Fernández-Urrutia Manuel, Arbelo Manuel, Gil Artur
Departamento de Física, Universidad de La Laguna, 38200 San Cristobal de La Laguna, Spain.
Irish Centre for High-End Computing (ICHEC), University of Galway, H91TK33 Galway, Ireland.
Sensors (Basel). 2023 Aug 3;23(15):6932. doi: 10.3390/s23156932.
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.
大米是养活了近一半世界人口的主食。随着地球人口预计持续增长，进行精确的测绘、监测和评估至关重要，因为这些工作会对粮食安全、气候变化、空间规划和土地管理产生重大影响。本文采用PRISMA系统评价方案，识别并挑选了122篇科学文章（期刊论文和会议论文），这些文章探讨了2010年至2022年10月期间发表的基于不同遥感方法的稻田测绘。该分析全面涵盖了稻田测绘及其作物成熟的各个阶段。这篇综述文章根据数据源对方法进行了分类：（a）多光谱（62%），（b）多源（20%），以及（c）雷达（18%）。此外，文章分析了机器学习对这些方法的影响以及所使用的最常见算法。我们发现，MODIS（28%）、哨兵-2（18%）、哨兵-1（15%）和陆地卫星-8（11%）是使用最多的传感器。由于后向散射信息在确定不同阶段纹理和减少云层覆盖限制方面的潜力，哨兵-1对多源解决方案的影响也在增加。首选的解决方案包括通过使用植被指数、设置阈值或应用机器学习算法对图像进行分类的物候算法。在机器学习算法方面，随机森林使用最多（17次），其次是支持向量机（12次）和迭代自组织数据分析技术（7次）。随着技术和计算能力的不断发展，预计多源解决方案等方法将更频繁地出现，并以更高的分辨率覆盖不同地点的更大区域。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。
