Crop acreage analysis and yield estimation are of prime importance in field-level agricultural monitoring and management. This enables prudent decision making during any crop failure event and for ensuing crop insurance. The free availability of the high resolution Sentinel-2 satellite datasets has created new possibilities for mapping and monitoring agricultural lands in this regard. In the present study conducted on the Tamluk Subdivision of the Purba Medinipur District of West Bengal, the heterogeneous crop area was mapped according to the respective crop type, using Sentinel-2 multi-spectral images and two machine learning algorithms- K Nearest Neighbour (KNN) and Random Forest (RF). Plot-level field information was collected from different cropland types to frame the training and validation datasets (comprising 70% and 30% of the total dataset, respectively) for cropland classification and accuracy assessment. Through this, the major summer crop acreage was identified (Boro rice, vegetables and betel vine- the three main crops in the study area). The extracted maps had an overall accuracy of 97.16% and 97.22%, respectively, in the KNN and RF classifications, with respective Kappa index values of 95.99% and 96.08%, and the RF method proved to be more accurate. This study was particularly useful in mapping the betel leaf acreage herein since scant information exists for this crop and it is cultivated by many smallholder farmers in the region. The methods used in this paper can be readily applied elsewhere for accurately enumerating the respective crop acreages.

作物种植面积分析和产量估算在田间农业监测和管理中至关重要。这使得在任何作物歉收事件和随后的作物保险期间都能做出谨慎的决策。高分辨率 Sentinel-2 卫星数据集的免费可用性为在这方面绘制和监测农业用地创造了新的可能性。在目前对西孟加拉邦 Purba Medinipur 区 Tamluk 分区进行的研究中，使用 Sentinel-2 多光谱图像和两种机器学习算法——K 最近邻（KNN），根据各自的作物类型绘制了异质作物区域) 和随机森林 (RF)。从不同农田类型收集地块级字段信息，以构建训练和验证数据集（分别占总数据集的 70% 和 30%），用于农田分类和准确性评估。通过这个，确定了主要的夏季作物种植面积（Boro水稻、蔬菜和槟榔——研究区的三种主要农作物）。提取的图在KNN和RF分类中的总体准确率分别为97.16%和97.22%，Kappa指数分别为95.99%和96.08%，证明RF方法更准确。这项研究在绘制槟榔叶面积图时特别有用，因为这种作物的信息很少，而且该地区的许多小农都种植这种作物。本文中使用的方法可以很容易地应用于其他地方，以准确计算各个作物的种植面积。