Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site Summary
2.2. Input Data
2.2.1. Daymet Data
2.2.2. MODIS Data
2.2.3. AmeriFlux Data
2.3. GPP Modeling Approach
2.4. Calibration and Validation
- (1)
- Field Year Selection. For statistical purposes, 75% of field years were chosen randomly without replacement (i.e., 47 out of the 65 total field years) to calibrate the model (Table 2), to ensure at least one field year was reserved for each crop type for validation purposes for a total of 18 field years (Table 3).
- (2)
- LUEc Calibration. The iterative training process using an R script as described in Nguy-Robertson et al. [13] was used to determine ε0, β, σWscalar, and σPscalar values for irrigated and nonirrigated maize and soybean crops. The training utilized input values from the selected calibration field year flux data, Daymet derived Tavg, VPD, APAR, and Tscalar, and remotely-derived gLAI. The iterative process trained the parameters using a step-by-step method in which each scalar was estimated one at a time. During the iteration in which a parameter is calculated, assumptions are made about the other parameters to mimic optimal field conditions. Once the parameters are calculated, the iterations are repeated using the entire calibration dataset to produce ε0, β, σWscalar, and σPscalar values with corresponding standard deviations. The calibrated dataset includes AmeriFlux-derived GPP, Daymet-derived Tavg, VPD, CC, APAR, and Tscalar, and remotely derived gLAI data. Additionally, gLAImax values for irrigated and rainfed soybean and maize were estimated from MODIS-derived gLAI data (Equations (5) and (6)). From these outputs, scalars were calculated (Equations (8)–(15)).
- (3)
- Daily GPP Estimation. Data from validation field year datasets (Table 3) and parameters derived from the iterative process along with specified constants (Table 4), were used to calculate daily values of the scalars, Cscalar, Wscalar, and Pscalar (Equations (11), (14), and (15)) and APAR (Equation (10)), from which daily GPP values were estimated (Equation (8)).
- (4)
- LUEc Validation. The estimated daily GPP values were compared to the observed AmeriFlux determined daily GPP using linear regression (α = 0.05), with corresponding R2, root mean square error (RMSE), and mean normalized bias (MNB).
3. Results
3.1. All Sites Years Combined
3.2. Maize and Soybean Field Years
4. Discussion
4.1. Daymet-Derived PARin and VPD
4.2. LUEc Scalars
4.3. gLAI Estimates
4.4. APAR Importance in GPP Estimates
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Approximate Field Site Location | AmeriFlux Site | Latitude, Longitude, Elevation | Management | Maize Crop | Soybean Crop | Mean Annual Temperature (°C) | Mean Annual Precipitation [+Irrigation] (mm) | Area (ha) |
---|---|---|---|---|---|---|---|---|
Mead, NE | US-Ne1 | 41.1650° N, 96.4766° W, 361 m | Irrigated | 2002–2013 | -- | 10 | 790 [+242] | 49.8 |
US-Ne2 | 41.1650° N, 96.4700° W, 362 m | Irrigated | 2003–2013 Odd years; 2010, 2012 | 2002–2008 Even years | 10 | 790 [+214] | 53.5 | |
US-Ne3 | 41.1797° N, 96.4396° W, 363 m | Rainfed | 2003–2013 Odd years | 2002–2012 Even years | 10 | 790 | 66.2 | |
Brooks Field, near Ames, IA | US-Br1 | 41.9749° N, 93.6906° W, 313 m | Rainfed | 2005–2011 Odd years | 2006–2010 Even years | 9 | 845 | 30.8 |
US-Br3 | 41.9747° N, 93.6935° W, 313 m | Rainfed | 2006–2010 Even years | 2005–2011 Odd years | 9 | 845 | 18.1 | |
Bondville, IL | US-Bo1 | 40.0062° N, 88.2904° W, 219 m | Rainfed | 2001–2007 Odd years | 2002–2006 Even years | 11 | 991 | 31.7 |
Rosemount, MN | US-Ro1 | 44.7143° N, 93.0898° W; 290 m | Rainfed | 2005–2011 Odd years | 2006–2012 Even years | 6 | 879 | 17.7 |
Calibration Years | ||
---|---|---|
Site | Maize | Soybean |
US-Ne1 (NE—irrigated) | 2002–2005, 2007, 2009–2012 | -- |
US-Ne2 (NE—irrigated) | 2003, 2005, 2007, 2009, 2010, 2013 | 2002, 2004, 2008 |
US-Ne3 (NE) | 2003, 2005, 2011, 2013 | 2006, 2008, 2010, 2012 |
US-Br1 (IA) | 2005, 2007, 2009 | 2006,2008 |
US-Br3 (IA) | 2006, 2010 | 2007, 2009, 2011 |
US-Bo1 (IL) | 2001, 2005, 2007 | 2002, 2004 |
US-Ro1 (MN) | 2005, 2009, 2011 | 2006, 2010, 2012 |
Field Years | 30 | 17 |
Validation Years | ||
---|---|---|
Site | Maize | Soybean |
US-Ne1 (NE—irrigated) | 2006, 2008, 2013 | N/A |
US-Ne2 (NE—irrigated) | 2011, 2012 | 2006 |
US-Ne3 (NE) | 2007, 2009 | 2002, 2004 |
US-Br1 (IA) | 2011 | 2010 |
US-Br3 (IA) | 2008 | 2005 |
US-Bo1 (IL) | 2003 | 2006 |
US-Ro1 (MN) | 2007 | 2008 |
Field Years | 11 | 7 |
Nguy-Robertson et al. [13] | This Study | ||||||
---|---|---|---|---|---|---|---|
Variables | Symbol | Equation | Units | Maize | Soybean | Maize | Soybean |
Derived through iterative process: | |||||||
Maximal light use efficiency | ε0 | (8) | g C mol−1 | 0.526 ± 0.007 | 0.374 ± 0.005 | 0.573 ± 0.002 | 0.407 ± 0.002 |
Sensitivity of ε to diffuse light | β | (11) | unitless | 0.347 ± 0.051 | 0.411 ± 0.056 | 0.181 ± 0.014 | 0.294 ± 0.020 |
Water stress curvature parameter | σWscalar | (14) | kPa | 6 ± 0.25 | 4 ± 0 | 6 ± 0 | 4 ± 0 |
Phenology curvature parameter | σPscalar | (15) | m2 m−2 | 18 ± 4.59 | 18 ± 7.15 | 7 ± 0 | 8 ± 0 |
Calculated | |||||||
Maximal green leaf area index [irrigated] | gLAImax | (10), (15) | m2 m−2 | 4.93 [6.78] | 4.63 [6.15] | 5.04 [5.14] | 3.86 [4.25] |
Constants | |||||||
Light extinction coefficient | k | (10) | unitless | 0.443 | 0.601 | 0.443 | 0.601 |
Minimum temperature for physiological activity | Tmin constant | (1), (13) | °C | 10 | 10 | 10 | 10 |
Maximum temperature for physiological activity | Tmax constant | (1), (13) | °C | 48 | 48 | 48 | 48 |
Optimal temperature for physiological activity | Topt constant | (13) | °C | 28 | 28 | 28 | 28 |
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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
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 StyleMalek-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
APA StyleMalek-Madani, G., Walter-Shea, E. A., Nguy-Robertson, A. L., Suyker, A., & Arkebauer, T. J. (2020). Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data. Remote Sensing, 12(23), 3956. https://doi.org/10.3390/rs12233956