Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)

  • Avinash Kumar Ranjan
  • Bikash Ranjan ParidaEmail author


Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3–77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108–126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and accuracy assessment of LULC maps revealed that the SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.


Acreage mapping Yield estimation Random Forest classifier SAR data 



This research was supported by the Science and Engineering Research Board (SERB), Department of Science & Technology (DST) project grant no. YSS/2015/000801. Authors thanks to USGS and ASF for providing Sentinel-2B and Sentinel-1A satellite data. Authors also thank to anonymous reviewers for their constructive comment and suggestions.

Authors’ contributions

Conceived, designed research, analyzed data, and wrote the manuscript: AKR and BRP.

Compilance with ethical standards

Conflict of interest

Authors declare no potential conflict of interest.

Supplementary material

41324_2019_246_MOESM1_ESM.pdf (772 kb)
Supplementary material 1 (PDF 771 kb)


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Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.Department of Land Resource Management, School of Natural Resource ManagementCentral University of JharkhandRanchiIndia

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