Moumni Aicha, Lahrouni Abderrahman
Faculty of Science Semlalia, Cadi Ayyad University, Marrakesh 40000, Morocco.
Scientifica (Cairo). 2021 Apr 20;2021:8810279. doi: 10.1155/2021/8810279. eCollection 2021.
The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.
监测耕地作物和不同土地覆盖类型对于农业土地管理和作物产量预测而言是一个重要的环境与经济问题。在此背景下，本文旨在运用并评估基于机器学习分类器的多传感器分类对摩洛哥半干旱地区作物类型识别的贡献。这是一个非常多样化的区域，其特点是作物种类多样（乔木作物与一年生作物混种、同一农业季节内同一作物处于不同物候状态、作物轮作等）。因此，这种多样性使得作物类型的区分更加复杂。为克服这些挑战，本研究是该领域首次利用高时空分辨率的哨兵 -1 和哨兵 -2 卫星图像融合进行土地利用和土地覆盖制图的研究。三种机器学习分类算法，即人工神经网络（ANN）、支持向量机（SVM）和最大似然法（ML），被用于识别和绘制灌溉区域的作物类型。利用 2018 年摩洛哥中部豪兹平原 R3 区域的实地观测数据以及同年的卫星数据开展此项工作。结果表明，与单独使用光学或合成孔径雷达（SAR）数据的分类结果相比，C 波段和光学波段获取的组合图像显著提高了作物类型分类性能（总体精度 = 89%；卡帕系数 = 0.85）。
