Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources
Abstract
:1. Introduction
2. Materials and Methods
2.1. Retrieval of Biophysical and Agronomical Variables
2.1.1. Coupling EO Data with Crop Models for Yield Prediction
2.1.2. Cropping Seasons and Input Variables
- A spline interpolation was used to fill the missing dates with LAI values to ensure continuous data throughout the entire growing season.
- A 4253H twice filter was used as a form of moving average to smooth LAI values.
- A double logistic curve was fit to provide a more realistic representation of the field-wise LAI curves. LAI fit following [29,30,31] were visually inspected to select the best fitting method that gave the most realistic LAI curves given the satellite data available. The equation reported by [31] was finally selected.
2.1.3. SAFYsw Model Calibration and Data Assimilation
2.2. Crop Diseases Detection Methods
2.2.1. Orchards Diseases Detection
2.2.2. Wheat Yellow Rust Detection
3. Results
3.1. Coupling EO Data with Crop Models for Yield Prediction
3.2. Crop Diseases Results
3.2.1. Orchards Diseases Mapping
3.2.2. Wheat Yellow Rust Mapping
4. Discussion
5. Conclusions
- (a)
- Expand the estimation of new variables, by exploring the RTM code PROSPECT-PRO (e.g., for proteins and nitrogen);
- (b)
- Exploit the newly operative hyperspectral missions for their application on a regional base to detect pathogens;
- (c)
- The implementation of machine/deep learning techniques for the generalization of the pre-operative chains applicable to the local scale for agricultural applications.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Simple Algorithm for Yield Estimates (SAFY)
Appendix B. The Ensemble Kalman Filter (EnKF) Assimilation Procedure
- Initialization of the model ensemble: an ensemble of N model simulations (N = 100 in our case) was generated. Each element of the ensemble was obtained by creating a different set of model parameters. Some parameters were considered fixed (Table 1), while for the calibrated parameters a value was randomly sampled from a truncated gaussian distribution with fixed average and lower and upper limits corresponding to the range obtained during the calibration (Table 1). This perturbation allows for the simulation of the error covariance.
- Forecast step: a simulation was run with a daily timestep separately for each element of the model ensemble, till the day of observation, to obtain an ensemble of simulated LAI values. The error covariance of the forecast can be calculated from the ensemble as in Equation (A12):(PE)tf = (Xt − E|Xt|)∙(Xt − E|Xt|)T/(N − 1),
- Observation error propagation: an ensemble of N LAI observations was generated for each day with a LAI observation (in our case, each day of the growth cycle, due to the LAI fitting procedure). The ensemble was obtained by randomly sampling from a gaussian distribution with an average equal to the EO-derived LAI value and a standard deviation of 10%. Similarly, to the simulated values, the error covariance of the observation can be calculated from the ensemble as in Equation (A13):(RE)tf = (Yt − E|Yt|)∙(Yt − E|Yt|)T/(N − 1),
- Update step: the mean state and the model state covariances are updated using the Kalman gain (for each ensemble element) following Equation (A14):Kt = Ptf∙HT∙(H∙Ptf∙HT + Rt)−1,The mean state was (Equation (A15)):xta = xtf + Kt∙(yt − Hxtf),The model state covariance at time t (Pta) is expressed by Equation (A16):Pta = (I − Kt∙H)∙Ptf,
- The forecast, observation error propagation and update steps were repeated recursively till the end of the simulation, assimilating each new observation.
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Parameter | Meaning | Mean | Range 1 | Source |
---|---|---|---|---|
Crop growth parameters | ||||
εC | Climatic efficiency | 0.48 | [13,17] | |
εl | Light interception coefficient | 0.5 | [13,17] | |
SLA | Specific leaf area (m2·g−1) | 0.22 | [13,17] | |
Topt | Optimum temperature for development (°C) | 20 | [13,17] | |
Tmin | Minimum temperature for development (°C) | 0 | [13,17] | |
Tmax | Maximum temperature for development (°C) | 37 | [13,17] | |
DAM0 | Initial dry aboveground biomass (g·m−2) | 4.55 | LAI/SLA with LAI = 0.1 [13,17] | |
D0 | Emergence date (day of year) | 354 | Varies on visual inspection of LAI curve | |
ELUE | Effective light use efficiency (g·MJ−1) | 3.45 | 2.9–4.0 | Calibrated |
Py | Partition to grain (grain filling rate) | 0.0049 | 0.0028–0.007 | Calibrated |
Phenology parameters | ||||
PLa | Parameter A of partition to leaf | 0.121 | 0.067–0.175 | Calibrated |
PLb | Parameter B of partition to leaf | 0.0025 | 0.002–0.003 | Calibrated |
STT | Growing degree days (GDD) to senescence | 1225 | 850–1600 | Calibrated |
RS | Rate of senescence (GDD·day−1) | 20,500 | 8000–33,000 | Calibrated |
Soil parameters | ||||
RootRatio | Root weight to length ratio (cm·g−1) | 0.98 | [41] | |
RtGrtRate | Root growth rate (cm·GDD−1) | 0.22 | [41] | |
MxRDP | Maximum root depth (cm) | 120 | [41] | |
Rnff | Runoff factor (ratio to total rainfall) | 0.15 | [41] | |
SALB | Soil albedo | 0.16 | [41] | |
SWCON | Profile drainage coefficient (soil water conductivity) | 0.90 | [41] | |
MxRWU | Max root water uptake (cm3 water·cm−1 root) | 0.0035 | [41] | |
SAT | Saturation water content (cm3·cm−3) | 0.479 | Pedotransfer from ESDAC–JRC soil maps. Differences of values for the same soil water limit across fields were minimal (< 0.03 cm3·cm−3) | |
DUL | Drained upper limit (cm3·cm−3) | 0.240 | ||
LL | Lower limit (cm3·cm−3) | 0.115 | ||
AirDry | Residual humidity (cm3·cm−3) | 0.055 |
Simulation | RMSE t·ha−1 | RRMSE % | MAE % |
---|---|---|---|
With data assimilation | 1.42 | 31.7 | 1.14 |
Open loop | 4.42 | 97.3 | 3.97 |
Training Confusion Matrix | Validation Confusion Matrix | |||||
---|---|---|---|---|---|---|
Output class 1 | 36.3% | 3.8% | 90.6% | 25.0% | 8.8% | 74.1% |
Output class 2 | 5.4% | 54.6% | 91.0% | 5 6.3% | 48 60.0% | 90.6% |
87.0% | 93.6% | 85.0% | 80.0% | 87.3% | 85.0% | |
Target class 1 | Target class 2 | AO | Target class 1 | Target class 2 | AO | |
Test Confusion Matrix | All Confusion Matrix | |||||
Output class 1 | 22.5% | 10.0% | 69.2% | 31.3% | 6.0% | 83.9% |
Output class 2 | 5.0% | 62.5% | 92.6% | 5.5% | 57.3% | 91.2% |
81.8% | 86.2% | 85.0% | 85.0% | 90.5% | 88.5% | |
Target class 1 | Target class 2 | AO | Target class 1 | Target class 2 | AO |
Training Confusion Matrix | Validation Confusion Matrix | |||||
---|---|---|---|---|---|---|
Output class 1 | 59.5% | 5.4% | 91.7% | 61.5% | 5.4% | 92.0% |
Output class 2 | 2.1% | 33.1% | 94.2% | 5.4% | 27.7% | 83.7% |
96.7% | 86.0% | 92.6% | 92.0% | 83.7% | 89.2% | |
Target class 1 | Target class 2 | OA | Target class 1 | Target class 2 | OA | |
Test Confusion Matrix | All Confusion Matrix | |||||
Output class 1 | 52.3% | 2.3% | 95.8% | 31.3% | 6.0% | 83.9% |
Output class 2 | 3.8% | 41.5% | 91.5% | 5.5% | 57.3% | 91.2% |
93.2% | 94.7% | 93.8% | 85.0% | 90.5% | 88.5% | |
Target class 1 | Target class 2 | OA | Target class 1 | Target class 2 | OA |
Training Confusion Matrix | Validation Confusion Matrix | |||||
Output class 1 | 58.5% | 5.4% | 95.8% | 63.1% | 1.5% | 97.6% |
Output class 2 | 2.3% | 36.7% | 94.1% | 0.0% | 35.4% | 100% |
96.2% | 93.5% | 95.1% | 100.0% | 95.8% | 98.5% | |
Target class 1 | Target class 2 | OA | Target class 1 | Target class 2 | OA | |
Test Confusion Matrix | All Confusion Matrix | |||||
Output class 1 | 60.0% | 3.1% | 95.1% | 59.7% | 2.5% | 96.0% |
Output class 2 | 2.3% | 34.6% | 93.8% | 1.8% | 36.0% | 95.1% |
96.3% | 91.8% | 94.6% | 97.0% | 93.6% | 95.7% | |
Target class 1 | Target class 2 | OA | Target class 1 | Target class 2 | OA |
Confusion Matrix with 12 Initial VIs | Confusion Matrix with Extended List of VIs | |||
---|---|---|---|---|
Output class 1 | 58.5% | 4.8% | 59.7% | 2.5% |
Output class 2 | 3.1% | 33.7% | 1.8% | 36.0% |
Target class 1 | Target class 2 | Target class 1 | Target class 2 |
Confusion Matrix with 12 Initial VIs | |||
---|---|---|---|
Output class 1 | 58.5% | 4.8% | 92.5% |
Output class 2 | 3.1% | 33.7% | 91.6% |
95% | 87.6% | 92.2% | |
Confusion Matrix with Extended List of VIs | |||
Output class 1 | 59.7% | 2.5% | 96.0% |
Output class 2 | 1.6% | 36.0% | 95.1% |
97% | 93.6% | 95.7% | |
Target class 1 | Target class 2 | OA |
Feature | Class | Classification Accuracy (%) | ||||||
---|---|---|---|---|---|---|---|---|
7 DAI | 14 DAI | 21 DAI | 28 DAI | 31 DAI | 34 DAI | 41 DAI | ||
WRSFs | Healthy | 73.5 | 81.2 | 88.6 | 95.4 | 96.9 | 95.2 | 96.1 |
Infected | 80.5 | 84.8 | 79.8 | 92.7 | 98.2 | 98.4 | 98.5 |
Model | State | Classification Accuracy/% | ||||||
---|---|---|---|---|---|---|---|---|
7 DAI | 14 DAI | 21 DAI | 28 DAI | 31 DAI | 34 DAI | 41 DAI | ||
KPCA-SVM | Healthy | 88.7 | 92.4 | 97.5 | 99.2 | 98.8 | 96.7 | 98.9 |
Infected | 84.2 | 90.1 | 95.3 | 97.9 | 100 | 100 | 98.2 |
Healthy Wheat | Yellow Rust | User’s Accuracy (%) | Overall Accuracy (%) | Kappa Coefficient | |
---|---|---|---|---|---|
Healthy wheat | 16 | 4 | 80 | 84.6 | 0.742 |
Yellow rust | 2 | 17 | 89.5 | ||
Producer’s accuracy (%) | 88.9 | 81 |
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Pignatti, S.; Casa, R.; Laneve, G.; Li, Z.; Liu, L.; Marzialetti, P.; Mzid, N.; Pascucci, S.; Silvestro, P.C.; Tolomio, M.; et al. Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources. Remote Sens. 2021, 13, 2889. https://doi.org/10.3390/rs13152889
Pignatti S, Casa R, Laneve G, Li Z, Liu L, Marzialetti P, Mzid N, Pascucci S, Silvestro PC, Tolomio M, et al. Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources. Remote Sensing. 2021; 13(15):2889. https://doi.org/10.3390/rs13152889
Chicago/Turabian StylePignatti, Stefano, Raffaele Casa, Giovanni Laneve, Zhenhai Li, Linyi Liu, Pablo Marzialetti, Nada Mzid, Simone Pascucci, Paolo Cosmo Silvestro, Massimo Tolomio, and et al. 2021. "Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources" Remote Sensing 13, no. 15: 2889. https://doi.org/10.3390/rs13152889
APA StylePignatti, S., Casa, R., Laneve, G., Li, Z., Liu, L., Marzialetti, P., Mzid, N., Pascucci, S., Silvestro, P. C., Tolomio, M., Upreti, D., Yang, H., Yang, G., & Huang, W. (2021). Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources. Remote Sensing, 13(15), 2889. https://doi.org/10.3390/rs13152889