، مقادیر بهینه برای پارامترهای داخلی مدل C=70 و γ=0.3 محاسبه شد. این مقادیر در تابع RBF، به‌منظور شناسایی سطوح زیرکشت محصول سیب‌زمینی، در نظر گرفته شد. با اجرای الگوریتم طبقه‌بندی و سپس اعمال فیلتر Majority، نقشۀ سطوح زیرکشت سیب‌زمینی برای منطقۀ مورد مطالعه تهیه شد. این نقشه بیشترین تراکم کشت محصول سیب‌زمینی را در محدودۀ مرزی دو شهرستان (شمال‌غرب شهرستان همدان و شرق شهرستان بهار) نشان داد. سطح زیرکشت سیب‌زمینی برای سال زراعی 1399-1400، در شهرستان همدان، برابر 1/4527 هکتار و در شهرستان بهار، برابر 3/6088 هکتار به دست آمد. در ارزیابی نتایج، صحت کلی و ضریب کاپا به‌ترتیب برای همدان، 9/90% و 82/0 و برای بهار، 3/93% و 87/0 براساس ماتریس خطا برآورد شد. نتایج پژوهش حاضر به کارآیی الگوریتم ماشین ‌بردار‌ پشتیبان در شناسایی سطوح زیرکشت محصول سیب‌زمینی اشاره دارد و همچنین نشان داده است که شاخص‌های منطبق‌بر فنولوژی سیب‌زمینی را می‌توان، به‌منزلۀ ویژگی‌های متمایزکننده در شناسایی بهتر مزارع این محصول، استفاده کرد.
نتیجه‌گیری: شناسایی مزارع سیب‌زمینی، با استفاده از لایه‌های ورودی شاخص‌های منطبق‌بر فنولوژی محصول در الگوریتم ماشین‌ بردار‌ پشتیبان، نشان داد این روش می‌تواند صحت شناسایی سطوح زیرکشت این محصول را در سطح پایلوت بهبود ببخشد. ازاین‌رو می‌توان، برای شناسایی سایر محصولات مهم کشاورزی و نیز در دیگر مناطق، رویکردی مشابه را پیش گرفت و نتایج را ارزیابی کرد. همچنین پیشنهاد می‌شود کارآیی داده‌های مایکروویو و سایر الگوریتم‌های یادگیری ماشین در پژوهش‌های آینده مورد توجه قرار گیرد.
کلیدواژه‌ها
شناسایی محصول؛ سیب‌زمینی؛ سری زمانی تصاویر ماهوارۀ سنتینل‌ـ 2؛ سامانۀ گوگل ارث انجین؛ ماشین‌ بردار پشتیبان
عنوان مقاله [English]
Potato Fields Mapping Based on the Phenology Feature and Support Vector Machine Utilizing Google Earth Engine Platform
نویسندگان [English]
Salman Goodarzdashti؛ Mohamad Seifi؛ Mahshid Kohandel؛ Davoud Ashourloo؛ Hossein Aghighi
Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
چکیده [English]
Introduction: Potato is the fourth most cultivated crop worldwide. In terms of its strategic role in food security, accurate potato mapping provides essential information for national crop censuses and potato yield estimation and prediction at any scale. Although remote sensing (RS) approaches based on optical and/or microwave sensors have been widely employed to monitor cultivated lands (including crop area, conditions, and yield forecasting), the identification of potato planting areas using RS data and machine learning has not been much addressed. As a result, the present research addresses the literature gap by suggesting an effective potato mapping approach in Iran's main production center and tries to provide accurate information on the cultivated areas of this crop for the field of agricultural management.
Material and Methods: Since most crops have specific spectral and temporal characteristics during their cultivation period, this research has presented a method to discriminate potato fields from other crops using time series images without explicit thresholding. Is. This method identified this product by using layers based on potato phenology and machine learning. We employed the ground truth data of the crop types from the studied site, which included a total of 1648 samples of potato fields and other crops, to optimize the internal parameters of the algorithm, train, and evaluate the model. A handheld GPS receiver was used to collect this data. This research employed Sentinel-2 satellite images and the Support Vector Machine (SVM) algorithm to map potato fields. To accurately identify potato fields, we prepared appropriate input layers, including the phenological index of the potato crop and the median statistical index of NDVI (time series of Sentinel-2 satellite images) at specific intervals. We used these layers as inputs to the SVM. We optimized the gamma and C values using the 5-fold cross-validation method to train the optimal model for SVM using the RBF kernel. We then used these values in the algorithm implementation process under the Google Earth Engine cloud computing platform. We assessed the efficacy of the suggested approach in the Iranian cities of Hamedan and Bahar, key sites for the cultivation of this particular crop.
Results and Discussion: Based on the results, the optimal values for the internal parameters of the model (C = 70 and γ = 0.3) were calculated. We included these values in the RBF function to identify the cultivated areas of the potato crop. By implementing the classification algorithm and then applying the majority filter, a map of the areas under potato cultivation was prepared for the study area. This map showed the highest density of potato cultivation in the border area of two cities (northwest of Hamedan city and east of Bahar city). The calculated total area for potato farming was 4527.1 hectares in Hamedan city and 6088.3 hectares in Bahar city. The estimated overall accuracy and Kappa coefficient are 90.9% and 0.82 for Hamedan and 93.3% and 0.87 for Bahar, respectively. The present research's results demonstrate the effectiveness of the SVM algorithm in detecting potato cultivation areas, highlighting the potential of using indicators corresponding to potato phenology as distinguishing features for improved identification.
Conclusion: By employing the SVM method, we effectively identified potato fields by utilizing layers of indicators that correspond to crop phenology. At the trial stage, it was demonstrated that this method can improve the potato acreage mapping process. Therefore, a similar approach can be evaluated for identifying other important crops in other regions. It is also suggested that the efficiency of microwave data and other machine learning algorithms be considered in future research.
کلیدواژه‌ها [English]
Crop mapping, Potato, Sentinel-2 Time Series, Google Earth Engine Platform, Support Vector Machine
مراجع

Abdolmaleky, M., Mahdei, K.N. & Nejatian, P., 2022, Environmental Sustainability Assess-ment: Potato Production in Western Iran, Process Integration and Optimization for Sustainability, 6(4), PP. 1063-1073, https://doi.org/10.1007/s41660-022-00262-2.

Adrian, J., Sagan, V. & Maimaitijiang, M., 2021, Sentinel SAR-Optical Fusion for Crop Type Mapping Using Deep Learning and Google Earth Engine, ISPRS Journal of Photogrammetry and Remote Sensing, 175, PP. 215-235, https://doi.org/10.1016/j. isprsjprs.2021.02.018.

Agricultural jihad of Hamedan Province., 2019, Agricultural Statistics of Hamedan, http://hm.agri-jahad.ir, Hamadan (In Persian).

Akcay, O. & Avsar, E.O., 2017, The Effect of Image Enhancement Methods during Feature Detection and Matching of Thermal Images, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, P. 575, https://doi.org/10.5194/isprs-archives-XLII-1-W1-575-2017.

Ashourloo, D., Shahrabi, H.S., Azadbakht, M., Aghighi, H., Matkan, A.A. & Radiom, S., 2018, A Novel Automatic Method for Alfalfa Mapping Using Time Series of Landsat-8 Oli Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, PP. 4478-4487, https://doi.org/ 10.1109/JSTARS. 2018.2874726.

Ashourloo, D., Shahrabi, H.S., Azadbakht, M., Rad, A.M., Aghighi, H. & Radiom, S., 2020, A Novel Method for Automatic Potato Mapping Using Time Series of Sentinel-2 Images, Computers and Electronics in Agriculture, 175, https://doi.org/10.1016/ j.compag.2020.105583.

Ashourloo, D., Nematollahi, H., Huete, A., Aghighi, H., Azadbakht, M., Shahrabi, H.S. & Goodarzdashti, S., 2022, A New Phenology-Based Method for Mapping Wheat and Barley Using Time-Series of Sentinel-2 Images, Remote Sensing of Environment, 280, P. 113206, https://doi.org/10.1016/j.rse.2022.113206.

Atzberger, C., 2013, Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and major Information Needs, Remote Sensing, 5, PP. 949-981, https://doi.org/10.3390/rs5020949.

Baillarin, S.J., Meygret, A., Dechoz, C., Petrucci, B., Lacherade, S., Trémas, T., Isola, C., Martimort, P. & Spoto, F., 2012, Sentinel-2 Level 1 Products and Image Processing Performances, 2012 IEEE International Geoscience and Remote Sensing Symposium, IEEE, PP. 7003-7006, https:// doi.org/ 10.1109/IGARSS.2012.6351959.

Basak, D., Pal, S., Ch, D. & Patranabis, R., 2007, Support Vector Regression, Neural Inf. Process, 11, PP. 203-225.

Bazgă, B., 2015, Food Security Component of Sustainable Development – Prospects and Challenges in the Next Decade, Procedia Economics and Finance, 32, PP. 1075-1082, https://doi.org/10.1016/S2212-5671(15)01570-1.

Boser, B.E., Guyon, I.M. & Vapnik, V.N., 1992, Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, PP. 144-152, https://doi.org/10.1145/ 130385.130401.

Burges, C.J.C., 1998, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 2, PP. 121-167, https://doi.org/10.1023/A: 1009715923555.

Christensen, C.T., Rens, L.R., Pack, J.E., Zotarelli, L., Hutchinson, C., Dahl, W., Gergela, D. & White, J.M., 2013, Growing Potatoes in the Florida Home Garden, HS933/HS183, 4/2013, EDIS, 2013.

da Silva Junior, C.A., Leonel-Junior, A.H.S., Rossi, F.S., Correia Filho, W.L.F., de Barros Santiago, D., de Oliveira-Júnior, J.F., Teodoro, P.E., Lima, M. & Capristo-Silva, G.F., 2020, Mapping Soybean Planting Area in Midwest Brazil with Remotely Sensed Images and Phenology-Based Algorithm Using the Google Earth Engine Platform, Computers and Electronics in Agriculture, 169, P. 105194, https://doi.org/ 10.1016/j.compag.2019.105194.

Das, M., Ghosh, S.K., Chowdary, V.M., Mitra, P. & Rijal, S., 2022, Statistical and Machine Learning Models for Remote Sensing Data Mining-Recent Advancements, Remote Sensing, 14(8), P. 1906, https://doi.org/ 10.3390/rs14081906.

Devaux, A., Kromann, P. & Ortiz, O., 2014, Potatoes for Sustainable Global Food Security, Potato Research 57(3), PP. 185-199, https://doi.org/10.1007/s11540-014-9265-1.

Dheeravath, V., Thenkabail, P.S., Chandrakantha, G., Noojipady, P., Reddy, G.P.O., Biradar, C.M., Gumma, M.K. & Velpuri, M., 2010, Irrigated Areas of India Derived Using MODIS 500 m Time Series for the Years 2001-2003, ISPRS Journal of Photogrammetry and Remote Sensing, 65, PP. 42-59, https://doi.org/10.1016/j.isprsjprs. 2009.08.004.

Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., Jin, C., Zhou, Y., Wang, J. & Biradar, C., 2015, Tracking the Dynamics of Paddy Rice Planting Area in 1986–2010 through Time Series Landsat Images and Phenology-Based Algorithms, Remote Sensing of Environment, 160, PP. 99-113, https://doi.org/10.1016/j.rse.2015.01.004.

Egorov, A.V, Hansen, M.C., Roy, D.P., Kommareddy, A. & Potapov, P. V, 2015, Image Interpretation-Guided Supervised Classification Using Nested Segmentation, Remote Sensing of Environment, 165, PP. 135-147, https://doi.org/10.1016/j.rse.2015. 04.022.

ESA., 2022, User Guides, https://sentinels. copernicus.eu/web/sentinel/user-guides/sentinel-2-msi.

FAO, 2016, FAOSTAT. 2016. FAO Satisical Database, fao.org/publications.

Fatemi, S.B., Mubasheri, M.R. & Abkar, A.A., 2014, The Effect of Using Spatial Neighborhood Information on the Accuracy of Satellite Image Clustering, Journal of Geomatics Science and Technology, 3(4), P. 77 (In Persian).

Feng, S., Zhao, J., Liu, T., Zhang, H., Zhang, Z. & Guo, X., 2019, Crop Type Identification and Mapping Using Machine Learning Algorithms and Sentinel-2 Time Series Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, PP. 3295-3306, https://doi.org/ 10.1109/JSTARS.2019.2922469.

Foody, G.M. & Mathur, A., 2004, Toward Intelligent Training of Supervised Image Classifications: Directing Training Data Acquisition for SVM Classification, Remote Sensing of Environment, 93, PP. 107-117, https://doi.org/10.1016/j.rse.2004.06.017.

Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M. & Toulmin, C., 2010, Food Security: The Challenge of Feeding 9 Billion People, Science, 327, PP. 812-818, https://doi.org/ 10.1126/science. 1185383.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R., 2017, Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone, Remote Sensing of Environment, 202, PP. 18-27, https://doi.org/10.1016/j.rse.2017.06.031.

Hamedani, S.R., Shabani, Z. & Rafiee, S., 2011, Energy Inputs and Crop Yield Relationship in Potato Production in Hamadan Province of Iran, Energy, 36(5), PP. 2367-2371, https://doi.org/10.1016/j.energy.2011.01.013.

Htitiou, A., Boudhar, A., Lebrini, Y., Hadria, R., Lionboui, H. & Benabdelouahab, T, 2022, A Comparative Analysis of Different Phenological Information Retrieved from Sentinel-2 Time Series Images to Improve Crop Classification: A Machine Learning Approach, Geocarto International, 37(5), PP. 1426-1449, https://doi.org/10.1080/10106049. 2020.1768593.

Hu, Q., Sulla-Menashe, D., Xu, B., Yin, H., Tang, H., Yang, P. & Wu, W., 2019, A Phenology-Based Spectral and Temporal Feature Selection Method for Crop Mapping from Satellite Time Series, International Journal of Applied Earth Observation and Geoinformation, 80, PP. 218-229, https://doi.org/10.1016/j.jag.2019.04.014.

Jaʿfari, A., 2005, Gitā-Šenāsi-e-Irān I: Kuhhā va Kuh-Nāma-ye Irān, Gitashenasi Institute Tehran (In Persian).

James, G., Witten, D., Hastie, T. & Tibshirani, R., 2013, Bias-Variance Trade-Off for k-Fold Cross-Validation, An Introduction to Statistical Learning - with Applications in R, 7, P. 24. https://doi.org/10.1080/22797254. 2017.1297540.

Jayne, T.S., Chamberlin, J. & Headey, D.D., 2014, Land Pressures, the Evolution of Farming Systems, and Development Strategies in Africa: A Synthesis, Food Policy, 48, PP. 1-17, https://doi.org/10.1016/ j.foodpol.2014.05.014.

Jefferies, R.A. & Lawson, H.M., 1991, A Key for the Stages of Development of Potato (Solatium Tuberosum), Annals of Applied Biology, 119, PP. 387-399, https://doi.org/ 10.1111/j.1744-7348.1991.tb04879.x.

Jeganathan, C., Dash, J. & Atkinson, P.M., 2014, Remotely Sensed Trends in the Phenology of Northern High Latitude Terrestrial Vegetation, Controlling for Land Cover Change and Vegetation Type, Remote Sensing of Environment, 143, PP. 154-170, https://doi.org/10.1016/j.rse.2013.11.020.

Julien, Y., Sobrino, J.A. & Jiménez-Muñoz, J.-C., 2011, Land Use Classification from Multitemporal Landsat Imagery Using the Yearly Land Cover Dynamics (YLCD) Method, International Journal of Applied Earth Observation and Geoinformation, 13(5), PP. 711-720, https://doi.org/10.1016/ j.jag.2011.05.008.

Kaplan, G. & Avdan, U., 2017, Object-Based Water Body Extraction Model Using Sentinel-2 Satellite Imagery, European Journal of Remote Sensing, 50, PP. 137-143, https://doi.org/10.1080/22797254.2017.1297540.

Kidane, Y., 2022, Vegetation Diversity and Distribution along the Bale Mountains Afroalpine Hotspot of Biodiversity in the Face of a Fast-Changing World, Doctoral Dissertation, https://doi.org/10.15495/EPub_ UBT_00006510.

Lary, D.J., Alavi, A.H., Gandomi, A.H. & Walker, A.L., 2016, Machine Learning in Geosciences and Remote Sensing, Geoscience Frontiers, 7, PP. 3-10, https://doi.org/10.1016/j.gsf.2015.07.003.

Li, C., Li, H., Li, J., Lei, Y., Li, C., Manevski, K. & Shen, Y., 2019, Using NDVI Percentiles to Monitor Real-Time Crop Growth, Computers and Electronics in Agriculture, 162, PP. 357-363, https://doi.org/10.1016/ j.compag.2019.04.026.

Liu, L., Xiao, X., Qin, Y., Wang, J., Xu, X., Hu, Y. & Qiao, Z., 2020a, Mapping Cropping Intensity in China Using Time Series Landsat and Sentinel-2 Images and Google Earth Engine, Remote Sensing of Environment, 239, P. 111624, https://doi.org/ 10.1016/j.rse.2019.111624.

Liu, X., Zhai, H., Shen, Y., Lou, B., Jiang, C., Li, T., Hussain, S.B. & Shen, G., 2020b, Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, PP. 414-427, http://doi.org/ 10.1109/JSTARS.2019.2963539.

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G. & Johnson, B.A., 2019, Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review, ISPRS Journal of Photogrammetry and Remote Sensing, 152, PP. 166–177, https://doi.org/10.1016/ j.isprsjprs.2019.04.015.

Massey, R., Sankey, T.T., Congalton, R.G., Yadav, K., Thenkabail, P.S., Ozdogan, M. & Sánchez Meador, A.J., 2017, MODIS Phenology-Derived, Multi-Year Distribution of Conterminous U.S. Crop Types, Remote Sensing of Environment, 198, PP. 490-503, https://doi.org/10.1016/j.rse.2017.06.033.

Mather, P.M., 2004, Computer Processing of Remotely-Sensed Images: An Introduction, John Wiley & Sons, Inc., Hoboken, NJ, USA.

Maxwell, A.E., Warner, T.A. & Fang, F., 2018, Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review, International Journal of Remote Sensing, 39, PP. 2784-2817, https:// doi.org/10.1080/01431161.2018.1433343.

Mergos, G. & Papanastassiou, M., 2017, Food Security and Sustainability: Globalisation, Investment and Financing, Food Security and Sustainability, PP. 1-34, https://doi.org/ 10.1007/978-3-319-40790-6_1.

Mhango, J.K., Harris, W.E. & Monaghan, J.M., 2021, Relationships between the Spatio-Temporal Variation in Reflectance Data from the Sentinel-2 Satellite and Potato (Solanum Tuberosum L.) Yield and Stem Density, Remote Sensing, 13(21), P. 4371, https://doi.org/10.3390/rs13214371.

Mingwei, Z., Qingbo, Z., Zhongxin, C., Jia, L., Yong, Z. & Chongfa, C., 2008, Crop Discrimination in Northern China with Double Cropping Systems Using Fourier Analysis of Time-Series MODIS Data, International Journal of Applied Earth Observation and Geoinformation, 10, PP. 476-485, https://doi.org/10.1016/j.jag.2007.11.002.

Misra, G., Cawkwell, F. & Wingler, A., 2020, Status of Phenological Research Using Sentinel-2 Data: A Review, Remote Sensing, 12(17), P. 2760, https://doi.org/ 10.3390/rs12172760.

Mohammadi, A., Tabatabaeefar, A., Shahin, S., Rafiee, S. & Keyhani, A., 2008, Energy Use and Economical Analysis of Potato Production in Iran a Case Study: Ardabil Province, Energy Conversion and Management, 49, PP. 3566-3570, https:// doi.org/10.1016/j.enconman.2008.07.003.

Mohammadi, A., Costelloe, J.F. & Ryu, D., 2017,Application of Time Series of Remotely Sensed Normalized Difference Water, Vegetation and Moisture Indices in Characterizing Flood Dynamics of Large-Scale Arid Zone Floodplains, Remote Sensing of Environment, 190, PP. 70-82, https://doi.org/10.1016/j.rse.2016.12.003.

Mountrakis, G., Im, J. & Ogole, C., 2011, Support Vector Machines in Remote Sensing: A Review, ISPRS Journal of Photogrammetry and Remote Sensing, 66, PP. 247-259, https://doi.org/10.1016/ j.isprsjprs.2010.11.001.

Myneni, R.B., Maggion, S., Iaquinta, J., Privette, J.L., Gobron, N., Pinty, B., Kimes, D.S., Verstraete, M.M. & Williams, D.L., 1995, Optical Remote Sensing of Vegetation: Modeling, Caveats, and Algorithms, Remote Sensing of Environment, 51, PP. 169-188, https://doi.org/10.1016/0034-4257(94) 00073-V.

Navulur, K., 2006, Multispectral Image Analysis Using the Object-Oriented Paradigm, Multispectral Image Analysis Using the Object-Oriented Paradigm, PP. 1-155, https://doi.org/10.1201/9781420043075.

Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., Blackburn, G.A., Yan, J. & Liu, J., 2015, Mapping Crop Phenology Using NDVI Time-Series Derived from HJ-1 A/B Data, International Journal of Applied Earth Observation and Geoinformation, 34, PP. 188-197, https:// doi.org/10.1016/j.jag.2014.08.011.

Rao, P., Zhou, W., Bhattarai, N., Srivastava, A.K., Singh, B., Poonia, S., Lobell, D.B. & Jain, M., 2021, Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms, Remote Sensing, 13(10), P. 1870, https://doi.org/10.3390/ rs13101870.

Rouzbahani, M., Farsizadeh, N., Fathian, V. & Hajeb, M., 2014, Automatic Mapping of Urban Objects Using Remote Sensing Data, The 1st National Conference on Modern Studies and Research in Geography, Architecture and Urban Development of Iran, Tehran (In Persian).

Saad El Imanni, H., El Harti, A., Hssaisoune, M., Velastegui-Montoya, A., Elbouzidi, A., Addi, M., El Iysaouy, L. & El Hachimi, J., 2022, Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region, Journal of Imaging, 8(12), P. 316, https://doi.org/ 10.3390/jimaging8120316.

Sebbar, B.E., Moumni, A. & Lahrouni, A., 2020, Impact of Internal Parameterization on the Performance of Support Vector Machines for Crop Mapping Sentinel-2 NDVI Time Series, International conference on Advanced Technologies for Humanity, 1.

Shao, Y. & Lunetta, R.S., 2012, Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points, ISPRS Journal of Photogrammetry and Remote Sensing, 70, PP. 78-87, https://doi.org/10.1016/j.isprsjprs. 2012.04.001.

Shi, W., Zhang, M., Zhang, R., Chen, S. & Zhan, Z., 2020, Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges, Remote Sensing, 12, P. 1688, https://doi.org/10.3390/rs12101688.

Shin, K.S., Lee, T.S. & Kim, H.J., 2005, An Application of Support Vector Machines in Bankruptcy Prediction Model, Expert Systems with Applications, 28, PP. 127-135, https://doi.org/10.1016/j.eswa.2004.08.009.

Spooner, D.M., McLean, K., Ramsay, G., Waugh, R. & Bryan, G.J., 2005, A Single Domestication for Potato Based on Multilocus Amplified Fragment Length Polymorphism Genotyping, Proceedings of the National Academy of Sciences, 102, PP. 14694-14699, https://doi.org/10.1073/pnas.050740010.

Sun, Z., Di, L., Fang, H. & Burgess, A., 2020, Deep Learning Classification for Crop Types in North Dakota, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, PP. 2200-2213, https://doi.org/10.1109/JSTARS. 2020.2990104.

Tarabalka, Y., Fauvel, M., Chanussot, J. & Benediktsson, J.A., 2010, SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, 7, PP. 736-740, https://doi.org/ 10.1109/LGRS.2010.2047711.

Tatsumi, K., Yamashiki, Y., Torres, M.A.C. & Taipe, C.L.R., 2015, Crop Classification of Upland Fields Using Random Forest of Time-Series Landsat 7 ETM+ Data, Computers and Electronics in Agriculture, 115, PP. 171-179, https://doi.org/10.1016/ j.compag.2015.05.001.

Vapnik, V.N., 1999, The Nature of Statistical Learning Theory, Springer Science & Business Media.

Vapnik, V.N., 2000, The Nature of Statistical Learning Theory, Springer Science & Business Media .

Virnodkar, S.S., Pachghare, V.K., Patil, V.C. & Jha, S.K., 2020, Application of Machine Learning on Remote Sensing Data for Sugarcane Crop Classification: A Review BT - ICT Analysis and Applications, In: Fong, S., N. Dey & A. Joshi (eds.), Springer Singapore, Singapore, PP. 539-555, https:// doi.org/10.1007/978-981-15-0630-7_55.

Wang, L., Dong, Q., Yang, L., Gao, J. & Liu, J., 2019, Crop Classification Based on a Novel Feature Filtering and Enhancement Method, Remote Sensing, 11(4), P. 455, https://doi.org/10.3390/rs11040455.

Wardlow, B.D. & Egbert, S.L., 2008, Large-Area Crop Mapping Using Time-Series MODIS 250 m NDVI Data: An Assessment for the U.S. Central Great Plains, Remote Sensing of Environment, 112, PP. 1096-1116, https://doi.org/10.1016/j.rse.2007.07.019.

Xiong, J., Thenkabail, P.S., Gumma, M.K., Teluguntla, P., Poehnelt, J., Congalton, R.G., Yadav, K. & Thau, D., 2017, Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing, ISPRS Journal of Photogrammetry and Remote Sensing, 126, PP. 225-244, https://doi.org/10.1016/ j.isprsjprs.2017.01.019.

Yan, L. & Roy, D.P., 2018, Large-Area Gap Filling of Landsat Reflectance Time Series by Spectral-Angle-Mapper Based Spatio-Temporal Similarity (SAMSTS), Remote Sensing, 10 (4), P. 609, https://doi.org/ 10.3390/rs10040609.

Zhang, J., Feng, L. & Yao, F., 2014, Improved Maize Cultivated Area Estimation over a Large Scale Combining MODIS–EVI Time Series Data and Crop Phenological Information, ISPRS Journal of Photogrammetry and Remote Sensing, 94, PP. 102-113, https://doi.org/10.1016/ j.isprsjprs.2014.04.023.

Zhong, L., Hu, L., Yu, L., Gong, P. & Biging, G. S., 2016, Automated Mapping of Soybean and Corn Using Phenology, ISPRS Journal of Photogrammetry and Remote Sensing, 119, PP. 151-164, https://doi.org/10.1016/ j.isprsjprs.2016.05.014.

آمار
تعداد مشاهده مقاله: 3,384
تعداد دریافت فایل اصل مقاله: 1,116