Remote Sensing-Based Mapping of Paddy Rice Cultivation Areas: Progress and Prospects in the Era of Artificial Intelligence
综述2000年以来水稻遥感制图进展，重点评估2016年后人工智能在其中的作用。
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综述指出云污染、作物光谱混淆、跨区域泛化不足等瓶颈，并列出地球观测基础模型、自监督与物理引导学习等优先方向，可作为水稻遥感选题与产品化的路线参考。
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MAPPING RICE CROPS IN CLOUD-COVERED MOUNTAINOUS AREAS USING SENTINEL-1 RADAR AND RANDOM FOREST ALGORITHM: A CASE STUDY IN LAO CAI, VIETNAM
用Sentinel-1雷达时序与随机森林在越南老街多云山区实现水稻制图。
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该研究在越南老街多云山区用 Sentinel-1 时序 VH 后向散射结合随机森林实现 91.4% 总体精度，水稻面积相对误差 6.8%，其变量筛选思路可迁移至国内西南多云山区水稻监测，但样本与地形条件具地域性。
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Diagnosis of Rice Blast Disease Using UAV-Based Multispectral Data: A Comparative Study of Feature Selection Methods
Rice blast is one of the most destructive diseases affecting rice throughout its entire growth cycle, posing serious threats to yield and quality. Timely, accurate, and non-destructive monitoring is therefore crucial for early warning and food security. In this study, rice blast inoculation experiments were conducted in 2019 and 2020 in Wanchang Town, Jilin Province, China. UAV-based multispectral imagery was collected at three key growth stages (booting, heading, and milk stages), and real-time kinematic (RTK) positioning was used to precisely locate ground sampling points where disease observations were recorded simultaneously. Spectral reflectance, vegetation indices, and texture features were extracted; four machine learning algorithms were applied to construct rice blast diagnostic models, and multiple feature selection methods were used to optimize the original high-dimensional feature sets. The main contributions of this study include establishing a multi-source feature system combining spectral, vegetation index, and texture information from multi-stage UAV imagery, proposing three new red edge indices tailored for rice blast detection, and systematically evaluating interactions between machine learning models and feature selection strategies. Results showed that the random forest model based on texture features performed best before feature selection (R 2 = 0.795, RMSE = 0.873). After feature selection, all models improved, with the gradient boosting decision tree model using the selected spectral - texture features achieving the highest accuracy (R 2 = 0.850, RMSE = 0.748). These findings demonstrate that integrating machine learning with appropriate feature selection can significantly enhance rice blast diagnosis and contribute to the development of precision agriculture.
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Optical footprint and background control for rice leaf assessment using a miniature multispectral reflectance sensor
研究微型多光谱传感器测水稻叶片时的光学足迹与背景干扰，并测试窄几何设计。
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Polarimetric interferometric synthetic aperture radar and machine learning for rice canopy height estimation
综述Pol-InSAR与机器学习结合估算水稻冠层高度的方法与挑战。
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Sentinel-1 SAR and Sentinel-2 optical land-cover classification with a prototype decision-support dashboard for the monsoon-driven Cauvery Delta
用Sentinel-1 SAR与Sentinel-2光学特征在印度高韦里三角洲分类水稻、植被和建成区
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Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models
构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。
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Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh
融合Sentinel-1 SAR与Sentinel-2光学影像，用随机森林在GEE上绘制孟加拉国Bo
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Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice
Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square
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Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy
提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。
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Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger
结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。
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Rice Cropping Pattern Mapping Using GEE-Based Sentinel-1/2 Time-Series Imagery and Machine Learning
基于GEE融合Sentinel-1/2时序与地形特征，用机器学习高精度制图长三角水稻种植模式。
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Spatial and Temporal Analysis of Rice Yield Using the DSSAT Model in the Kommamuru Canal Command Area, Guntur (Dist), Andhra Pradesh, India
用DSSAT-CERES水稻模型结合Sentinel-2遥感，分析印度Guntur灌区2022-20
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Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery
提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。
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AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment
提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。
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Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture
用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。
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UAV-based rice nitrogen status monitoring using machine learning and deep learning
UAV-based rice nitrogen status monitoring using machine learning and deep learning。Precision Agriculture
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An Optimized Temporal Attention-based Deep Learning Framwork for Accurate Crop Yield Prediction from Multi-source Agricultural Data
提出TA-ABC-Bi-LSTM模型，用多源农业数据精准预测水稻产量。
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Assessing rice genotype dependency in remote sensing: challenges in nitrogen discrimination and yield forecasting
评估遥感模型在不同水稻基因型间的可转移性，用于氮肥监测和产量预测。
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Rice seedling diagnosis of light and water stress based on green – blue channel feature enhancement
提出基于绿蓝通道特征增强的水稻幼苗光水胁迫诊断方法。
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