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Article

Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data

1
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583-0961, USA
2
Department of Agronomy & Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0817, USA
*
Author to whom correspondence should be addressed.
Present address: Olsson, Lincoln, NE 68502, USA.
Remote Sens. 2020, 12(23), 3956; https://doi.org/10.3390/rs12233956
Received: 26 October 2020 / Revised: 29 November 2020 / Accepted: 30 November 2020 / Published: 3 December 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m−2 d−1) over the original study in which in situ data were used (RMSE = 2.6 g C m−2 d−1). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R2 = 0.80 and RMSE of 3.5 g C m−2 d−1). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP. View Full-Text
Keywords: gross primary production (GPP); light use efficiency (LUE); AmeriFlux; U.S. Corn Belt; gridded weather data gross primary production (GPP); light use efficiency (LUE); AmeriFlux; U.S. Corn Belt; gridded weather data
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MDPI and ACS Style

Malek-Madani, G.; Walter-Shea, E.A.; Nguy-Robertson, A.L.; Suyker, A.; Arkebauer, T.J. Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data. Remote Sens. 2020, 12, 3956. https://doi.org/10.3390/rs12233956

AMA Style

Malek-Madani G, Walter-Shea EA, Nguy-Robertson AL, Suyker A, Arkebauer TJ. Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data. Remote Sensing. 2020; 12(23):3956. https://doi.org/10.3390/rs12233956

Chicago/Turabian Style

Malek-Madani, Gunnar, Elizabeth A. Walter-Shea, Anthony L. Nguy-Robertson, Andrew Suyker, and Timothy J. Arkebauer. 2020. "Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data" Remote Sensing 12, no. 23: 3956. https://doi.org/10.3390/rs12233956

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