Measuring Plant Photosynthesis from Space to Understand Crop Production on the Ground: The Promise of Solar-Induced Chlorophyll Fluorescence
This post is written by Ying Sun, Cornell University
Projected expansion in food demand –– due to population and income growth, urbanization, and heightened agricultural vulnerability from climate change –– pose serious food security threats to African nations in the coming decades. Remote sensing and crop growth models have been successful in predicting crop yields in famine early warning and emergency response systems. However, accurate, low-cost, and timely estimation of crop yield and production continues to remain a challenge.
Current remote sensing-based approaches for crop yield predictions rely heavily on vegetation greenness indices, such as the normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI). Although often available at high spatial resolution, these indices only provide a general characterization of plant status. In other words, they do not directly measure the most important function of plants––photosynthesis––and thus, crop productivity. Furthermore, the presence of clouds severely curtails remote sensing acquisition of greenness indices, meaning that data are often not usable during the rainy season, the most critical growth period, when they are needed the most.
The recent advent of satellite solar-induced chlorophyll fluorescence (SIF) remote sensing holds great promise for near-real-time crop growth monitoring. SIF is an optical signal emanating from the core of plants’ photosynthetic machinery and contains the most direct functional information about photosynthesis. Satellite SIF is much less sensitive to atmospheric contamination than are conventional greenness measures and can capture crop growth at different phenological stages without the need to filter out much of the cloud-affected periods. Satellite SIF has been demonstrated to map and predict yields in the US Corn Belt much more precisely than conventional greenness indices. However, this potential has not been explored for use in most food-insecure countries of Africa.
In our USAID-funded project, we developed high-resolution near-real-time mapping of SIF to probe crop photosynthesis. Specifically, two high-resolution satellite SIF products at 5 km resolution were developed using machine learning techniques. The first product used a hybrid approach that combines artificial neural network (ANN) algorithms and physiological constraints to gap-fill the native SIF measurements from the orbiting carbon observatory (OCO)-2 satellite mission for the period 2014-2018 at bi-weekly resolution. The physiological constraint is implemented by stratifying times and biome types, considering that the relationship between predictors and response variables are physiologically different with times and biome types. This product is highly consistent with independent airborne measurements at high resolution in the U.S. It is capable of successfully identifying highly productive agricultural sectors in a more spatially explicit way than the original SIF observations directly acquired from satellite instruments in Africa (Figure 1).Figure 1. The spatial map of SIF (with the daily mean denoted as SIF) in Ethiopia from (a) the original observations from GOME-2 onboard MetOp-A, (b) the 5km high resolution product developed in Wen et al. (2019), (c) the original observations from OCO-2 satellite mission, (d) the 5km high resolution product developed in Yu et al. (2019). (a) and (b) are plotted for August 2015, (c) and (d) are plotted for the first bi-weekly of August 2015.
The second SIF product developed under this project is a long-term (2002-present) harmonized SIF dataset at monthly resolution created by fusing multiple satellite SIF products utilizing ANN and Random Forest (RF) methods, respectively, and cumulative probability distribution (CDF) matching. This is the first long-term SIF product available to analysts, overcoming the relatively short lifetime of individual satellite missions that have SIF capability. This product will allow examination of the historical variation of crop activities using SIF that was not previously possible. In addition, we have quantified the data uncertainty of this SIF product to facilitate eventual estimation of the confidence level of indicators predicted using this product as an input. Furthermore, we have established that this developed SIF product can reveal physiological response to drought, enabling the near-real-time characterization of drought events (Figure 2).
In the future, we plan to apply these two SIF products to predict crop yield, malnutrition, and rural poverty indicators in Feed the Future countries and to explore whether it adds value relative to the conventional vegetation indices already in use.Figure 2. The anomaly of SIF in Ethiopia for August 2015.
Wen, J., P. Köhler, G. Duveiller, N.C. Parazoo, L. Yu, C. Y. Chang, and Y. Sun (2019), Long-Term Records of High-Resolution Global Solar-Induced Chlorophyll Fluorescence (SIF) from 2002 to Present by Harmonizing GOME-2 and SCIAMACHY, Remote Sens. Environ. (in review)
Yu, L., J. Wen, C.Y., Chang, C. Frankenberg, Y. Sun (2019), High Resolution Global Contiguous Solar-Induced Chlorophyll Fluorescence (SIF) of Orbiting Carbon Observatory-2 (OCO-2), Geophysical Research Letters 46(3): 1449-1458. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL081109
Guan, K., Berry, J.A., Zhang, Y., Joiner, J., Guanter, L., Badgley, G., Lobell, D.B., 2016. Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence. Glob. Chang. Biol. 22, 716–726.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J.A., Frankenberg, C., Huete, A.R., Zarco-Tejada, P., Lee, J.-E., Moran, M.S., Ponce-Campos, G., Beer, C., Camps-Valls, G., Buchmann, N., Gianelle, D., Klumpp, K., Cescatti, A., Baker, J.M., Griffis, T.J., 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. U. S. A. 111, E1327-33. doi:10.1073/pnas.1320008111