How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?
Abstract
:1. Introduction
- How does the soil texture information drawn from SoilGrids compare to detailed soil texture derived from laboratory-analyzed soil samples taken on 14 conventionally farmed fields across France, Germany, and the Netherlands?
- With regard to the average total biomass of winter wheat at harvest per field: How does the crop model prediction perform when using either analyzed soil texture (in the following: AS) or texture extracted from SoilGrids (in the following: SG) as model input in a standard run (i.e., no ensemble creation, no LAI assimilation), the ensemble mean (i.e., no LAI assimilation), and combined with the assimilation of S2 LAI information based on either of two different approaches (ensemble Kalman filter and weighted mean)?
- Which approach, in combination with AS or SG, reproduces the measured spatial variability of aboveground biomass at harvest within a field better?
2. Materials and Methods
2.1. Experimental Fields and Data Collection
2.2. SoilGrids Soil Information
2.3. Leaf Area Index Estimations
2.4. Crop Model
2.5. Data Assimilation
2.6. Model Run and Evaluation
3. Results
3.1. Comparison of Measured Soil Texture Data and Texture Data from SoilGrids
3.2. Comparison of Simulated vs. Measured Aboveground Biomass
3.3. Assessment of Reproduction of Sub-Field Variability
4. Discussion
4.1. Results of the SoilGrids vs. Measured Soil Texture Comparison
4.2. Results of the Crop Modeling Excersises
- Mean values of the calculated metrics (RMSE, MAPE, Bias) across all fields did not substantially differ between ensemble-based SG and AS approaches (i.e., EM, EnKF, WM), but standard deviations were greater for SG-based approaches. The model’s standard runs (SR, i.e., no assimilation, no ensemble generation), performed worse, and biomass yield was largely overestimated as indicated by the positive bias.These findings show that the average biomass prediction performance based on SG soil information was not inferior to AS soil information prediction performance, but came with a greater prediction uncertainty.
- The simulated sub-field heterogeneity of biomass at harvest did not accurately match the measured one in any of the sites, as no approach showed a congruent performance for any of the metrics. AS-based approaches did not outperform SG-based approaches concerning the reproduction of heterogeneity. The performance of the assimilation approaches were largely site-specific, as we saw the approaches performing differently in the different sites, with no clear approach standing out. To our surprise, EM did not perform substantially worse.
4.3. Limitations of Input Data
4.4. Sentinel-2 LAI Estimations
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A
Country | Growing Season | Field | Location | Cultivar Grown | Planting Date | BMY | GY | HI |
---|---|---|---|---|---|---|---|---|
DE | 2016/2017 | 1 | Central Saxony | Benchmark | 2016-10-19 | 1645 | 912 | 0.55 |
2016-11-30 | ||||||||
DE | 2016/2017 | 2 | Southern Northrine-Westphalia | Jonny | 2016-10-25 | 1455 | 597 | 0.41 |
DE | 2016/2017 | 3 | Northern Hesse | Julius | 2016-10-04 | 1959 | 1023 | 0.52 |
2016-10-28 | ||||||||
DE | 2016/2017 | 4 | Northern Bavaria | RGT Reform | 2016-10-05 | 1737 | 905 | 0.54 |
2016-10-19 | ||||||||
FR | 2016/2017 | 5 | Northwestern Charente | Bologna | 2016-10-29 | 969 | 434 | 0.44 |
2016-11-15 | ||||||||
FR | 2016/2017 | 6 | Northern Oise | Lyrik | 2016-10-03 | 1579 | 868 | 0.54 |
2016-10-18 | ||||||||
NL | 2016/2017 | 7 | Eastern Drenthe | RGT Reform | 2016-10-19 | 1573 | 876 | 0.55 |
2016-11-03 | ||||||||
2016-11-14 | ||||||||
DE | 2017/2018 | 8 | Northern Bavaria | RGT Reform | 2017-11-03 | 1451 | 773 | 0.53 |
JB Asano | ||||||||
DE | 2017/2018 | 9 | RGT Reform | 2017-11-16 | 1640 | 809 | 0.49 | |
JB Asano | ||||||||
DE | 2017/2018 | 10 | Central Saxony | RGT Reform | 2017-09-21 | 1889 | 1008 | 0.53 |
JB Asano | ||||||||
DE | 2017/2018 | 11 | RGT Reform | 2017-10-16 | 1882 | 954 | 0.50 | |
JB Asano | ||||||||
DE | 2017/2018 | 12 | Central Thuringia | JB Asano | 2017-09-19, 2017-10-19 | 1242 | 527 | 0.42 |
RGT Reform | ||||||||
DE | 2017/2018 | 13 | Central Lower Saxony | RGT Reform | 2017-11-03 | 1451 | 808 | 0.55 |
JB Asano | ||||||||
DE | 2017/2018 | 14 | RGT Reform | 2017-10-17 | 1795 | 889 | 0.49 | |
JB Asano |
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Field | Gravel AS | Gravel SG | Sand AS | Sand SG | Silt AS | Silt SG | Clay AS | Clay SG | |
---|---|---|---|---|---|---|---|---|---|
1 | 0–30 | 0.02 | 6.96 | 4.25 | 26.46 | 75.95 | 58.24 | 19.8 | 15.31 |
30–60 | 0.02 | 6.96 | 3.48 | 26.46 | 75.4 | 58.24 | 21.12 | 15.31 | |
60–90 | 0.15 | 7.67 | 3.26 | 25.19 | 76.01 | 58.10 | 20.73 | 16.71 | |
2 | 0–30 | 1.07 | 4.08 | 54.46 | 38.14 | 29.61 | 43.28 | 15.93 | 18.58 |
30–60 | 0.66 | 4.28 | 52.06 | 39.17 | 27.69 | 42.61 | 20.26 | 18.22 | |
60–90 | 0.47 | 5.08 | 56.64 | 38.17 | 25.59 | 43.14 | 17.77 | 18.69 | |
3 | 0–30 | 1.52 | 9.12 | 37.97 | 27.22 | 41.57 | 54.27 | 20.46 | 18.52 |
30–60 | 1.32 | 8.48 | 34.04 | 27.25 | 40.65 | 54.23 | 25.31 | 18.52 | |
60–90 | 3.34 | 9 | 36.18 | 25.22 | 39.79 | 54.78 | 24.02 | 20 | |
4 | 0–30 | 0.92 | 8.46 | 10 | 27.39 | 62.93 | 49.01 | 27.06 | 23.6 |
30–60 | 1.05 | 8.46 | 7.96 | 27.78 | 55.91 | 48.76 | 36.13 | 23.46 | |
60–90 | 1.41 | 9.94 | 8.11 | 26.67 | 59.06 | 48.47 | 32.82 | 24.86 | |
5 | 0–30 | 22.12 | 12.96 | 13.64 | 23.74 | 38.25 | 48.64 | 48.12 | 27.62 |
30–60 | 21.58 | 13.28 | 13.55 | 24.33 | 38.21 | 48.26 | 48.26 | 27.4 | |
6 | 0–30 | 0.23 | 9.89 | 4.75 | 16.94 | 72.3 | 62.18 | 22.96 | 20.88 |
30–60 | 0.05 | 10.29 | 2.65 | 17.71 | 67.15 | 61.36 | 30.2 | 20.93 | |
7 | 0–30 | 0.01 | 4.52 | 80.93 | 80.44 | 10.26 | 15.19 | 8.83 | 4.37 |
30–60 | 0.03 | 4.45 | 78.45 | 80.98 | 12.7 | 14.2 | 8.85 | 4.82 | |
8 | 0–30 | 0.76 | 7.37 | 7.06 | 22.24 | 69.22 | 55.96 | 26.41 | 21.8 |
30–60 | 0.72 | 7.37 | 8.79 | 21.51 | 70.97 | 57.06 | 25.11 | 21.43 | |
60–90 | 0.54 | 8.88 | 13.56 | 19.29 | 75.88 | 57.9 | 19.69 | 22.8 | |
9 | 0–30 | 0.03 | 7.41 | 6.26 | 20 | 65.02 | 58.48 | 28.72 | 21.52 |
30–60 | 0.54 | 8 | 4.82 | 20.3 | 67.7 | 58.48 | 27.48 | 21.23 | |
60–90 | 1.89 | 8.89 | 4.83 | 18.89 | 72.82 | 59.48 | 22.35 | 21.64 | |
10 | 0–30 | 0.41 | 5.83 | 3.64 | 26.78 | 80.7 | 56.1 | 15.66 | 17.12 |
30–60 | 0 | 5.83 | 3.33 | 26.97 | 79.53 | 56.31 | 17.15 | 16.72 | |
60–90 | 0 | 6.52 | 2.56 | 25 | 79.74 | 55.72 | 17.7 | 19.28 | |
11 | 0–30 | 0 | 6.05 | 4.63 | 20 | 84.32 | 62.33 | 11.05 | 17.67 |
30–60 | 0 | 6.33 | 3.02 | 20 | 85.07 | 62 | 11.91 | 18 | |
60–90 | 0 | 6.33 | 2.48 | 18 | 76.82 | 62.29 | 20.71 | 19.71 | |
12 | 0–30 | 2.34 | 6.83 | 12.16 | 21.15 | 42.85 | 52.11 | 44.99 | 26.74 |
30–60 | 1.94 | 7.02 | 10.93 | 21.64 | 48.1 | 51.89 | 40.97 | 26.47 | |
60–90 | 4.01 | 7.62 | 9.1 | 18.73 | 52.17 | 53.74 | 38.73 | 27.53 | |
13 | 0–30 | 0.2 | 6.24 | 2.8 | 18.34 | 82.3 | 63.95 | 14.91 | 17.71 |
30–60 | 0 | 6.13 | 2.06 | 19 | 81.82 | 63.37 | 16.12 | 17.63 | |
60–90 | 0 | 7.45 | 1.49 | 17.55 | 83.88 | 64.24 | 14.64 | 18.21 | |
14 | 0–30 | 0.25 | 6.19 | 4.47 | 17.26 | 84.2 | 61.91 | 11.33 | 20.84 |
30–60 | 0 | 6.19 | 2.9 | 18.56 | 83.76 | 61.05 | 13.33 | 20.4 | |
60–90 | 0 | 7.51 | 1.98 | 16.86 | 81.27 | 61.74 | 16.75 | 21.4 |
Clay Content | Gravel Content | Plant-Available Water Content | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Field | R | RMSE | Bias | R | RMSE | Bias | R | RMSE | Bias | AWC AS (%) | AWC SG (%) |
1 | −0.05 | 5.14 | −2.27 | −0.01 | 9.58 | 9.39 | −0.21 | 0.03 | −0.03 | 0.17 | 0.14 |
2 | 0.49 | 5.27 | 0.81 | −0.22 | 5.16 | 4.95 | −0.22 | 0.01 | −0.01 | 0.124 | 0.133 |
3 | 0.45 | 5.67 | −2.59 | 0.16 | 12.05 | 10.42 | 0.53 | 0.01 | 0 | 0.133 | 0.13 |
4 | 0.24 | 11.65 | −5.93 | −0.02 | 10.70 | 10.37 | −0.12 | 0.02 | −0.01 | 0.135 | 0.122 |
5 | 0.00 | 17.61 | −16.25 | −0.05 | 8.91 | −7.28 | 0.22 | 0.05 | 0.05 | 0.184 | 0.112 |
6 | 0.67 | 6.17 | −4.87 | 0.05 | 9.94 | 9.92 | 0.27 | 0.02 | −0.02 | 0.15 | 0.134 |
7 | −0.17 | 5.13 | −4.07 | 0.03 | 4.30 | 4.28 | 0.09 | 0.04 | 0.04 | 0.106 | 0.145 |
8 | −0.60 | 4.75 | 0.67 | −0.21 | 10.26 | 10.04 | 0.34 | 0.03 | −0.03 | 0.157 | 0.13 |
9 | −0.53 | 6.08 | −2.38 | 0.55 | 10.13 | 9.99 | −0.11 | 0.02 | −0.02 | 0.152 | 0.133 |
10 | 0.31 | 4.44 | 3.77 | −0.53 | 8.39 | 8.18 | 0.27 | 0.04 | −0.04 | 0.177 | 0.138 |
11 | 0.84 | 7.40 | 6.66 | NA | 9.83 | 9.52 | 0.52 | 0.04 | −0.04 | 0.184 | 0.142 |
12 | −0.04 | 14.16 | −13.20 | 0.27 | 7.09 | 6.64 | 0.71 | 0.02 | 0.02 | 0.107 | 0.123 |
13 | 0.33 | 4.01 | 3.80 | −0.63 | 8.38 | 8.27 | 0.60 | 0.04 | −0.04 | 0.183 | 0.142 |
14 | 0.59 | 8.14 | 7.89 | −0.53 | 7.95 | 7.89 | −0.01 | 0.05 | −0.05 | 0.186 | 0.135 |
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Tewes, A.; Hoffmann, H.; Nolte, M.; Krauss, G.; Schäfer, F.; Kerkhoff, C.; Gaiser, T. How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level? Remote Sens. 2020, 12, 925. https://doi.org/10.3390/rs12060925
Tewes A, Hoffmann H, Nolte M, Krauss G, Schäfer F, Kerkhoff C, Gaiser T. How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level? Remote Sensing. 2020; 12(6):925. https://doi.org/10.3390/rs12060925
Chicago/Turabian StyleTewes, Andreas, Holger Hoffmann, Manuel Nolte, Gunther Krauss, Fabian Schäfer, Christian Kerkhoff, and Thomas Gaiser. 2020. "How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?" Remote Sensing 12, no. 6: 925. https://doi.org/10.3390/rs12060925