In presenting an irrigation detection methodology that leverages multiscale satellite imagery of vegetation abundance, this paper introduces a process to supplement limited ground-collected labels and ensure classifier applicability in an area of interest. Spatiotemporal analysis of MODIS 250m Enhanced Vegetation Index (EVI) timeseries characterizes native vegetation phenologies at regional scale to provide the basis for a continuous phenology map that guides supplementary label collection over irrigated and non-irrigated agriculture. Subsequently, validated dry season greening and senescence cycles observed in 10m Sentinel-2 imagery are used to train a suite of classifiers for automated detection of potential smallholder irrigation. Strategies to improve model robustness are demonstrated, including a method of data augmentation that randomly shifts training samples; and an assessment of classifier types that produce the best performance in withheld target regions. The methodology is applied to detect smallholder irrigation in two states in the Ethiopian highlands, Tigray and Amhara. Results show that a transformer-based neural network architecture allows for the most robust prediction performance in withheld regions, followed closely by a CatBoost random forest model. Over withheld ground-collection survey labels, the transformer-based model achieves 96.7% accuracy over non-irrigated samples and 95.9% accuracy over irrigated samples. Over a larger set of samples independently collected via the introduced method of label supplementation, non-irrigated and irrigated labels are predicted with 98.3% and 95.5% accuracy, respectively. The detection model is then deployed over Tigray and Amhara, revealing crop rotation patterns and year-over-year irrigated area change. Predictions suggest that irrigated area in these two states has decreased by approximately 40% from 2020 to 2021.
翻译:本文在介绍利用植被丰度的多尺度卫星图像的灌溉探测方法时,介绍了一个过程,以补充有限的地面采集标签,并确保在一个感兴趣的地区适用分类。对MODIS 250m 增强植被指数(EVI)的时间序列进行斯帕蒂时间序列分析,对MODIS 250m 增强植被指数(EVI)进行了区域范围的本地植被动物学分析,以提供一个连续的动物学图,指导灌溉和非灌溉农业补充标签收集工作。随后,在10m Sentinel-2图像中观测到的旱季绿化和耐人性循环周期,用于培训一组分类人员,以便自动检测潜在的小农灌溉;展示了提高模型可靠性的战略,包括随机移动培训样本的数据增强方法;评估了在被扣留的目标地区产生最佳绩效的分类类型。该方法用于埃塞俄比亚高地提格雷和阿姆哈拉两个州的小农灌溉。结果显示,基于变压器的神经网络架构允许在被扣留地区进行最可靠的预测,紧随其后的是一个Ctoboost森林随机检测模式。超过95号的土壤探测模型显示,经过了98%的准确度测量标值区域。