IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4728-d541849.html
   My bibliography  Save this article

Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

Author

Listed:
  • Zinhle Mashaba-Munghemezulu

    (Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
    Geoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South Africa)

  • George Johannes Chirima

    (Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
    Geoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South Africa)

  • Cilence Munghemezulu

    (Geoinformation Science Division, Agricultural Research Council Institute for Soil, Climate and Water, Pretoria 0001, South Africa)

Abstract

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

Suggested Citation

  • Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4728-:d:541849
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4728/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/9/4728/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ghali Abdullahi Abubakar & Ke Wang & AmirReza Shahtahamssebi & Xingyu Xue & Marye Belete & Adam Juma Abdallah Gudo & Kamal Abdelrahim Mohamed Shuka & Muye Gan, 2020. "Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    2. Romy Santpoort, 2020. "The Drivers of Maize Area Expansion in Sub-Saharan Africa. How Policies to Boost Maize Production Overlook the Interests of Smallholder Farmers," Land, MDPI, vol. 9(3), pages 1-13, February.
    3. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    4. Giller, Ken E. & Rowe, Ed C. & de Ridder, Nico & van Keulen, Herman, 2006. "Resource use dynamics and interactions in the tropics: Scaling up in space and time," Agricultural Systems, Elsevier, vol. 88(1), pages 8-27, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uttam Bandyopadhyay & Atanu Biswas & Shirsendu Mukherjee, 2009. "Adaptive two-treatment two-period crossover design for binary treatment responses incorporating carry-over effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 13-33, March.
    2. repec:osf:socarx:783rw_v1 is not listed on IDEAS
    3. Nyakudya, Innocent Wadzanayi & Stroosnijder, Leo & Nyagumbo, Isaiah, 2014. "Infiltration and planting pits for improved water management and maize yield in semi-arid Zimbabwe," Agricultural Water Management, Elsevier, vol. 141(C), pages 30-46.
    4. repec:ags:ijag24:346816 is not listed on IDEAS
    5. Bridget. B. Umar & Jens. B. Aune & Fred. H. Johnsen & Obed. I. Lungu, 2011. "Options for Improving Smallholder Conservation Agriculture in Zambia," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 3(3), pages 1-50, September.
    6. Preety Srivastava & Xueyan Zhao, 2010. "What Do the Bingers Drink? Micro‐Unit Evidence on Negative Externalities and Drinker Characteristics of Alcohol Consumption by Beverage Types," Economic Papers, The Economic Society of Australia, vol. 29(2), pages 229-250, June.
    7. Holger Schwender & Margaret A. Taub & Terri H. Beaty & Mary L. Marazita & Ingo Ruczinski, 2012. "Rapid Testing of SNPs and Gene–Environment Interactions in Case–Parent Trio Data Based on Exact Analytic Parameter Estimation," Biometrics, The International Biometric Society, vol. 68(3), pages 766-773, September.
    8. Matysková, Ludmila & Rogers, Brian & Steiner, Jakub & Sun, Keh-Kuan, 2020. "Habits as adaptations: An experimental study," Games and Economic Behavior, Elsevier, vol. 122(C), pages 391-406.
    9. André, Kévin, 2013. "Applying the Capability Approach to the French Education System: An Assessment of the "Pourquoi pas moi ?"," ESSEC Working Papers WP1316, ESSEC Research Center, ESSEC Business School.
    10. Irina Zrnić Novaković & Dean Ajduković & Helena Bakić & Camila Borges & Margarida Figueiredo-Braga & Annett Lotzin & Xenia Anastassiou-Hadjicharalambous & Chrysanthi Lioupi & Jana Darejan Javakhishvil, 2023. "Shaped by the COVID-19 pandemic: Psychological responses from a subjective perspective–A longitudinal mixed-methods study across five European countries," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-32, April.
    11. Melo, Grace & Palma, Marco & Chomali, Laura & Ribera, Luis, 2025. "Are experts overoptimistic about the success of market labeling information?," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 360812, Agricultural and Applied Economics Association.
    12. Ruiz-Frau, A. & Krause, T. & Marbà, N., 2018. "The use of sociocultural valuation in sustainable environmental management," Ecosystem Services, Elsevier, vol. 29(PA), pages 158-167.
    13. Berrueta, Cecilia & Giménez, Gustavo & Dogliotti, Santiago, 2021. "Scaling up from crop to farm level: Co-innovation framework to improve vegetable farm systems sustainability," Agricultural Systems, Elsevier, vol. 189(C).
    14. Rufino, M.C. & Dury, J. & Tittonell, P. & van Wijk, M.T. & Herrero, M. & Zingore, S. & Mapfumo, P. & Giller, K.E., 2011. "Competing use of organic resources, village-level interactions between farm types and climate variability in a communal area of NE Zimbabwe," Agricultural Systems, Elsevier, vol. 104(2), pages 175-190, February.
    15. Yexin Tian & Shuo Xu & Yuchen Cao & Zhongyan Wang & Zijing Wei, 2025. "An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection," Mathematics, MDPI, vol. 13(13), pages 1-24, June.
    16. AlMalki, Hameeda A. & Durugbo, Christopher M., 2023. "Evaluating critical institutional factors of Industry 4.0 for education reform," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    17. Guevara, C. Angelo & Fukushi, Mitsuyoshi, 2016. "Modeling the decoy effect with context-RUM Models: Diagrammatic analysis and empirical evidence from route choice SP and mode choice RP case studies," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 318-337.
    18. Giller, Ken E. & Andersson, Jens & Delaune, Thomas & Silva, João Vasco & Descheemaeker, Katrien & van de Ven, Gerrie & Schut, Antonius G.T. & van Wijk, Mark & Hammond, Jim & Hochman, Zvi & Taulya, God, "undated". "IFAD Research Series 83: The future of farming: who will produce our food?," IFAD Research Series 322005, International Fund for Agricultural Development (IFAD).
    19. repec:ags:aaea22:343870 is not listed on IDEAS
    20. Alexandra I. Khalyasmaa & Pavel V. Matrenin & Stanislav A. Eroshenko & Vadim Z. Manusov & Andrey M. Bramm & Alexey M. Romanov, 2022. "Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    21. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    22. Alireza Taheri Dehkordi & Mohammad Javad Valadan Zoej & Hani Ghasemi & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    23. Lahtinen, Tuomas J. & Hämäläinen, Raimo P., 2016. "Path dependence and biases in the even swaps decision analysis method," European Journal of Operational Research, Elsevier, vol. 249(3), pages 890-898.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4728-:d:541849. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.