大米是世界上近一半人口的主食。随着我们星球上的人口预计会持续增长,进行准确的制图至关重要,监测,和评估,因为这些可能会显著影响粮食安全,气候变化,空间规划,和土地管理。使用PRISMA系统审查方案,本文确定并选择了122篇科学论文(期刊论文和会议记录),涉及基于遥感的不同方法来绘制稻田,在2010年至2022年10月之间发布。该分析包括对稻田及其作物成熟的各个阶段的制图的全面报道。本文根据数据源对方法进行了分类:(a)多光谱(62%),(b)多源(20%),和(c)雷达(18%)。此外,它分析了机器学习对这些方法和最常用的算法的影响。我们发现MODIS(28%),Sentinel-2(18%),Sentinel-1(15%),Landsat-8(11%)是最常用的传感器。Sentinel-1对多源解决方案的影响也在增加,这是由于反向散射信息在不同阶段确定纹理并减少云层覆盖约束的潜力。优选的解决方案包括通过使用植被指数的物候算法,设置阈值,或应用机器学习算法对图像进行分类。在机器学习算法方面,随机森林是最常用的(17次),其次是支持向量机(12次)和isodata(7次)。随着技术和计算的不断发展,预计多源解决方案等解决方案将更频繁地出现,并以更高的分辨率覆盖不同位置的更大区域。此外,云检测算法的不断改进将对多光谱解决方案产生积极影响。
Rice is a staple food that feeds nearly half of the world\'s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic
review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This
review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c)
radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.