Strategic Ground Data Planning for Efficient Crop Classification Using Remote Sensing and Mobile-Based Survey Tools

Ramavenkata Mahesh Nukala, Pranay Panjala, Vazeer Mahammood and Murali Krishna Gumma ()
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Ramavenkata Mahesh Nukala: Faculty of Geo-Engineering, Andhra University, Visakhapatnam 530003, India
Pranay Panjala: Digital Agriculture and Geospatial Sciences, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India
Vazeer Mahammood: Faculty of Geo-Engineering, Andhra University, Visakhapatnam 530003, India
Murali Krishna Gumma: Digital Agriculture and Geospatial Sciences, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India

Geographies, 2025, vol. 5, issue 4, 1-14

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.

Keywords: ground data; crop classification; iCrops; mobile application (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2025
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