CAREER: Scalable Remote Sensing Computational Framework for Near-real-time Crop Characterization

The increasing proliferation of earth observation satellites, along with the explosive growth of remote sensing data, has dramatically facilitated timely land surface characterization worldwide. With several national and international agricultural initiatives, near-real-time crop type characterization has become vital for providing early warnings on food insecurity and timely crop yield forecasting, and for global food market transparency. However, near-real-time crop type characterization remains a challenge in agricultural remote sensing, due to the difficulty in collecting timely crop ground reference data, the limited generalizability of existing characterization models, and the lack of appropriate remote sensing cyberinfrastructure. Recent advances in satellite remote sensing and computational cyberinfrastructure open a new avenue to tackle the challenge. The overarching goal of the project is to establish a scalable remote sensing computational framework for near-real-time crop type characterization and to promote computational remote sensing education. The computational framework can transform the large-scale agricultural monitoring paradigm to meet the timely crop characterization requirements of global agricultural initiatives. The framework can substantially boost the ability to respond rapidly to emerging food crises, as well as create cross-cutting impacts in advancing a broad spectrum of remote sensing and agricultural research. The synergistic education and outreach activities offer unique learning opportunities about computational remote sensing to students from K-12 to the graduate level, and will broaden the participation of underrepresented students in computing. These activities also facilitate the open development and adoption of the computational framework across a range of disciplines. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity, and welfare.

The advanced remote sensing computational framework focuses on the development of a benchmark data repository called CropSight, a crop characterization modeling system, and a cutting-edge remote sensing cyberinfrastructure, to catalyze near-real-time crop and land surface characterizations. CropSight is a unique national-scale crop ground reference data repository, and embodies a wealth of season-long remotely sensed crop growth and environmental attributes across crop growing locations for most crop types in the U.S. CropSight can be generalized to continental and global scales, and will be used as a large-scale, systematic, and consistent ground reference data repository. The crop characterization system comprises a suite of novel deep learning-based computational models that can fuse the imagery from a set of earth observation satellites for timely crop monitoring, as well as identify varying crop types via innovative modeling of complex crop-environment interactions. The system will increase modeling generalizability for crop type characterization, and holds considerable potential to be extrapolated over wide geographical regions. The remote sensing cyberinfrastructure will include a highly scalable and cloud native implementation of the CropSight and a near-real-time on-demand crop monitoring system. With a serverless architecture, the project will build the cloud middleware that integrates various geospatial data sources and enables data-intensive remote sensing data analytics for timely crop characterization. The cyberinfrastructure will empower the paradigm shift from conventional compute-limited remote sensing analysis to massive imagery analysis for timely land surface monitoring.


Diao, C., and G. Li. (2022). Near-surface and high-resolution satellite time series for detecting crop phenology. Remote Sensing, 14(9), 1957.


Conference Presentations:

Yang, Z. and C. Diao. A phenology-guided deep learning model for early crop mapping at the field level. Annual Meeting of the Association of American Geographers (AAG), New York City, NY. February 25-March 1, 2022.

Diao, C. Towards remote sensing modeling framework for crop phenological characterization. Annual Meeting of the Association of American Geographers (AAG), New York City, NY. February 25-March 1, 2022.

Diao, C., Yang, Z., Gao, F., Zhang, X. and Z. Yang. A novel hybrid phenology matching model for robust crop growth stage characterization. American Geophysical Union (AGU) Fall Meeting, New Orleans, LA, December 13-December 17, 2021.


Conference Sessions:

"Advances in Agricultural Remote Sensing and Artificial Intelligence", Annual Meeting of the Association of American Geographers (AAG), New York City, NY. February 25-March 1, 2022.