Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
2.2.1. Meteorological Data
2.2.2. Agro-Meteorological Data
2.2.3. Satellite Soil Moisture
3. Methods
3.1. Crop Model Description and Verification
3.2. Assimilation Algorithms
3.3. Data Assimilation Implementation
3.4. Agricultural Drought Index
3.5. Evaluation Criteria
4. Results
4.1. Comparisons of Open-Loop and Assimilated Soil Moisture
4.1.1. Soil Moisture at Different Depths
4.1.2. Root-Zone Soil Moisture
4.2. Impacts of Different Strategies on Data Assimilation
4.2.1. Impact of Simultaneous State-Parameter Estimation
4.2.2. Impact of Ensemble Size
4.2.3. Impact of Temporal Interval
4.3. Effect of Data Assimilation on Agricultural Drought Monitoring
4.3.1. Comparison with Observed Soil Moisture-Based Drought Index
4.3.2. Comparison with Winter Wheat Yield
5. Discussion
6. Conclusions
- (1)
- Compared with open-loop simulations, crop growth model-based data assimilation effectively improved soil moisture estimation throughout the soil profile. Soil moisture in the root zone was more consistent with in situ observation (RMSE = 0.035 m3·m−3, R = 0.742) than that in other soil layers;
- (2)
- Simultaneous state-parameter estimation (based on AEnKF algorithm) performed better than state-only estimation (based on EnKF algorithm). As the observation frequency and ensemble size increased, the accuracy of soil moisture estimates also increased. In the case of a comprehensive consideration of estimation accuracy and calculation cost, observation frequency within five days with twenty ensemble members could meet the requirement of soil moisture accuracy;
- (3)
- Compared with the drought index based on open-loop soil moisture, the drought index based on assimilated results improves at least one drought level in agricultural drought monitoring. Additionally, the drought index based on assimilated soil moisture was better correlated with winter wheat yield than that based on open-loop simulation.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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RSMI Value | Drought Level |
---|---|
−1 to 0 | Less intense |
−2 to −1 | Moderate |
−3 to −2 | High intense |
−4 to −3 | Severe |
−4 or less | Extreme |
10 cm | 20 cm | 30 cm | 40 cm | 50 cm | Root Zone | |
---|---|---|---|---|---|---|
-RMSE (root mean square error, m3·m−3) | ||||||
OL | 0.084 | 0.087 | 0.088 | 0.092 | 0.066 | 0.067 |
DA | 0.048 | 0.031 | 0.074 | 0.060 | 0.043 | 0.038 |
-R (correlation coefficient) | ||||||
OL | 0.685 | 0.531 | 0.546 | 0.503 | 0.514 | 0.664 |
DA | 0.598 | 0.603 | 0.644 | 0.650 | 0.499 | 0.671 |
-Eff (effectiveness, %) | ||||||
66.875 | 87.389 | 27.947 | 57.882 | 56.037 | 67.336 |
Sites | OL | DA | ||||||
---|---|---|---|---|---|---|---|---|
10 cm | 20 cm | 50 cm | Root zone | 10 cm | 20 cm | 50 cm | Root Zone | |
-RMSE (m3·m−3) | ||||||||
AHSX | 0.056 | 0.066 | 0.059 | 0.048 | 0.050 | 0.038 | 0.040 | 0.032 |
HBBZ | 0.084 | 0.087 | 0.066 | 0.067 | 0.048 | 0.031 | 0.043 | 0.038 |
HBHH | 0.101 | 0.090 | 0.024 | 0.062 | 0.053 | 0.046 | 0.027 | 0.031 |
HNXX | 0.073 | 0.066 | 0.044 | 0.045 | 0.046 | 0.047 | 0.039 | 0.033 |
SDDZ | 0.083 | 0.093 | 0.052 | 0.062 | 0.044 | 0.056 | 0.050 | 0.039 |
SDHM | 0.129 | 0.140 | 0.074 | 0.105 | 0.046 | 0.054 | 0.050 | 0.040 |
SDJX | 0.094 | 0.089 | 0.045 | 0.059 | 0.043 | 0.045 | 0.042 | 0.031 |
SDTA | 0.058 | 0.077 | 0.064 | 0.048 | 0.040 | 0.041 | 0.063 | 0.035 |
All sites | 0.088 | 0.091 | 0.056 | 0.065 | 0.047 | 0.046 | 0.045 | 0.035 |
-R | ||||||||
AHSX | 0.688 | 0.681 | 0.537 | 0.738 | 0.715 | 0.688 | 0.527 | 0.758 |
HBBZ | 0.685 | 0.531 | 0.514 | 0.664 | 0.598 | 0.603 | 0.499 | 0.671 |
HBHH | 0.502 | 0.583 | 0.908 | 0.717 | 0.643 | 0.698 | 0.880 | 0.810 |
HNXX | 0.526 | 0.357 | 0.183 | 0.513 | 0.564 | 0.374 | 0.260 | 0.531 |
SDDZ | 0.695 | 0.524 | 0.378 | 0.628 | 0.708 | 0.530 | 0.354 | 0.617 |
SDHM | 0.355 | 0.174 | 0.399 | 0.312 | 0.366 | 0.326 | 0.473 | 0.487 |
SDJX | 0.582 | 0.538 | 0.212 | 0.639 | 0.670 | 0.566 | 0.222 | 0.656 |
SDTA | 0.692 | 0.577 | 0.168 | 0.658 | 0.763 | 0.647 | 0.076 | 0.652 |
All sites | 0.541 | 0.442 | 0.545 | 0.579 | 0.669 | 0.629 | 0.666 | 0.742 |
Sites | 10 cm | 20 cm | 50 cm | Root Zone | ||||
-Eff (%) | ||||||||
AHSX | 19.270 | 67.425 | 54.866 | 56.319 | ||||
HBBZ | 66.875 | 87.389 | 56.037 | 67.336 | ||||
HBHH | 73.014 | 74.341 | −25.365 | 74.744 | ||||
HNXX | 60.265 | 50.033 | 22.175 | 46.646 | ||||
SDDZ | 71.841 | 63.894 | 9.073 | 60.075 | ||||
SDHM | 87.311 | 85.031 | 54.002 | 85.467 | ||||
SDJX | 78.943 | 74.336 | 13.335 | 73.261 | ||||
SDTA | 53.083 | 71.068 | 4.587 | 46.338 | ||||
All sites | 71.346 | 74.348 | 33.450 | 71.197 |
AHSX | HBBZ | HBHH | HNXX | SDDZ | SDHM | SDJX | SDTA | Mean | |
---|---|---|---|---|---|---|---|---|---|
OL | 2.31 (0.74) | 3.49 (0.65) | 2.81 (0.70) | 2.12 (0.50) | 2.19 (0.63) | 3.61 (0.76) | 2.35 (0.31) | 1.95 (0.66) | 2.60 (0.62) |
DA | 1.53 (0.75) | 1.31 (0.74) | 1.54 (0.80) | 1.52 (0.55) | 1.3 (0.63) | 1.29 (0.78) | 1.15 (0.50) | 1.43 (0.66) | 1.38 (0.68) |
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Zhou, H.; Geng, G.; Yang, J.; Hu, H.; Sheng, L.; Lou, W. Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring. Remote Sens. 2022, 14, 3187. https://doi.org/10.3390/rs14133187
Zhou H, Geng G, Yang J, Hu H, Sheng L, Lou W. Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring. Remote Sensing. 2022; 14(13):3187. https://doi.org/10.3390/rs14133187
Chicago/Turabian StyleZhou, Hongkui, Guangpo Geng, Jianhua Yang, Hao Hu, Li Sheng, and Weidong Lou. 2022. "Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring" Remote Sensing 14, no. 13: 3187. https://doi.org/10.3390/rs14133187