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Cotton cultivated area detection and yield monitoring combining remote sensing with field data in lower Indus River basin, Pakistan

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Abstract

As the Earth’s population continuously increase with the passage of time, the demand for agricultural raw material for human need increases. It is critical to maintaining updated and accurate information about the dynamics and properties of the world agricultural systems. As cash crop, the updated information of the spatial distribution of cotton field is necessary to monitor the crop area and growth changes at regional level. We used 8-day enhanced vegetation index (EVI) time series to detect cotton crop area and binomial probabilistic approach to obtain the probability distribution of cotton crop occurrence. We used Gaussian kriging to derive cotton yield inside the detected cotton crop areas through crop reporting data. We also used field data from farmers to validate the cotton yield results. A strong correlation between the MODIS-derived cotton cultivated area and statistical data at the tehsil level were achieved (R2 = 0.84) for all study years (2004–2019). The total accuracy for the cotton crop area detection was 84.6% and yield prediction was 92.1%. Our study presents new approaches to map cotton area and yield, which are applicable to other regions through machine learning.

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Data availability

The datasets used in this study during the analysis are available from the corresponding author on the reasonable request.

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Acknowledgements

We are very thankful to Mr. Muhammad Mateen Tahir who helped us to collect the field data for this study during the pandemic period; it was really hard work. The crop data that is used in this study was obtained from crop reporting service of Statistical Bureau of Pakistan (SBP) Islamabad, Pakistan.

Funding

This work was completed with supported by the National Key Research and Development Program of China, grant number 2017YFA0604403-3 and 2016YFA0602301, the Joint Fund of National Natural Science Foundation of China, grant number U19A2023, the National Natural Science Foundation of China (41971124), and the Natural Science Foundation of Jilin Scientific Institute of China (YDZJ202201ZYTS470).

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Authors and Affiliations

  1. Key Laboratory of Geographical Processes and Ecological Security in Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun, 130024, China

    Muhammad Naveed, Shengwei Zong, Haibo Du, Hang Sun & Shuai Chang

  2. School of Natural Resources, University of Missouri, Columbia, MO, 65211, USA

    Hong S. He

  3. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ministry of Education, School of Geographical Sciences, 818 South Beijing Road, Urumqi, Xinjiang, People’s Republic of China

    Zulqarnain Satti

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  1. Muhammad Naveed
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  2. Hong S. He
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  3. Shengwei Zong
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  4. Haibo Du
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  5. Zulqarnain Satti
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  6. Hang Sun
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  7. Shuai Chang
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Contributions

All authors contributed intellectual input and assistance to this study and manuscript preparation. M. Naveed and Hong S. He developed the original idea. M. Naveed, S Zong, and H. Du designed the methodology. M. Naveed, Z. Satti, and Shuai C. investigate the data collection. M. Naveed and Hang Sun perform statistical and spatial analysis. M. Naveed and S Zong: writing — original draft. Hong S. He supervised this work. All authors read and approved the final manuscript.

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Correspondence to Hong S. He or Shengwei Zong.

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Naveed, M., He, H.S., Zong, S. et al. Cotton cultivated area detection and yield monitoring combining remote sensing with field data in lower Indus River basin, Pakistan. Environ Monit Assess 195, 401 (2023). https://doi.org/10.1007/s10661-023-11004-3

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