NASA Harvest partners at The University of Illinois at Urbana-Champaign and the Illinois Corn Growers Association as well as colleagues from the Illinois Farm Business and Farm Management Associations published their work on improvements for Leaf area index (LAI) datasets, which are commonly used in estimates of crop productivity as well as defining crop growth conditions. Found in Remote Sensing of Environment, “Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data” looks at the use of data fusion techniques in light of better availability of quality satellite data. Although LAI algorithms have seen improvements in recent years, these datasets are in need of spatial and temporal resolution enhancements in order to be sufficiently useful for agricultural tools and applications.
New LAI estimates have been created for the U.S. Corn Belt’s average agricultural farmland as a result of this work by combining available satellite remote sensing data, STAIR fusion, and Planet Labs’ CubeSat data. Both the STAIR fused data, which is a combination of MODIS and Landsat data, and the CubeSat data (reprocessed by the investigators) have fine spatial resolution and high frequency of collection. When estimating LAI from these datasets, the team weighed the statistics against ground-truthed LAI data from over 30 sites in the study area, confirming that their estimates were reliable when using the combination of satellite data. Based on these high-frequency and high-spatial-resolution LAI datasets, the team concludes that their suggested LAI estimation methods have useful applications in the U.S. Corn Belt region and can augment current crop monitoring methods.
Dr. Guan and his team contribute to NASA Harvest activities by focusing on research efforts that have potential to strengthen engagement with U.S. farmers. Compared to previously-available LAI products, the new LAI estimates derived from this research reveal significantly more information on spatial and temporal variations which can be practically applied by farmers to further enhance precision agriculture and agricultural resource management. Farmers can also effectively make use of this increased LAI detail because this information calibrates sub-field scale variability in the rate of crop growth and even classifies crops that are growing at a slower rate. This is especially useful to farmers because they can examine sub-field crop stress and make improvements in field management, ultimately increasing their yield products.
Read the full publication in Remote Sensing of Environment.