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ARS Home » Research » Publications at this Location » Publication #106955
Title: IMAGE-BASED REMOTE SENSING FOR PRECISION CROP MANAGEMENT - A STATUS REPORT

Author
item Moran, Mary

Submitted to: American Society of Civil Engineers
Publication Type: Proceedings
Publication Acceptance Date: 6/14/2000
Publication Date: N/A
Citation: N/A

Interpretive Summary: Farm managers are adopting high-technology approaches for precision crop management, based on desk-top computers, satellite-based positioning systems, and automated systems for control of farm machinery. An information technology that is also being considered is the use of images from orbiting sensors (termed remote sensing) to provide detailed information about crop and soil variability. This status report offers an assessment of progress made in image-based remote sensing in relation to the information needs of precision crop management. Remote sensing products were classified into five levels, ranging from a simple color picture to a decision support system (DSS) that combined remote sensing information with crop growth simulation models to develop management strategies. Great strides have been made in the first three product levels related to sensor calibration, image atmospheric correction, and crop and soil condition mapping. Less progress has been made at the fourth and fifth levels to assimilate remotely sensed information into crop growth simulation models and use DSS to provide farm management advice. Scientists and engineers should be working to refine remote sensing systems and image processing techniques toward the goal of providing not just a pretty picture, but rather, sound farm management advice. This will be beneficial for farmers, consultants, researchers, and other agricultural support agencies.

Technical Abstract: This status report offers an assessment of progress made in image-based remote sensing in relation to the information needs of precision crop management (PCM). The assessment includes discussion of (1) image geometric, radiometric, and atmospheric correction; (2) retrieval of crop and soil information from image data; and (3) transformation of such information into management advice that could be implemented with PCM technology. The report is presented through examples of case studies conducted on Arizona farms by scientists with the USDA, Agricultural Research Service.