Nukala, R M and Panjala, P and Mahammood, V and Gumma, M K (2025) Strategic Ground Data Planning for Efficient Crop Classification Using Remote Sensing and Mobile-Based Survey Tools. Geographies, 5 (4). ISSN 2673-7086
Full text not available from this repository. (Request a copy)Abstract
Reliable and representative ground data is fundamental for accurate crop classification using satellite imagery. This study demonstrates a structured approach to ground truth planning in the Bareilly district, Uttar Pradesh, where wheat is the dominant crop. Pre-season spectral clustering of Sentinel-2 Level-2A NDVI time-series data (November–March) was applied to identify ten spectrally distinct zones across the district, capturing phenological and land cover variability. These clusters were used at the village level to guide spatially stratified and optimized field sampling, ensuring coverage of heterogeneous and agriculturally significant areas. A total of 197 ground truth points were collected using the iCrops mobile application, enabling standardized and photo-validated data collection with offline functionality. The collected ground observations formed the basis for random forest supervised classification, enabling clear differentiation between major land use and land cover (LULC) classes with an overall accuracy of 91.6% and a Kappa coefficient of 0.886. The findings highlight that systematic ground data collection significantly enhances the reliability of remote sensing-based crop mapping. The outputs serve as a valuable resource for agricultural planners, policymakers, and local stakeholders by supporting crop monitoring, land use planning, and informed decision-making in the context of sustainable agricultural development.
| Item Type: | Article |
|---|---|
| Divisions: | Global Research Program - Resilient Farm and Food Systems |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | ground data, crop classification, iCrops, mobile application |
| Subjects: | Others > Remote Sensing Others > Data & Analytics |
| Depositing User: | Mr Nagaraju T |
| Date Deposited: | 24 Feb 2026 04:00 |
| Last Modified: | 24 Feb 2026 04:00 |
| URI: | http://oar.icrisat.org/id/eprint/13495 |
| Official URL: | https://www.mdpi.com/2673-7086/5/4/59 |
| Projects: | UNSPECIFIED |
| Funders: | UNSPECIFIED |
| Acknowledgement: | The authors would like to thank the International Crops Research Institute for The Semi-Arid Tropics (ICRISAT), and Andhra University for providing support to conduct research. We are grateful for the help of the field and the staff of ICRISAT for assisting with ground data collection. |
| Links: |
Actions (login required)
 |
View Item |
Altmetric
Altmetric