Remote sensing and simulation: A novel approach to rice yield estimation in the Cauvery delta

S Manikandan; S  Pazhanivelan; K P Ragunath; R Kumaraperumal; A P Sivamurugan; M Raju; N S Sudarmanian; S Satheesh

doi:10.14719/pst.7442

Authors

S Manikandan Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0009-0009-9621-5543
S Pazhanivelan entre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0000-0002-3596-3232
K P Ragunath entre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0000-0002-7851-4437
R Kumaraperumal Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0000-0001-6659-9587
A P Sivamurugan Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0000-0002-3567-6698
M Raju Cotton Research Station, Tamil Nadu Agricultural University, Srivilliputhur, Virudhunagar 626 125, India https://orcid.org/0000-0001-9026-5106
N S Sudarmanian Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0000-0003-4327-3589
S Satheesh Department of Remote Sensing and GIS, Tamil Nadu Agricultural University, Coimbatore 641 003, India https://orcid.org/0009-0008-5139-1036

DOI:

https://doi.org/10.14719/pst.7442

Keywords:

crop monitoring, food security, remote sensing, rice, yield

Abstract

This study explores the use of the C-band Synthetic Aperture Radar (SAR) dataset from Sentinel-1A for crop area delineation and its integration with the DSSAT crop simulation model for spatial yield estimation for rice crop in the Cauvery Delta region of Tamil Nadu, India. With the global population increasing at a rapid rate, precision agriculture is critical for addressing food security challenges. The near-real-time monitoring capabilities of remote sensing techniques, especially microwave datasets made all season crop monitoring possible. The DSSAT model has demonstrated its capability to simulate yields under varying climatic and management scenarios. This approach offers timely and scalable solutions for monitoring crop health and forecasting yields, which are critical for mitigating the impact of climate change on agriculture. The estimated rice area using the Parameterized classification technique was 118104 ha in the Thanjavur district and 102138 ha in the Thiruvarur district, with an accuracy of 86 %. Upon validation against crop-cutting experiments, the DSSAT approach achieved average RMSE values of 440?kg?ha-1 and 450?kg?ha-1, along with yield agreements of 90 % and 89 % in Thanjavur and Thiruvarur respectively. These quantitative results highlight the enhanced precision of the integrated remote sensing and simulation framework for rice yield estimation, offering a robust tool for precision agriculture and improved decision-making under variable climatic conditions.

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