[1]国家自然科学基金委员会, 面上项目, 42071423, 区域小麦赤霉病遥感动态预测方法研究, 2021-01-0 1 至 2024-12-31, 56万元, 在研, 主持

[2]“一带一路”国际科学组织联盟资助, ANSO-CR-KP-2021-06, 全球流行性和迁飞性农业重大病虫害遥感监测与预测, 2021.12.1-2024.12.1, 130万元, 在研, 主持

[3]科学技术部, 国家重点研发计划, 重大蝗虫成灾机制与可持续防控技术研究, 2022YFD1400503, 子课题——蝗虫行为适应与干预, 2022.09-2025.12, 60万元, 在研, 主持

[4]科学技术部, 国家重点研发计划政府间国际科技创新合作重点专项, 2017YFE0122400, 粮食作物重大病虫害遥感监测预警与防控技术, 2019.03-2022.02, 839万元, 已结题, 主持

[5]科学技术部, 国家重点研发计划粮食丰产增效科技创新项目子课题, 2016YFD0300702-子课题, 基于时空连续数据的黄淮海区主要作物病虫害遥感定量监测, 2016.07-2020.12, 47万元, 已结题, 主持

[6]国家自然科学基金委员会, 青年科学基金项目, 41601466, 结合形态和营养指标的小麦长势遥感监测方法, 2017.01-2019.12, 19万元, 已结题, 主持

[7]北京市科学技术委员会, 北京市科技新星计划, Z191100001119089, 小麦主产区重大病虫害遥感动态预警研究, 2019.11-2022.10, 50万元, 已结题, 主持

[8]中国科学院, 青年创新促进会, 2017085, 2017.01-2020.12, 80万元, 已结题, 主持

[9]中国科学院战略性先导科技专项(A类)任务, DESP Explorer关键技术研究与平台研制, XDA19080101-任务4, 2018.01-2022.12, 1132.77万, 已结题, 主持

[10] 中国科学院空天信息创新研究院, 重点部署项目, E0Z205010F, 基于AI遥感的全球迁飞性害虫监测预警, 2020.08-2022.08, 150万元, 已结题, 主持

[11] 国家自然科学基金委员会, 国际(地区)合作与交流项目, 61661136004, 主要作物病虫害遥感监测与预测方法研究, 2016.04-2019.03, 299.892万元, 已结题, 参与

[12] 国家自然科学基金委员会, 面上项目, 41571354, 类胡萝卜素探测作物养分胁迫的光谱诊断机理与方法研究, 2016.01-2019.12, 80万元, 已结题, 参与

[13] 国家自然科学基金委员会, 面上项目, 41571423, 时序遥感数据与生长模型结合的区域人工林地上生物量估算方法研究, 2016.01-2019.12, 60万元, 已结题, 参与

[14] 国家自然科学基金委员会, 青年科学基金项目, 41501468, 玉米冠层氮素养分垂直分布遥感监测研究, 2016.01-2018.12, 20万元, 已结题, 参与

[15] 联合国粮农组织FAO基金项目, 高加索和中亚地区的飞蝗监测与生物防治, 2021.01-2022.12, USD50万元, 结题, 参与

[1]董莹莹(1/10); 植被病虫害遥感监测预警与防控关键技术及应用, 北京市人民政府, 科技进步, 省部二等奖, 2023(董莹莹; 黄文江; 朱景全; 孙红; 周晋峰; 郭蕾; 张竞成; 叶回春; 王豁; 张弼尧) .

[2]董莹莹(1/10); 蝗虫遥感监测和预测关键技术与应用, 中国林学会, 梁希林业科学技术奖科技进步奖, 二等奖, 2021(董莹莹; 方国飞; 黄文江; 李计顺; 高薇; 叶回春; 李晓冬; 孙红; 赫传杰; 张家胜).

[3]董莹莹(1/10); 植被病虫害遥感监测和预测关键技术与应用, 北京市大北农科技奖奖励委员会, 大北农科技奖, 创新奖, 2021(董莹莹; 朱景全; 黄文江; 李红梅; 蒙艳华; 农向群; 王志国; 任彬元; 刘博; 刘林毅).

[1]Yingying Dong, Longlong Zhao, Wenjiang Huang. < Monitoring of Desert Locust in Africa and Asia>, Science Press, Springer, Beijing, 2023.01. ISBN: 978-981-19-7238-6.

[2]Yingying Dong, Wenjiang Huang, Yun Geng, Linyi Liu, Huiqin Ma, Anting Guo and Chao Ruan, , Science Press, EDP Sciences, 2022.03. ISBN: 978-2-7598-2659-9.

[3]董莹莹, 赵龙龙, 黄文江著,《亚非沙漠蝗虫灾情遥感监测》, 科学出版社, 北京, 2021. ISBN: 978-7-03-068239-0

[4]Yingying Dong, Fang Xu, Linyi Liu, et al. Automatic System for Crop Pest and Disease Dynamic Monitoring and Early Forecasting[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4410-4418.

[5]Qi T, Dong Y, Li X L, et al. Fall armyworm habitat analysis in Africa with multi-source earth observation data[J]. Computers and Electronics in Agriculture, 2024, 225: 109283.

[6]Guo J, Huang W, Dong Y, et al. Spatiotemporal monitoring of grasshopper habitats using multi-source data: Combined with landscape and spatial heterogeneity[J]. International Journal of Applied Earth Observation and Geoinformation, 2024, 130: 103838.

[7]Han Y, Dong Y, Zhu Y, et al. Remote sensing inversion of vegetation parameters with IPROSAIL-Net[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023.

[8]Zhao M, Dong Y, Huang W, et al. Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors[J]. Remote Sensing, 2023, 15(18): 4631.

[9]Huang L, Chen X, Dong Y, et al. Dynamic Analysis of Regional Wheat Stripe Rust Environmental Suitability in China[J]. Remote Sensing, 2023, 15(8): 2021.

[10] Wang J, Huang Y, Huang L, et al. Migration risk of fall armyworm (Spodoptera frugiperda) from North Africa to Southern Europe[J]. Frontiers in Plant Science, 2023, 14: 1141470.

[11] Xueling Li, Yingying Dong*, Yining Zhu, et al. Enhanced Leaf Area Index Estimation with CROP-DualGAN Network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, DOI: 10.1109/TGRS.2022.3230354.

[12] Yingxin Xiao, Yingying Dong*, Wenjiang Huang, et al. Regional prediction of Fusarium head blight occurrence in wheat with remote sensing based Susceptible-Exposed-Infectious-Removed model[J]. International Journal of Applied Earth Observations and Geoinformation, 2022, 114: 103043.

[13] Ruiqi Sun, Wenjiang Huang, Yingying Dong*, et al. Dynamic Forecast of Desert Locust Presence Using Machine Learning with a Multivariate Time Lag Sliding Window Technique[J]. Remote Sensing, 2022, 14(3): 747-767.