Abstract
In the agriculture sector food productivity, security, and sustainability, imposed challenges on farmers, regulatory bodies, and policymakers due to increasing demand and depleting natural resources and environmental concerns. Rice crop holds a prominent place in Pakistan's agriculture sector, it is not only consumed locally but also exported to many countries including China. Gathering crop information such as variety maps, yield estimation, or etc. can help farmers, regulatory bodies, policymakers, and rice mills in decision-making. In Pakistan, crop information is collected through manual field surveys that require a lot of human labor, are costly, and are time-consuming. One cannot ignore human error and bias in the process. A new framework for pixel classification is proposed that uses both spectral and time-series data of Sentinal-2 satellite for mapping two rice varieties "Basmati" and "IRRI, grown in Pakistan. The data were collected from twelve rice fields (approx. 307 acres) of different geographical locations at 16-time instances to cover the complete rice-growing season (May-October) in 2019. A linear spectral unmixing model is used to determine sub-pixel information of water, soil, and vegetation content, which is used for labeling each pixel for supervised learning. The input to our classifier is a 16 x 15 image formed using 15 spectral features (12 spectral bands and 3 radiometric indices) of 16 carefully selected different time instances for each pixel. The output is a pixel-level classification (semantic segmentation) of each pixel into Basmati, IRRI, and others (soil, water, etc.). Experimental results have exhibited an excellent overall accuracy of 98.6% with the proposed approach. The Basmati rice obtained higher accuracy of 99.7% as compared to IRRI rice with an accuracy of 95.2%.