Indonesia recently implemented a novel, technology-driven approach for conducting agricultural production surveys, which involves monthly observations at many thousands of strategic locations and automated data logging via a cellular phone application. Data from these comprehensive field surveys offer immense value for advancing remote sensing technology to map crop production across Indonesia, particularly through the development of machine learning approaches to relate survey data with satellite imagery. The objective of this study was to compare different machine learning scenarios for classifying and mapping the temporal progression of paddy rice production stages across West Java, Indonesia using synthetic aperture radar (SAR) and optical remote sensing data from Sentinel-1 and Sentinel-2 satellites. Monthly paddy rice survey data at 21,696 locations across West Java from November 2018 through April 2019 were used for model training and testing. Five classes related to rice production stage or other field conditions were defined, including rice at tillering, heading, and harvest stages, rice fields with little to no vegetation present, and non-rice areas. A recurrent neural network (RNN) with long short term memory (LSTM) nodes provided optimal performance with classification accuracies of 79.6% and 75.9% for model training and testing, respectively, and reduced computational effort. Other approaches that incorporated a convolutional neural network (CNN) either reduced classification accuracy or increased computational effort. Deep machine learning methods (RNN and CNN) generally outperformed other non-deep classifiers, which achieved up to 63.3% accuracy for model testing. Classification accuracies were optimized by inputting two Sentinel-1 channels (VH and VV polarizations) and ten Sentinel-2 channels. Temporal patterns of paddy rice production stages were consistent between the monthly ground-based agricultural survey data and 10-m, satellite-based rice classification maps obtained by applying the LSTM-based RNN across West Java. The results demonstrated the value of combining modern agricultural survey data, satellite remote sensing, and a recurrent neural network to develop multitemporal maps of paddy rice production stages.

印度尼西亚最近实施了一种新技术驱动的农业生产调查方法，包括每月在数千个战略地点进行观察，并通过手机应用程序自动记录数据。这些综合实地调查的数据为推进遥感技术绘制印度尼西亚作物生产地图提供了巨大价值，特别是通过开发机器学习方法将调查数据与卫星图像相关联。本研究的目的是使用合成孔径雷达 (SAR) 和 Sentinel-1 和 Sentinel-2 卫星的光学遥感数据，比较不同的机器学习场景，以对印度尼西亚西爪哇水稻生产阶段的时间进程进行分类和绘制. 21日的每月水稻调查数据，从 2018 年 11 月到 2019 年 4 月，西爪哇的 696 个地点用于模型训练和测试。定义了与水稻生产阶段或其他田间条件相关的五个类别，包括分蘖、抽穗和收获阶段的水稻、几乎没有植被的稻田和非水稻区。具有长短期记忆 (LSTM) 节点的循环神经网络 (RNN) 提供了最佳性能，模型训练和测试的分类准确率分别为 79.6% 和 75.9%，并减少了计算工作量。其他包含卷积神经网络 (CNN) 的方法要么降低了分类精度，要么增加了计算量。深度机器学习方法（RNN 和 CNN）普遍优于其他非深度分类器，模型测试的准确率高达 63.3%。通过输入两个 Sentinel-1 通道（VH 和 VV 极化）和十个 Sentinel-2 通道来优化分类精度。水稻生产阶段的时间模式在每月地面农业调查数据和通过在西爪哇应用基于 LSTM 的 RNN 获得的 10 米卫星水稻分类图之间是一致的。结果证明了结合现代农业调查数据、卫星遥感和循环神经网络来开发水稻生产阶段的多时相图的价值。通过在西爪哇应用基于 LSTM 的 RNN 获得的基于卫星的水稻分类图。结果证明了结合现代农业调查数据、卫星遥感和循环神经网络来开发水稻生产阶段的多时相图的价值。通过在西爪哇应用基于 LSTM 的 RNN 获得的基于卫星的水稻分类图。结果证明了结合现代农业调查数据、卫星遥感和循环神经网络来开发水稻生产阶段的多时相图的价值。