Abstract
Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.
Highlights
Rice is the principal food for nearly 50% of the world’s seven billion people, mostly in Asia, Africa and Latin America [1]
Classification results revealed that the paddy fields were primarily distributed in the plain areas over the region (Figures 7 and 8)
The temporal features represented the seasonal variation of crops that helped with better classification, as seasonal characteristics differ according to crop types
Summary
Rice is the principal food for nearly 50% of the world’s seven billion people, mostly in Asia, Africa and Latin America [1]. Paddy rice covers more than 12% of global cropland areas [2], and it consumes about 24%–30% of the world’s developed fresh water resources [3]. Rice is the largest water-consuming crop and is cultivated primarily in constantly flooded fields. Rice cultivation can contribute to climate change [4] as the flooded paddy fields are responsible for 10% of human-induced methane (CH4 ) [5], or 20% of total agricultural CH4 emissions [5]. Increasing urbanization, rising global temperature, industrialization, and changing precipitation patterns are affecting the land and water resources of rice production [7]. It is important to monitor and map the paddy rice fields for the assessment of food security, efficient water resources management, environmental sustainability, and controlling the transmission of influenza viruses
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