Abstract Accurate and up-to-date cropland maps play an important role in the study of food security. Traditional mapping of croplands using medium resolution (10–100 m) remote sensing imagery involving a “one-time, one-place” approach requires significant computing and labor resources. Although high mapping accuracies can be achieved using this approach, it is tedious and expensive to collect reference information to train the classifiers at each location and to apply over large areas, such as a continent. Moreover, large area cropland mapping presents additional challenges including a wide range of agricultural management practices, climatic conditions, and crop types. To overcome these challenges, here we report on a generalized image classifier to map cropland extent, which builds a classification model using training data from one location and time period, applied to other times and locations without the need for additional training data. The study was demonstrated across eight agro-ecological zones (AEZs) in Europe, the Middle East and North Africa using Landsat data acquired between 2009 and 2011. To reduce between-scene variability associated with image availability and cloud cover, input data were reduced to salient temporal statistics derived from enhanced vegetation index (EVI) combined with topographic variables. The generalized classifier was then tested across three levels of generalization: 1. individual - where training data were extracted from and applied to the same Landsat footprint; 2. AEZ where training data were extracted from a set of Landsat footprints within an AEZ and applied to any other Landsat footprint in the same AEZ; and 3. regional where training data were extracted from a set of Landsat footprints in the whole study area and applied to any other Landsat footprint inside the study area. Results showed that the generalized classifier is successful in identifying and mapping croplands with comparable success across all three levels of generalization with minimal cost: average loss in accuracy (as measured by overall accuracy) from the individual level (average overall accuracy of 80 ± 5%) to regional level (average overall accuracy of 74 ± 10%) is between 2 and 10% depending on the location. Results also show that generalization is not sensitive to the choice of the classification algorithm – the Linear Discriminant Analysis (LDA) model performs equally well compared to many popular machine learning algorithms found in the literature. This work suggests the generalization/signature extension framework has a great potential for rapid identification and mapping of croplands with reasonable accuracies over large areas using only easily computed vegetation indices with very little user input and ground information requirement.

中文翻译：

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使用 Landsat 数据和广义分类器绘制大面积农田范围

摘要 准确和最新的农田地图在粮食安全研究中发挥着重要作用。使用中分辨率（10-100 m）遥感影像的传统农田制图涉及“一次性、一地”方法，需要大量的计算和劳动力资源。尽管使用这种方法可以实现高映射精度，但收集参考信息以在每个位置训练分类器并应用于大面积区域（例如大陆）是繁琐且昂贵的。此外，大面积农田制图带来了额外的挑战，包括广泛的农业管理实践、气候条件和作物类型。为了克服这些挑战，我们在这里报告了一种用于映射农田范围的广义图像分类器，它使用来自一个位置和时间段的训练数据构建分类模型，应用于其他时间和位置，而无需额外的训练数据。该研究使用 2009 年至 2011 年期间获取的 Landsat 数据在欧洲、中东和北非的八个农业生态区 (AEZ) 中进行了演示。为了减少与图像可用性和云量覆盖相关的场景间可变性，输入数据减少到从增强的植被指数 (EVI) 与地形变量相结合得出的显着时间统计数据。然后在三个泛化级别上测试泛化分类器： 1. 个体 - 从其中提取训练数据并将其应用于相同的 Landsat 足迹；2. AEZ，其中训练数据是从 AEZ 内的一组 Landsat 足迹中提取的，并应用于同一 AEZ 中的任何其他 Landsat 足迹；3. 从整个研究区域的一组 Landsat 足迹中提取训练数据并应用于研究区域内的任何其他 Landsat 足迹的区域。结果表明，广义分类器成功地识别和映射农田，并以最小的成本在所有三个泛化级别上取得了相当的成功：从个体级别（平均整体准确度为 80 ± 5%）的平均准确度损失（以整体准确度衡量） ) 到区域级别（平均总体准确度为 74 ± 10%）介于 2 到 10% 之间，具体取决于位置。结果还表明，泛化对分类算法的选择并不敏感——与文献中发现的许多流行的机器学习算法相比，线性判别分析 (LDA) 模型的表现同样出色。这项工作表明，泛化/特征扩展框架在快速识别和绘制大面积农田的合理精度方面具有巨大潜力，仅使用易于计算的植被指数，用户输入和地面信息要求很少​​。