Linking Fine-Scale Observations and Model Output with Imagery at Multiple Scales
Thursday, 18 December 2014: 2:40 PM
The development and implementation of a system for seasonal worldwide agricultural yield estimates is underway with the international Group on Earth Observations GeoGLAM project. GeoGLAM includes a research component to continually improve and validate its algorithms. There is a history of field measurement campaigns going back decades to draw upon for ways of linking surface measurements and model results with satellite observations. Ground-based, in-situ measurements collected by interdisciplinary teams include yields, model inputs and factors affecting scene radiation. Data that is comparable across space and time with careful attention to calibration is essential for the development and validation of agricultural applications of remote sensing. Data management to ensure stewardship, availability and accessibility of the data are best accomplished when considered an integral part of the research. The expense and logistical challenges of field measurement campaigns can be cost-prohibitive and because of short funding cycles for research, access to consistent, stable study sites can be lost. The use of a dedicated staff for baseline data needed by multiple investigators, and conducting measurement campaigns using existing measurement networks such as the USDA Long Term Agroecosystem Research network can fulfill these needs and ensure long-term access to study sites.