Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems

Michael Marshall* (Corresponding Author), Kevin Tu, Jesslyn Brown

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

44 Citations (Scopus)
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Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. Although PEMs have many advantages over other crop yield models, they are not widely used, because performance is relatively poor. Here, a new PEM is presented that addresses deficiencies for macro-scale application: Production Efficiency Model Optimized for Crops (PEMOC). It was developed by optimizing functions from the literature with GPP estimated by eddy covariance flux towers in the United States. The model was evaluated using newly developed Earth observation products and county-level yield statistics for major crops. PEMOC generally performed better at the field and county level than another commonly used PEM, the Moderate Resolution Imaging Spectroradiometer GPP (MOD17). PEMOC and MOD17 estimates of GPP had an R 2 and root mean squared error (RMSE) over the growing season of 0.71–0.89 (9.87–17.47 g CO 2 d −1) and 0.59–0.83 (6.86–22.20 g CO 2 d −1) with flux tower GPP. PEMOC produced R 2s and RMSE of 0.70 (0.52), 0.60 (0.61), and 0.62 (0.59), while MOD17 produced R 2s and RMSE of 0.65 (0.57), 0.53 (0.66), and 0.65 (0.57) with corn, soybean, and winter wheat crop yield anomalies. The sample size of rice was small, so yields were compared directly. PEMOC and MOD17 produced R 2s and RMSE of 0.53 (3.42 t ha −1) and 0.40 (4.89 t ha −1). The most sizeable model improvements were seen for C 3 and C 4 crops during emergence/senescence and peak season, respectively. These improvements were attributed to C 3 and C 4 partitioning, optimized temperature and moisture constraints, and an evapotranspiration-based soil moisture index.

Original languageEnglish
Pages (from-to)258-271
Number of pages14
JournalRemote sensing of environment
Early online date24 Aug 2018
Publication statusPublished - 1 Nov 2018




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