Tracking Crop Leaf Area Index and Chlorophyll Content Using RapidEye Data in Northern Ontario, Canada

Wednesday, 17 December 2014
Jiali Shang1, Jiangui Liu1, Baoluo Ma1, Ting Zhao1, John Kovacs2, Xianfeng Jiao3, Taifeng Dong1, Ted Huffman1, Xiaoyuan Geng1 and Dan Walters2, (1)Agriculture and Agri-Food Canada, Ottawa, ON, Canada, (2)Nipissing University, Geography, North Bay, ON, Canada, (3)Natural Resources Canada, CCRS, Ottawa, ON, Canada
Information on crop phenological state such as flowering, maturing, drying, senescence, and harvesting is essential for crop production surveillance and yield prediction. Earth Observation data provide an important information source for monitoring crop development at various temporal and spatial scales. In particular, the availability of many high-spatial-resolution space sensors offers a powerful tool for precision farming. This study reports the results of a two-year (2012, 2013) study over spring wheat and canola fields using six different vegetation indices derived from the high-resolution (6.5m) RapidEye optical satellite data in northern Ontario, Canada. The study revealed that for both wheat and canola, significant relationships were observed between the ground-derived leaf area index (LAI) and all 6 vegetation indices tested. For spring wheat, the strongest relationship was found between LAI and the Modified Triangular Vegetation Index 2 (MTVI2), with a coefficient of determination (R2) of 0.95. For canola, a R2 of 0.92 was achieved. Strong relationships were also found between all six vegetation indices and the chlorophyll concentration index (CCI) measured in the fields using a CCM-200 device. The strongest correlation exists between CCI and the ratio of Modified the Chlorophyll Absorption Reflected Index (MCARI) and the Optimized Soil Adjusted Vegetation Index (OSAVI), with an R2 of 0.86. It suggests that RapidEye data can be used to track field-scale crop LAI and monitor crop chlorophyll content.