Data for \"A Two-Step Method for Monitoring Annual Paddy Rice Planted Area in Tropical Region by Integrating Rice Identification and Cropping Intensity Estimation\"

This dataset accompanies the research article \"A Two-Step Method for Monitoring Annual Paddy Rice Planted Area in Tropical Region by Integrating Rice Identification and Cropping Intensity Estimation\" , comprising two primary components for Vietnam in 2020: (1) the high-resolution paddy rice spatial distribution data and (2) the paddy rice cropping intensity data. The paddy rice spatial distribution data was generated through a machine learning workflow where Random Forest classifier was trained using rice/non-rice samples and features extracted from Sentinel-1 SAR and Sentinel-2 MSI images. This classification process was specifically constrained within the Rice Potential Zone (RPZ) to enhance accuracy. Based on the fine spatial distribution of paddy rice, the Sentinel-1 SAR time series images were smoothed by Harmonic Analysis of Time Series (HANTS), and then the quadratic difference method was used to generate the cropping intensity information of rice. Please refer to the original article for more details on the methodology and implementation. DOI: https://doi.org/10.1109/tgrs.2025.3549296

本数据集配套于研究论文《集成水稻识别与种植强度估算的热带区域年度水稻种植面积两步监测方法》（A Two-Step Method for Monitoring Annual Paddy Rice Planted Area in Tropical Region by Integrating Rice Identification and Cropping Intensity Estimation），包含2020年越南的两大核心组成部分：(1) 高分辨率水稻空间分布数据；(2) 水稻种植强度数据。水稻空间分布数据通过机器学习流程生成：以水稻/非水稻样本及从哨兵1号合成孔径雷达（Sentinel-1 SAR）、哨兵2号多光谱成像仪（Sentinel-2 MSI）影像中提取的特征为训练数据，训练随机森林分类器（Random Forest classifier）；为提升分类精度，该分类过程严格限定于水稻潜在种植区（RPZ）内开展。基于精细化的水稻空间分布结果，研究团队首先通过时间序列谐波分析法（HANTS）对哨兵1号合成孔径雷达时间序列影像进行平滑处理，随后采用二次差分法生成水稻种植强度信息。如需了解方法学与具体实现细节，请参阅原研究论文。DOI: https://doi.org/10.1109/tgrs.2025.3549296