EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 600 (2024) E3S Web Conf., 600 (2024) 03007 References
Open Access
Issue
E3S Web Conf.
Volume 600, 2024
The 6th International Geography Seminar (IGEOS 2023)
Article Number 03007
Number of page(s) 17
Section GIS and Remote Sensing Application
DOI https://doi.org/10.1051/e3sconf/202460003007
Published online 29 November 2024
  1. G. Li et al., Crop type mapping using time-series Sentinel-2 imagery and U-Net in early growth periods in the Hetao irrigation district in China, Comput Electron Agric, vol. 203, p. 107478, (2022), doi: https://doi.org/10.1016/j.compag.2022.107478. [CrossRef] [Google Scholar]
  2. M. W. Rosegrant, Global Food Security: Challenges and Policies, Science, vol. 302, no. 5652. pp. 1917–1919, (2003). doi: 10.1126/science.1092958. [CrossRef] [PubMed] [Google Scholar]
  3. H. C. J. Godfray, Food security: The challenge of feeding 9 billion people, Science, vol. 327, no. 5967. pp. 812–818, (2010). doi: 10.1126/science.1185383. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  4. R. Ridwana, A. Al Kautsar, F. Saleh, S. Himayah, R. Arrasyid, and T. D. Pamungkas, Spatiotemporal monitoring of rice crops in the covid-19 pandemic period for local food security using sentinel 2b imagery case ctudy: tasikmalaya city, IOP Conf Ser Earth Environ Sci, vol. 1089, no. 1, (2022), doi: 10.1088/1755-1315/1089/1/012039. [CrossRef] [Google Scholar]
  5. M. A. M. Javed, A. D. Vishwakarma, S. Ansari, and V. D. Chaudhari, IOT Based Smart Agriculture System, International Journal of Innovations in Engineering and Science, vol. 6, no. 10, p. 130, (2021), doi: 10.46335/ijies.2021.6.10.27. [CrossRef] [Google Scholar]
  6. J. Kent, A. J. Russell, and F. Isaac, Method and system for agriculture, US Patent App. 16/960, 902, (2020), [Online]. Available: https://patents.google.com/patent/US20200333782A1/en [Google Scholar]
  7. G. Lemaire, Integrated crop-livestock systems: Strategies to achieve synergy between agricultural production and environmental quality, Agric Ecosyst Environ, vol. 190, pp. 4–8, (2014), doi: 10.1016/j.agee.2013.08.009. [CrossRef] [Google Scholar]
  8. U. S. Aliyu, H. Ozdeser, B. Çavuşoğlu, and M. A. M. Usman, Food security sustainability: A synthesis of the current concepts and empirical approaches for meeting SDGs, Sustainability (Switzerland), vol. 13, no. 21, (2021), doi: 10.3390/su132111728. [Google Scholar]
  9. J. Pretty, Sustainable intensification in agricultural systems, Annals of Botany, vol. 114, no. 8. pp. 1571–1596, (2014). doi: 10.1093/aob/mcu205. [CrossRef] [PubMed] [Google Scholar]
  10. H. Charles, Food security and sustainable intensification, Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 369, no. 1639, (2014), doi: 10.1098/rstb.2012.0273. [Google Scholar]
  11. U. N. D. of E. and S. Affairs, The Sustainable Development Goals Report 2019, United Nations publication issued by the Department of Economic and Social Affairs, p. 64, (2019), [Online]. Available: https://unstats.un.org/sdgs/report/2022/ https://www.unilibrary.org/content/books/9789210018098 https://www.unilibrary.org/content/books/9789210478878 [Google Scholar]
  12. S. K. Singla, Spatiotemporal analysis of LANDSAT Data for crop yield prediction, Journal of Engineering Science and Technology Review, vol. 11, no. 3, pp. 9–17, (2018), doi: 10.25103/jestr.113.02. [CrossRef] [Google Scholar]
  13. K. Jhajharia, P. Mathur, S. Jain, and S. Nijhawan, Crop Yield Prediction using Machine Learning and Deep Learning Techniques, Procedia Comput Sci, vol. 218, pp. 406–417, (2023), doi: 10.1016/j.procs.2023.01.023. [CrossRef] [Google Scholar]
  14. J. B. Fisher, The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, Water Resources Research, vol. 53, no. 4. pp. 2618–2626, (2017). doi: 10.1002/2016WR020175. [CrossRef] [Google Scholar]
  15. K. Edjame, Potential of Irrigated Crop Water Productivity Mapping to Improving Food and Water Security around Lake Bam, Northwest Burkina Faso. wascal-togo.org, (2018). [Online]. Available: https://www.wascaltogo.org/public/images/publication/Thesis_OUATTARA_MRP_CCHS.pdf [Google Scholar]
  16. O. Rozenstein, N. Haymann, G. Kaplan, and J. Tanny, Estimating cotton water consumption using a time series of Sentinel-2 imagery, Agric Water Manag, vol. 207, pp. 44–52, Aug. (2018), doi: 10.1016/j.agwat.2018.05.017. [CrossRef] [Google Scholar]
  17. K. Li et al., Dynamic Evaluation of Agricultural Drought Hazard in Northeast China Based on Coupled Multi-Source Data, Remote Sens (Basel), vol. 15, no. 1, (2023), doi: 10.3390/rs15010057. [Google Scholar]
  18. M. Kamal, H. Hartono, P. Wicaksono, N. S. Adi, and S. Arjasakusuma, Assessment of Mangrove Forest Degradation Through Canopy Fractional Cover in Karimunjawa Island, Central Java, Indonesia, Geoplanning: Journal of Geomatics and Planning, vol. 3, no. 2, p. 107, (2016), doi: 10.14710/geoplanning.3.2.107-116. [CrossRef] [Google Scholar]
  19. S. Fraval, Remote sensing and food security: Monitoring agriculture, ecosystems, hydrology, food environments, and health outcomes, Proceedings of SPIE The International Society for Optical Engineering, vol. 11527. (2020). doi: 10.1117/12.2576496. [Google Scholar]
  20. P. S. Thenkabail, Assessing future risks to agricultural productivity, water resources and food security: How can remote sensing help?, Photogramm Eng Remote Sensing, vol. 78, no. 8, pp. 773–782, (2012), [Online]. Available: https://api.elsevier.com/content/abstract/scopus_id/84868009667 [Google Scholar]
  21. J. K. Gilbertson and A. van Niekerk, Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning, Comput Electron Agric, vol. 142, pp. 50–58, (2017), doi: https://doi.org/10.1016/j.compag.2017.08.024. [CrossRef] [Google Scholar]
  22. H. Li, C. Zhang, S. Zhang, and P. M. Atkinson, Full year crop monitoring and separability assessment with fully-polarimetric L-band UAVSAR: A case study in the Sacramento Valley, California, International Journal of Applied Earth Observation and Geoinformation, vol. 74, pp. 45–56, (2019), doi: https://doi.org/10.1016/j.jag.2018.08.024. [CrossRef] [Google Scholar]
  23. L. Xun, J. Zhang, D. Cao, S. Zhang, and F. Yao, Crop Area Identification Based on Time Series EVI2 and Sparse Representation Approach: A Case Study in Shandong Province, China, IEEE Access, vol. 7, pp. 157513–157523, (2019), doi: 10.1109/ACCESS.2019.2949799. [CrossRef] [Google Scholar]
  24. L. Zhong, L. Hu, and H. Zhou, Deep learning based multi-temporal crop classification, Remote Sens Environ, vol. 221, pp. 430–443, (2019), doi: https://doi.org/10.1016/j.rse.2018.11.032. [CrossRef] [Google Scholar]
  25. L. Zhong, L. Hu, H. Zhou, and X. Tao, Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US, Remote Sens Environ, vol. 233, p. 111411, (2019), doi: https://doi.org/10.1016/j.rse.2019.111411. [CrossRef] [Google Scholar]
  26. R. Ridwana and S. Himayah, Utilization of Remote Sensing Technology and Geographic Information Systems for Tourism Development, International Journal of Applied Sciences in Tourism and Events, vol. 4, no. 2, pp. 158–169, (2020), doi: 10.31940/ijaste.v4i2.2042. [CrossRef] [Google Scholar]
  27. S. Khanal, K. C. Kushal, J. P. Fulton, S. Shearer, and E. Ozkan, Remote sensing in agriculture—accomplishments, limitations, and opportunities, Remote Sens (Basel), vol. 12, no. 22, pp. 1–29, (2020), doi: 10.3390/rs12223783. [Google Scholar]
  28. R. P. Sishodia, R. L. Ray, and S. K. Singh, Applications of remote sensing in precision agriculture: A review (Indices vegetativos utilizados na agricultura), Remote Sens (Basel), vol. 12, no. 19, pp. 1–31, (2020). [Google Scholar]
  29. M. Wójtowicz, A. Wójtowicz, and J. Piekarczyk, Application of remote sensing methods in agriculture, Communications in Biometry and Crop Science, vol. 11, no. 1, pp. 31–50, (2016). [Google Scholar]
  30. J. M. Peña-Barragán, M. K. Ngugi, R. E. Plant, and J. Six, Object-based crop identification using multiple vegetation indices, textural features and crop phenology, Remote Sens Environ, vol. 115, no. 6, pp. 1301–1316, Jun. (2011), doi: 10.1016/j.rse.2011.01.009. [CrossRef] [Google Scholar]
  31. I. Kyere, T. Astor, R. Graß, and M. Wachendorf, Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data, Comput Electron Agric, vol. 179, p. 105864, (2020), doi: https://doi.org/10.1016/j.compag.2020.105864. [CrossRef] [Google Scholar]
  32. A. Nemmaoui, M. A. Aguilar, F. J. Aguilar, A. Novelli, and ..., Greenhouse crop identification from multi-temporal multi-sensor satellite imagery using object-based approach: A case study from Almería (Spain), Remote Sens (Basel), (2018), [Online]. Available: https://www.mdpi.com/362018 [Google Scholar]
  33. M. Kamal, S. Phinn, and K. Johansen, Assessment of multi-resolution image data for mangrove leaf area index mapping, Remote Sens Environ, vol. 176, pp. 242–254, (2016), doi: 10.1016/j.rse.2016.02.013. [CrossRef] [Google Scholar]
  34. R. Ridwana, D. Sugandi, R. Arrasyid, S. Himayah, and T. D. Pamungkas, Multitemporal landsat image utilization for spatial prediction of built up area in tasikmalaya city, indonesia, IOP Conf Ser Earth Environ Sci, vol. 683, no. 1, (2021), doi: 10.1088/17551315/683/1/012101. [CrossRef] [Google Scholar]
  35. R. Ridwana et al., Monitoring Aerosol Optical Depth for Air Quality Through Himawari-8 in Urban Area West Java Province Indonesia, vol. 15, no. 2, (2023), doi: 10.24114/jg.v15i2.36866. [Google Scholar]
  36. Q. HU et al., How do temporal and spectral features matter in crop classification in Heilongjiang Province, China?, J Integr Agric, vol. 16, no. 2, pp. 324–336, (2017), doi: https://doi.org/10.1016/S2095-3119(15)61321-1. [CrossRef] [Google Scholar]
  37. L. H. Samberg, J. S. Gerber, N. Ramankutty, M. Herrero, and P. C. West, Subnational distribution of average farm size and smallholder contributions to global food production, Environmental Research Letters, vol. 11, no. 12, Nov. (2016), doi: 10.1088/1748-9326/11/12/124010. [CrossRef] [Google Scholar]
  38. L. Jia, Comparative study on crop recognition of LandSat-OLI and RapidEye data, 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017. (2017). doi: 10.1109/Agro-Geoinformatics.2017.8047024. [Google Scholar]
  39. F. Arias, M. Zambrano, K. Broce, C. Medina, H. Pacheco, and Y. Nunez, Hyperspectral imaging for rice cultivation: Applications, methods and challenges, AIMS Agriculture and Food, vol. 6, no. 1, pp. 273–307, (2020), doi: 10.3934/AGRFOOD.2021018. [CrossRef] [Google Scholar]
  40. X. Wang, Crops identification in Kaikong River Basin of Xinjiang based on time series Landsat remote sensing images, Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 35, no. 16, pp. 180–188, (2019), doi: 10.11975/j.issn.1002-6819.2019.16.020. [Google Scholar]
  41. Z. Yang, Y. Shao, K. Li, Q. Liu, L. Liu, and B. Brisco, An improved scheme for rice phenology estimation based on time-series multispectral HJ-1A/B and polarimetric RADARSAT-2 data, Remote Sens Environ, vol. 195, pp. 184–201, (2017), doi: https://doi.org/10.1016/j.rse.2017.04.016. [CrossRef] [Google Scholar]
  42. X. Huang, J. Wang, J. Shang, C. Liao, and J. Liu, Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal Cband polarimetric RADARSAT-2 imagery, Remote Sens Environ, vol. 193, pp. 11–28, (2017), doi: https://doi.org/10.1016/j.rse.2017.02.014. [CrossRef] [Google Scholar]
  43. G. Franceschetti, Synthetic aperture radar processing. (2018). doi: 10.1201/9780203737484. [CrossRef] [Google Scholar]
  44. D. S. Ladue, P. L. Heinselman, and J. F. Newman, Strengths and Limitations of Current RADAR Systems for Two Stakeholder Groups in the Southern Plains, doi: 10.1175/2009BAMS2830.2. [Google Scholar]
  45. A. Veloso, Understanding the temporal behavior of crops using Sentinel-1 and Sentinel2-like data for agricultural applications, Remote Sens Environ, vol. 199, pp. 415–426, (2017), doi: 10.1016/j.rse.2017.07.015. [CrossRef] [Google Scholar]
  46. O. Csillik, M. Belgiu, G. P. Asner, and M. Kelly, Object-based time-constrained dynamic time warping classification of crops using Sentinel-2, Remote Sens (Basel), (2019), [Online]. Available: https://www.mdpi.com/469590 [Google Scholar]
  47. Z. Wang, H. Zhang, W. He, and L. Zhang, Cross-phenological-region crop mapping framework using Sentinel-2 time series Imagery: A new perspective for winter crops in China, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 193, pp. 200–215, (2022), doi: https://doi.org/10.1016/j.isprsjprs.2022.09.010. [CrossRef] [Google Scholar]
  48. X. Wu, X. Xiao, Z. Yang, J. Wang, J. Steiner, and R. Bajgain, Spatial-temporal dynamics of maize and soybean planted area, harvested area, gross primary production, and grain production in the Contiguous United States during 2008-2018, Agric For Meteorol, vol. 297, p. 108240, (2021), doi: https://doi.org/10.1016/j.agrformet.2020.108240. [CrossRef] [Google Scholar]
  49. M. G. Maponya, A. van Niekerk, and Z. E. Mashimbye, Pre-harvest classification of crop types using a Sentinel-2 time-series and machine learning, Comput Electron Agric, vol. 169, Feb. (2020), doi: 10.1016/j.compag.2019.105164. [CrossRef] [Google Scholar]
  50. J. Dong, Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine, Remote Sens Environ, vol. 185, pp. 142–154, (2016), doi: 10.1016/j.rse.2016.02.016. [CrossRef] [PubMed] [Google Scholar]
  51. B. D. Wardlow and S. L. Egbert, Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains, Remote Sens Environ, vol. 112, no. 3, pp. 1096–1116, (2008), doi: https://doi.org/10.1016/j.rse.2007.07.019. [CrossRef] [Google Scholar]
  52. Y. Chen et al., Mapping croplands, cropping patterns, and crop types using MODIS time-series data, International Journal of Applied Earth Observation and Geoinformation, vol. 69, pp. 133–147, (2018), doi: https://doi.org/10.1016/j.jag.2018.03.005. [CrossRef] [Google Scholar]
  53. G. Myburgh and A. Van Niekerk, Effect of Feature Dimensionality on Object-based Land Cover Classification: A Comparison of Three Classifiers, South African Journal of Geomatics, vol. 2, no. 1, pp. 13–27, (2013), [Online]. Available: http://www.sajg.org.za/index.php/sajg/article/view/64 [Google Scholar]
  54. P. S. Thenkabail, Global Cropland-Extent Product at 30-m Resolution (GCEP30) Derived from Landsat Satellite Time-Series Data for the Year 2015 Using Multiple Machine-Learning Algorithms on Google Earth Engine Cloud, US Geological Survey Professional Paper, vol. 2021, no. 1868, pp. 1–63, (2021), doi: 10.3133/pp1868. [Google Scholar]
  55. R. M. A. Latif, J. He, and M. Umer, Mapping Cropland Extent in Pakistan Using Machine Learning Algorithms on Google Earth Engine Cloud Computing Framework, ISPRS Int J Geoinf, vol. 12, no. 2, (2023), doi: 10.3390/ijgi12020081. [Google Scholar]
  56. J. Yao, J. Wu, C. Xiao, Z. Zhang, and J. Li, The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine, Remote Sens (Basel), vol. 14, no. 12, p. 2758, Jun. (2022), doi: 10.3390/rs14122758. [CrossRef] [Google Scholar]
  57. A. Htitiou, A. Boudhar, A. Chehbouni, and T. Benabdelouahab, National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine, Remote Sens (Basel), vol. 13, no. 21, p. 4378, Oct. (2021), doi: 10.3390/rs13214378. [CrossRef] [Google Scholar]
  58. J. K. Gilbertson, Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques, Comput Electron Agric, vol. 134, pp. 151–159, (2017), doi: 10.1016/j.compag.2016.12.006. [CrossRef] [Google Scholar]
  59. C. Dang, Y. Liu, H. Yue, J. X. Qian, and R. Zhu, Autumn Crop Yield Prediction using Data-Driven Approaches:Support Vector Machines, Random Forest, and Deep Neural Network Methods, Canadian Journal of Remote Sensing, vol. 47, no. 2, pp. 162–181, (2021), doi: 10.1080/07038992.2020.1833186. [CrossRef] [Google Scholar]
  60. R. Li, Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model, Soil Tillage Res, vol. 206, (2021), doi: 10.1016/j.still.2020.104838. [PubMed] [Google Scholar]
  61. Y. Liu, D. Xiao, and W. Yang, An algorithm for early rice area mapping from satellite remote sensing data in southwestern Guangdong in China based on feature optimization and random Forest, Ecol Inform, vol. 72, p. 101853, (2022), doi: https://doi.org/10.1016/j.ecoinf.2022.101853. [CrossRef] [Google Scholar]
  62. K. S. Hariyanti, T. June, Y. Koesmaryono, R. Hidayat, and A. Pramudia, Penentuan Waktu Tanam dan Kebutuhan Air Tanaman Padi, Jagung, Kedelai dan Bawang Merah di Provinsi Jawa Barat dan Nusa Tenggara Timur, Jurnal Tanah dan Iklim, vol. 43, no. 1, pp. 83–92, (2019). [Google Scholar]
  63. X. Wang et al., Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region, (2022). [Google Scholar]
  64. D. M. W. Powers, Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, pp. 37–63, (2020). [Google Scholar]
  65. D. Lewis, S. Phinn, and L. Arroyo, Cost-effectiveness of seven approaches to map vegetation communities A case study from Northern Australia’s tropical savannas, Remote Sens (Basel), vol. 5, no. 1, pp. 377–414, (2013), doi: 10.3390/rs5010377. [CrossRef] [Google Scholar]
  66. D. Lewis, S. Phinn, and L. Arroyo, Cost-effectiveness of seven approaches to map vegetation communities A case study from Northern Australia’s tropical savannas, Remote Sens (Basel), vol. 5, no. 1, pp. 377–414, (2013), doi: 10.3390/rs5010377. [CrossRef] [Google Scholar]
  67. A. N. L. Winter, Evaluation and Comparison of Random Forest and Wheat Identification, (2019). [Google Scholar]
  68. M. Belgiu, Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114. pp. 24–31, (2016). doi: 10.1016/j.isprsjprs.2016.01.011. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.