Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India

Srikanth Rupavatharam; Pranuthi Gogumalla; Jameeruddin Shaik; Suman Saraswathibatla; Mukund Patil

doi:doi:10.11648/j.ajrs.20251302.11

Research Article |

| Peer-Reviewed

Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India

Srikanth Rupavatharam^*

, Pranuthi Gogumalla

, Jameeruddin Shaik

, Suman Saraswathibatla, Mukund Patil

Published in American Journal of Remote Sensing (Volume 13, Issue 2)

Received: 8 September 2025 Accepted: 23 September 2025 Published: 30 October 2025

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Abstract

Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.

Published in	American Journal of Remote Sensing (Volume 13, Issue 2)
DOI	10.11648/j.ajrs.20251302.11
Page(s)	48-72
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Precision Agriculture, Crop Yield Estimation, Sentinel 1 & 2 Satellite Data, Google Earth Engine, Machine Learning, Remote Sensing

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Rupavatharam, S., Gogumalla, P., Shaik, J., Saraswathibatla, S., Patil, M. (2025). Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India. American Journal of Remote Sensing, 13(2), 48-72. https://doi.org/10.11648/j.ajrs.20251302.11

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Rupavatharam, S.; Gogumalla, P.; Shaik, J.; Saraswathibatla, S.; Patil, M. Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India. Am. J. Remote Sens. 2025, 13(2), 48-72. doi: 10.11648/j.ajrs.20251302.11

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Rupavatharam S, Gogumalla P, Shaik J, Saraswathibatla S, Patil M. Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India. Am J Remote Sens. 2025;13(2):48-72. doi: 10.11648/j.ajrs.20251302.11

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@article{10.11648/j.ajrs.20251302.11,
  author = {Srikanth Rupavatharam and Pranuthi Gogumalla and Jameeruddin Shaik and Suman Saraswathibatla and Mukund Patil},
  title = {Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India
},
  journal = {American Journal of Remote Sensing},
  volume = {13},
  number = {2},
  pages = {48-72},
  doi = {10.11648/j.ajrs.20251302.11},
  url = {https://doi.org/10.11648/j.ajrs.20251302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20251302.11},
  abstract = {Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
},
 year = {2025}
}

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TY  - JOUR
T1  - Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India

AU  - Srikanth Rupavatharam
AU  - Pranuthi Gogumalla
AU  - Jameeruddin Shaik
AU  - Suman Saraswathibatla
AU  - Mukund Patil
Y1  - 2025/10/30
PY  - 2025
N1  - https://doi.org/10.11648/j.ajrs.20251302.11
DO  - 10.11648/j.ajrs.20251302.11
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 48
EP  - 72
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20251302.11
AB  - Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.

VL  - 13
IS  - 2
ER  -

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Author Information

Srikanth Rupavatharam

Digital Agriculture, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India

Contact Email

http://orcid.org/0000-0001-9142-0876
Pranuthi Gogumalla

Digital Agriculture, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India

Contact Email

http://orcid.org/0000-0003-3533-7423
Jameeruddin Shaik

Digital Agriculture, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India

Contact Email

http://orcid.org/0000-0003-4591-0019
Suman Saraswathibatla

Dr. Reddy’s Foundation, Hyderabad, India

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Mukund Patil

Digital Agriculture, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India

Contact Email

http://orcid.org/0000-0002-7197-8861

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APA Style

Rupavatharam, S., Gogumalla, P., Shaik, J., Saraswathibatla, S., Patil, M. (2025). Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India. American Journal of Remote Sensing, 13(2), 48-72. https://doi.org/10.11648/j.ajrs.20251302.11

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ACS Style

Rupavatharam, S.; Gogumalla, P.; Shaik, J.; Saraswathibatla, S.; Patil, M. Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India. Am. J. Remote Sens. 2025, 13(2), 48-72. doi: 10.11648/j.ajrs.20251302.11

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AMA Style

Rupavatharam S, Gogumalla P, Shaik J, Saraswathibatla S, Patil M. Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India. Am J Remote Sens. 2025;13(2):48-72. doi: 10.11648/j.ajrs.20251302.11

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@article{10.11648/j.ajrs.20251302.11,
  author = {Srikanth Rupavatharam and Pranuthi Gogumalla and Jameeruddin Shaik and Suman Saraswathibatla and Mukund Patil},
  title = {Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India
},
  journal = {American Journal of Remote Sensing},
  volume = {13},
  number = {2},
  pages = {48-72},
  doi = {10.11648/j.ajrs.20251302.11},
  url = {https://doi.org/10.11648/j.ajrs.20251302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20251302.11},
  abstract = {Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Field-Scale Monitoring of Rice Crop Using Open-Source Satellite Data and Digital Platforms - A Case Study of Samastipur District, Bihar, India

AU  - Srikanth Rupavatharam
AU  - Pranuthi Gogumalla
AU  - Jameeruddin Shaik
AU  - Suman Saraswathibatla
AU  - Mukund Patil
Y1  - 2025/10/30
PY  - 2025
N1  - https://doi.org/10.11648/j.ajrs.20251302.11
DO  - 10.11648/j.ajrs.20251302.11
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 48
EP  - 72
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20251302.11
AB  - Crop monitoring over large areas with high accuracy is of great significance in precision agriculture. The study is an attempt to assess the potential of open-source high-resolution satellite datasets and open-source digital platforms in combination with AI/ML algorithms for near-real-time crop monitoring and yield estimation at the farm level. In this research, we used Sentinel-1 and Sentinel-2 datasets, by processing them on the Google Earth Engine platform and developed several crop-based indicators to assess crop phenology as well as the distinction between a well-managed field (demo plots) vs a normal farmers' practice-managed crop (control plots) using Sentinel-1 satellite data. Further, crop yields were estimated before the harvesting of the crop by using Sentinel-1 and Sentinel-2 data with machine learning algorithms. The findings demonstrate that the effect of an improved package of practices on rice was significantly different from the farmer's practice. Among the statistical yield models developed for yield estimation, the gradient tree boosting model performed better than other models. This study proposes a novel method of near-real-time remote crop monitoring right from sowing to harvest time to estimate crop yields with an accuracy of 77 percent. There is potential in using open-source satellite data for monitoring farm fields in the future.

VL  - 13
IS  - 2
ER  -

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