A new space-borne perspective of crop productivity variations over the US Corn Belt

Somkuti, P., Boesch, H., Feng, L., Palmer, P. I., Parker, R. J. and Quaife, T. ORCID: https://orcid.org/0000-0001-6896-4613 (2020) A new space-borne perspective of crop productivity variations over the US Corn Belt. Agricultural and Forest Meteorology, 281. 107826. ISSN 0168-1923 doi: 10.1016/j.agrformet.2019.107826

Abstract/Summary

Remotely-sensed solar-induced chlorophyll fluorescence (SIF) provides a means to assess vegetation productivity in a more direct way than via the greenness of leaves. SIF is produced by plants alongside photosynthesis so it is generally thought to provide a more direct probe of plant status. We analyze inter-annual variations of SIF over the US Corn Belt using a seven-year time series (2010–2016) retrieved from measurements of short-wave IR radiation collected by the Japanese Greenhouse gases Observing SATellite (GOSAT). Using survey data and annual reports from the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS), we relate anomalies in the GOSAT SIF time series to meteorological and climatic events that affected planting or growing seasons. The events described in the USDA annual reports are confirmed using remote sensing-based data such as land surface temperature, precipitation, water storage anomalies and soil moisture. These datasets were carefully collocated with the GOSAT footprints on a sub-pixel basis to remove any effect that could occur due to different sampling. We find that cumulative SIF, integrated from April to June, tracks the planting progress established in the first half of the planting season (Pearson correlation r > 0.89). Similarly, we show that crop yields for corn (maize) and soybeans are equally well correlated to the integrated SIF from July to October (r > 0.86). Our results for SIF are consistent with reflectance-based vegetation indices, that have a longer established history of crop monitoring. Despite GOSAT’s sparse sampling, we were able to show the potential for using satellite-based SIF to study agriculturally-managed vegetation.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/87016
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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