Abstract Accurate crop mapping provides important and timely information for decision support on the estimation of crop production at large scale. Most existing crop-specific cover products based on remote sensing data and machine learning algorithms cannot serve large agriculture production areas as a result of poor model transfer capabilities. Developing a generalizable crop classification model for spatial transfer across regions is greatly needed. A deep learning approach, named DeepCropMapping (DCM), has been developed based on long short-term memory structure with attention mechanisms through integrating multi-temporal and multi-spectral remote sensing data for large-scale dynamic corn and soybean mapping. Full cross validation of classification experiments were conducted in six sites each covering 2,890,000 pixels at 30 m resolution in the U.S. corn belt from Year 2015 to 2018. Landsat Analysis Ready Data (ARD) and Cropland Data Layer (CDL) were adopted as the input satellite observations and ground reference, respectively. Transformer, Random Forest (RF), and Multilayer Perceptron (MLP) models were built for comparison. The DCM model produced a mean kappa score of 85.8% in base sites and a mean average kappa score of 82.0% in transfer sites at the end of the growing season. It yielded a comparable performance to Transformer and better than RF and MLP at the local test. The DCM model significantly outperformed other three models with a 95% confidence interval in the spatial transfer analysis. The results demonstrated the capability of learning generalizable features by the DCM model from ARD time series. The computational complexity analysis suggested that the DCM model required a shorter training time than Transformer but longer than MLP and RF. The results of the in-season classification experiment indicated the DCM model captured critical information from key growth phases and achieved higher accuracy than other models after the beginning of July. By monitoring the classification confidence in each time step, the results showed that the increased length of seasonal remote sensing time series would reduce the classification uncertainty in all sites. This study provided a viable option toward large-scale dynamic crop mapping through the integration of deep learning and remote sensing time series.

中文翻译：

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DeepCropMapping：一种多时相深度学习方法，具有改进的动态玉米和大豆映射空间泛化性

摘要 准确的作物制图为大规模作物产量估算的决策支持提供了重要而及时的信息。大多数现有的基于遥感数据和机器学习算法的特定作物覆盖产品由于模型转移能力差而无法服务于大型农业生产区。非常需要为跨区域的空间转移开发可推广的作物分类模型。通过整合多时相和多光谱遥感数据，基于长短期记忆结构和注意机制，开发了一种名为 DeepCropMapping (DCM) 的深度学习方法，用于大规模动态玉米和大豆制图。在六个站点进行了分类实验的全面交叉验证，每个站点覆盖 2,890，2015 年至 2018 年美国玉米带在 30 m 分辨率下为 000 像素。分别采用陆地卫星分析就绪数据 (ARD) 和农田数据层 (CDL) 作为输入卫星观测和地面参考。构建了 Transformer、随机森林 (RF) 和多层感知器 (MLP) 模型以进行比较。DCM 模型在生长季节结束时在基点产生了 85.8% 的平均 kappa 得分，在转移点产生了 82.0% 的平均 kappa 得分。它产生了与 Transformer 相当的性能，并在本地测试中优于 RF 和 MLP。DCM 模型在空间转移分析中以 95% 的置信区间显着优于其他三个模型。结果证明了 DCM 模型从 ARD 时间序列中学习可泛化特征的能力。计算复杂度分析表明，DCM 模型需要的训练时间比 Transformer 短，但比 MLP 和 RF 长。季节性分类实验结果表明，DCM模型从关键生长阶段捕获了关键信息，并在7月初之后取得了比其他模型更高的准确率。通过监测每个时间步长的分类置信度，结果表明，季节性遥感时间序列长度的增加将降低所有站点的分类不确定性。这项研究通过深度学习和遥感时间序列的整合，为大规模动态作物制图提供了一个可行的选择。季节性分类实验结果表明，DCM模型从关键生长阶段捕获了关键信息，并在7月初之后取得了比其他模型更高的准确率。通过监测每个时间步长的分类置信度，结果表明，季节性遥感时间序列长度的增加将降低所有站点的分类不确定性。这项研究通过深度学习和遥感时间序列的整合，为大规模动态作物制图提供了一个可行的选择。季节性分类实验结果表明，DCM模型从关键生长阶段捕获了关键信息，并在7月初之后取得了比其他模型更高的准确率。通过监测每个时间步长的分类置信度，结果表明，季节性遥感时间序列长度的增加将降低所有站点的分类不确定性。这项研究通过深度学习和遥感时间序列的整合，为大规模动态作物制图提供了一个可行的选择。结果表明，季节性遥感时间序列长度的增加将减少所有站点的分类不确定性。这项研究通过深度学习和遥感时间序列的整合，为大规模动态作物制图提供了一个可行的选择。结果表明，季节性遥感时间序列长度的增加将减少所有站点的分类不确定性。这项研究通过深度学习和遥感时间序列的整合，为大规模动态作物制图提供了一个可行的选择。