article · Scientific Reports
Accurate mapping of rice fields is essential for agricultural planning and food security policy. Using Google Earth Engine, research evaluated the efficiency of satellite datasets at different spatial resolutions, comparing Sentinel-2, Landsat-8, and MODIS across southern Punjab, Pakistan. Sentinel-2 achieved the highest overall classification accuracy at 96 per cent and an F1-score of 83.8 per cent, outperforming Landsat-8 and MODIS. Overall accuracy improved as spatial resolution increased. Sentinel-2 was specifically able to differentiate individual farm-level paddy fields, a task Landsat-8 could not achieve. Area estimates derived from Sentinel-2 and MODIS aligned closely with official regional statistics, showing a difference of under 20 per cent, whilst Landsat-8 showed a 33 per cent discrepancy. Additionally, satellite observations indicated a higher total cultivated area than recorded in official agricultural reports.
Reliable estimation of rice crop distribution and yields is vital for national food security and policy development. By establishing the relative accuracy of publicly available satellite data, this work helps agricultural authorities identify the most effective tools for tracking crop growth over large areas, ensuring land use data reflects real conditions on the ground.
This approach represents an applied and tested methodology for agricultural monitoring and regional planning. Government agencies, policy planners, and agricultural intelligence providers can use these Google Earth Engine workflows to estimate rice production areas accurately. Operating on existing, accessible satellite data, the technique appears near-market for deployment within regional crop monitoring systems and public sector agricultural forecasting platforms.
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Timely and accurate estimation of rice-growing areas and forecasting of production can provide crucial information for governments, planners, and decision-makers in formulating policies. While there exists studies focusing on paddy rice mapping, only few have compared multi-scale datasets performance in rice classification. Furthermore, rice mapping of large geographical areas with sufficient accuracy for planning purposes has been a challenge in Pakistan, but recent advancements in Google Earth Engine make it possible to analyze spatial and temporal variations within these areas. The study was carried out over southern Punjab (Pakistan)-a region with 380,400 hectares devoted to rice production in year 2020. Previous studies support the individual capabilities of Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) for paddy rice classification. However, to our knowledge, no study has compared the efficiencies of these three datasets in rice crop classification. Thus, this study primarily focuses on comparing these satellites' data by estimating their potential in rice crop classification using accuracy assessment methods and area estimation. The overall accuracies were found to be 96% for Sentinel-2, 91.7% for Landsat-8, and 82.6% for MODIS. The F1-Scores for derived rice class were 83.8%, 75.5%, and 65.5% for Sentinel-2, Landsat-8, and MODIS, respectively. The rice estimated area corresponded relatively well with the crop statistics report provided by the Department of Agriculture, Punjab, with a mean percentage difference of less than 20% for Sentinel-2 and MODIS and 33% for Landsat-8. The outcomes of this study highlight three points; (a) Rice mapping accuracy improves with increase in spatial resolution, (b) Sentinel-2 efficiently differentiated individual farm level paddy fields while Landsat-8 was not able to do so, and lastly (c) Increase in rice cultivated area was observed using satellite images compared to the government provided statistics.
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DOI: 10.1038/s41598-022-17454-y
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