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Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping

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Data Science in Agriculture and Natural Resource Management

Part of the book series: Studies in Big Data ((SBD,volume 96))

Abstract

Crop monitoring becomes essential in attaining food security for implementation of various agricultural serving programs. So, fast and reliable crop monitoring is must. Using traditional methods, crop monitoring maps need high amount of satellite data downloading and processing time. Google Earth Engine (GEE) cloud platform enables us to save time in downloading and processing of time series satellite data, the every satellite imagery is converted into Normalized Difference Vegetation Index (NDVI) image and stacked monthly wise maximum images. The stacked image was used for conducting supervised classification. The main objective of this study is to evaluate the performance of different supervised machine learning (ML) classifiers in GEE platform and Spectral Matching Technique (SMT) using Sentinel-2 10 m satellite imagery in specific crop type classification. The crop classification for the year 2018–19 (rabi season) was carried for Jhansi District using supervised classifiers like Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Trees (CART) in GEE platform and also with SMT with the help of ground data. It was attained nearly 81.8% accuracy for RF, 68.8% for SVM, 64.9% for CART and 88% for SMT. The results obtained using RF classifier were nearly relative to SMT classification map. The study indicates that classifier’s performance depends on the quality of ground data used, RF can reduce the error samples in ground samples and produce satisfactory results. This study compared results obtained from all the above classifiers with agricultural statistics and also compared crop-wise accuracies. In the study, it was observed that RF classification is outperformed when compared with other classifiers considered in the study.

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Acknowledgements

This research was supported by the RIICE III project funded by the Swiss Agency for Development Cooperation (SDC), the Government of Uttar Pradesh, India, through its ‘Doubling Farmers Income’ in Bundelkhand, and the CGIAR Research Program on Water, Land and Ecosystems (WLE). The authors are grateful to Divya Kashyap, Arindom Baidya, and other team members for their suggestions and recommendations. We also thank Jameeruddin A and Ismail for their support during ground data collection.

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Authors and Affiliations

  1. Geospatial Sciences and Big Data, Global Research Program on Resilient Farm and Food Systems, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 502 324, Patancheru, India

    Pranay Panjala & Murali Krishna Gumma

  2. Western Geographic Science Center, U.S. Geological Survey (USGS), AZ, 86001, Flagstaff, USA

    Pardhasaradhi Teluguntla

  3. Bay Area Environmental Research Institute (BAERI), NASA Research Park, Moffett Field, San Jose, CA, 94035, USA

    Pardhasaradhi Teluguntla

Authors
  1. Pranay Panjala
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  2. Murali Krishna Gumma
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  3. Pardhasaradhi Teluguntla
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Corresponding author

Correspondence to Murali Krishna Gumma .

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Editors and Affiliations

  1. Division of Remote Sensing Applications, ICAR-National Bureau of Soil Survey and Land Use Planning, Nagpur, India

    G. P. Obi Reddy

  2. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India

    Mehul S. Raval

  3. Centre of Studies in Resources Engineer, Indian Institute of Technology Bombay (IITB), Mumbai, India

    J. Adinarayana

  4. School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India

    Sanjay Chaudhary

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Panjala, P., Gumma, M.K., Teluguntla, P. (2022). Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping. In: Reddy, G.P.O., Raval, M.S., Adinarayana, J., Chaudhary, S. (eds) Data Science in Agriculture and Natural Resource Management. Studies in Big Data, vol 96. Springer, Singapore. https://doi.org/10.1007/978-981-16-5847-1_8

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