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Leaf area index from CHRIS satellite data and applications in plant yield estimation 1

In situ weather sensors provide temporally dense but spatially sparse information. Conversely, remote sensing data tend to provide spatially dense but temporally sparse information. The fusion of weather, soil, and remote sensing data in state-of-the-art plant growth models provides a powerful tool for estimating plant biomass and yield, and for monitoring the impacts of drought or management practices on a local or regional level. In 2003, a two-year project funded in part by Precarn Incorporated and by its participants, MDA, Natural Resources Canada (NRCan), Agriculture and Agri-food Canada (AAFC), York University, and Radarsat International (RSI) commenced to develop an intelligent sensorweb system for yield prediction in agricultural crops and rangeland. Two sites were selected in Alberta, a wheat field and rangeland. In situ instrumentation was installed in the spring of 2004 to record soil moisture to a variety of depths, soil temperature, air temperature, rainfall, incoming solar radiation, and wind speed and direction, among other variables. Throughout the season, data from the Compact High Resolution Imaging Spectrometer (CHRIS) were acquired to derive leaf area index information. Ground data, including biomass and leaf area index, were collected monthly at the rangeland site and weekly or tri-weekly at the crop site. A good relationship was observed between ground-based and remote sensing derived leaf area index in the case of wheat (r > 0.7). The correlation was poorer in the case of rangeland, probably due to the low productivity of the system and the presence of litter. Preliminary conclusions are that in wheat, remote sensing data may be useful for extrapolating plant models in space, whereas in rangeland the impact of litter needs to be evaluated.