An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Field-Observed Data
2.2.2. Remotely Sensed Data
2.3. WOFOST Model and EnKF Method
2.4. Data Assimilation Framework
3. Results
3.1. Assimilation of LAI and SM into WOFOST Model at 500 m Grid Size
3.2. Assimilation of LAI and SM into WOFOST Model at 10 m Grid Size
3.3. Comparative Analysis of Different Assimilation Strategies at Two Spatial Scales
4. Discussion
4.1. Potential for Estimating Crop Yield by Integrating Remotely Sensed LAI and SM with Crop Model
4.2. Potential Sources of Errors in the Assimilation of LAI and SM into the WOFOST Model
4.3. Future Directions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AGB | Above Ground Biomass |
CC | Canopy Cover |
CDMA | Crop Data-Model Assimilation |
CGM | Crop Growth Model |
DA | Data Assimilation |
DSSAT | Decision Support System for Agrotechnology Transfer |
EnKF | Ensemble Kalman Filter |
LAI | Leaf Area Index |
MCMC | Markov-Chain Monte Carlo |
MODIS | MODerate resolution Imaging Spectrometer |
OL | Open Loop |
RE | Relative Error |
RMSE | Root Mean Squared Error |
RS | Remote Sensing |
SAR | Synthetic Aperture Radar |
SM | Soil Moisture |
SMAP | Soil Moisture Active Passive |
SNAP | Sentinel Application Platform |
WCM | Water Cloud Model |
WOFOST | WOrld FOod STudies |
Appendix A
References
- Sun, Z.; Scherer, L.; Zhang, Q.; Behrens, P. Adoption of plant-based diets across Europe can improve food resilience against the Russia–Ukraine conflict. Nat. Food. 2022, 3, 905–910. [Google Scholar] [CrossRef]
- Food and Agriculture Organization of the United Nations; International Fund for Agricultural Development; United Nations Children’s Fund; World Food Programme; World Health Organization. The State of Food Security and Nutrition in the World 2020; Transforming Food Systems for Affordable Healthy Diets; FAO: Rome, Italy, 2020. [Google Scholar]
- Tao, F.; Palosuo, T.; Rötter, R.P.; Díaz-Ambrona, C.G.H.; Mínguez, M.I.; Semenov, M.A.; Kersebaum, K.C.; Cammarano, D.; Specka, X.; Nendel, C.; et al. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agric. For. Meteorol. 2020, 281, 107851. [Google Scholar] [CrossRef]
- Wang, J.; Li, X.; Lu, L.; Fang, F. Estimating near future regional corn yields by integrating multi-source observations into a crop growth model. Eur. J. Agron. 2013, 49, 126–140. [Google Scholar] [CrossRef]
- Huang, J.; Tian, L.; Liang, S.; Becker-Reshef, I.; Su, W.; Zhang, X.; Zhu, D.; Wu, W. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agric. For. Meteorol. 2015, 204, 106–121. [Google Scholar] [CrossRef] [Green Version]
- Bouman, B.A.M.; Van Diepen, C.A.; Vossen, P.; Van Der Val, T. Simulation and systems analysis tools for crop yield forecasting. In Applications of Systems Approaches at the Farm and Regional Levels; Teng, P.S., Ed.; Kluwer Academic Publishers: Amsterdam, The Netherlands, 1997; pp. 325–340. [Google Scholar]
- Ines, A.V.; Das, N.N.; Hansen, J.W.; Njoku, E.G. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sens. Environ. 2013, 138, 149–164. [Google Scholar] [CrossRef] [Green Version]
- Fang, H.; Liang, S.; Hoogenboom, G.; Teasdale, J.; Cavigelli, M. Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-maize model. Int. J. Remote Sens. 2008, 29, 3011–3032. [Google Scholar] [CrossRef]
- Huang, J.; Gómez-Dans, J.L.; Huang, H.; Ma, H.; Wu, Q.; Lewis, P.E.; Liang, S.; Chen, Z.; Xue, J.H.; Wu, Y.; et al. Assimilation of remote sensing into crop growth models: Current status and perspectives. Agric. For. Meteorol. 2019, 276–277, 107609. [Google Scholar] [CrossRef]
- Huang, H.; Huang, J.; Li, X.; Zhuo, W.; Wu, Y.; Niu, Q.; Su, W.; Yuan, W. A dataset of winter wheat aboveground biomass in China during 2007–2015 based on data assimilation. Sci. Data 2022, 9, 200. [Google Scholar] [CrossRef]
- Jin, X.; Li, Z.; Yang, G.; Yang, H.; Feng, H.; Xu, X.; Wang, J.; Li, X.; Luo, J. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS J. Photogramm. Remote Sens. 2017, 126, 24–37. [Google Scholar] [CrossRef]
- Weiss, M.; Jacob, F.; Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 2020, 236, 111402. [Google Scholar] [CrossRef]
- Zhuo, W.; Huang, J.; Li, L.; Zhang, X.; Ma, H.; Gao, X.; Huang, H.; Xu, B.; Xiao, X. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sens. 2019, 11, 1618. [Google Scholar] [CrossRef] [Green Version]
- Zhuo, W.; Fang, S.; Gao, X.; Wang, L.; Wu, D.; Fu, S.; Wu, Q.; Huang, J. Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: A case study for winter wheat in Hebei, China during 2009–2013. Int. J. Appl. Earth Obs. Geoinf. 2022, 106, 102668. [Google Scholar] [CrossRef]
- Xie, Y.; Wang, P.; Bai, X.; Khan, J.; Zhang, S.; Li, L.; Wang, L. Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using landsat imagery and the CERES-wheat model. Agric. For. Meteorol. 2017, 246, 194–206. [Google Scholar] [CrossRef]
- Wu, S.; Yang, P.; Chen, Z.; Ren, J.; Li, H.; Sun, L. Estimating winter wheat yield by assimilation of remote sensing data with a four-dimensional variation algorithm considering anisotropic background error and time window. Agric. For. Meteorol. 2021, 301, 108345. [Google Scholar] [CrossRef]
- Wu, S.; Yang, P.; Ren, J.; Chen, Z.; Li, H. Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm. Remote Sens. Environ. 2021, 255, 112276. [Google Scholar] [CrossRef]
- Jin, X.; Kumar, L.; Li, Z.; Feng, H.; Xu, X.; Yang, G.; Wang, J. A review of data assimilation of remote sensing and crop models. Eur. J. Agron. 2018, 92, 141–152. [Google Scholar] [CrossRef]
- de Wit, A.J.W.; van Diepen, C.A. Crop model data assimilation with the Ensemble Kalman Filter for improving regional crop yield forecasts. Agric. For. Meteorol. 2007, 146, 38–56. [Google Scholar] [CrossRef]
- Huang, J.; Sedano, F.; Huang, Y.; Ma, H.; Li, X.; Liang, S.; Tian, L.; Zhang, X.; Fan, J.; Wu, W. Assimilating a synthetic Kalman flter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 2016, 216, 188–202. [Google Scholar] [CrossRef]
- Ji, F.; Meng, J.; Cheng, Z.; Fang, H.; Wang, Y. Crop Yield Estimation at Field Scales by Assimilating Time Series of Sentinel-2 Data into a Modified CASA-WOFOST Coupled Model. IEEE Trans. Geosci. Remote Sens. 2021, 60, 4400914. [Google Scholar] [CrossRef]
- Zhuo, W.; Fang, S.; Wu, D.; Wang, L.; Li, M.; Zhang, J.; Gao, X. Integrating remotely sensed water stress factor with a crop growth model for winter wheat yield estimation in the North China Plain during 2008–2018. Crop J. 2022, 10, 1470–1482. [Google Scholar] [CrossRef]
- Nearing, G.S.; Crow, W.T.; Thorp, K.R.; Moran, M.S.; Reichle, R.H.; Gupta, H.V. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment. Water Resour. Res. 2012, 48, W05525. [Google Scholar] [CrossRef] [Green Version]
- Burgers, G.; Van Leeuwen, P.J.; Evensen, G. Analysis scheme in the ensemble Kalman filter. Mon. Weather Rev. 1998, 126, 1719–1724. [Google Scholar] [CrossRef]
- Zhuo, W.; Huang, J.; Xiao, X.; Huang, H.; Bajgain, R.; Wu, X.; Gao, X.; Wang, J.; Li, X.; Wagle, P. Assimilating Remote sensing-based VPM GPP into the WOFOST Model for Improving Regional Winter Wheat Yield Estimation. Eur. J. Agron. 2022, 139, 126556. [Google Scholar] [CrossRef]
- Li, X.; Zhao, H.; Zhang, H.; Wang, J. Global change study and quantitative remote sensing for land surface parameters. Earth Sci. Front. 2002, 9, 365–370. (In Chinese) [Google Scholar]
- Wang, L.; Fang, S.; Pei, Z.; Wu, D.; Zhu, Y.; Zhuo, W. Developing machine learning models with multisource inputs for improved land surface soil moisture in China. Comput. Electron. Agric. 2022, 192, 106623. [Google Scholar] [CrossRef]
- Zhao, F.; Gu, X.; Verhoef, W.; Wang, Q.; Yu, T.; Liu, Q.; Huang, H.; Qin, W.; Chen, L.; Zhao, H. A spectral directional reflectance model of row crops. Remote Sens. Environ. 2010, 114, 265–285. [Google Scholar] [CrossRef]
- Wu, S.; Ren, J.; Chen, Z.; Yang, P.; Li, H. Soil moisture estimation based on the microwave scattering mechanism during different crop phenological periods in a winter wheat-producing region. J. Hydrol. 2020, 590, 125521. [Google Scholar] [CrossRef]
- Veloso, A.; Mermoz, S.; Bouvet, A.; Le Toan, T.; Planells, M.; Dejoux, J.F.; Ceschia, E. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2- like data for agricultural applications. Remote Sens. Environ. 2017, 199, 415–426. [Google Scholar] [CrossRef]
- Louis, J.; Debaecker, V.; Pflug, B.; Main-Knorn, M.; Bieniarz, J.; Mueller-Wilm, U.; Cadau, E.; Gascon, F. Sentinel-2 Sen2Cor: L2A Processor for Users. In Proceedings of the Living Planet Symposium 2016, Prague, Czech Republic, 9–13 May 2016; pp. 1–8. [Google Scholar]
- de Wit, C.T. Photosynthesis of Leaf Canopies; Agricultural Research Reports; Pudoc: Wageningen, The Netherlands, 1965. [Google Scholar]
- Lin, C.; Wang, Z.; Zhu, J. An ensemble Kalman filter for severe dust storm data assimilation over China. Atmos. Chem. Phys. 2008, 8, 2975–2983. [Google Scholar] [CrossRef] [Green Version]
- Pan, H.; Chen, Z.; de Allard, W.; Ren, J. Joint assimilation of leaf area index and soil moisture from sentinel-1 and sentinel-2 data into the WOFOST model for winter wheat yield estimation. Sensors 2019, 19, 3161. [Google Scholar] [CrossRef] [Green Version]
- Kang, Y.; Özdoğan, M. Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach. Remote Sens. Environ. 2019, 228, 144–163. [Google Scholar] [CrossRef]
- Huang, J.; Zhuo, W.; Li, Y.; Huang, R.; Sedano, F.; Su, W.; Dong, J.; Tian, L.; Huang, Y.; Zhu, D.; et al. Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield. Int. J. Digit. Earth 2020, 13, 504–526. [Google Scholar] [CrossRef]
- Dong, T.; Liu, J.; Qian, B.; He, L.; Liu, J.; Wang, R.; Jing, Q.; Champagne, C.; McNairn, H.; Powers, J.; et al. Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data. ISPRS J. Photogramm. Remote Sens. 2020, 168, 236–250. [Google Scholar] [CrossRef]
- Zhuo, W.; Huang, J.; Gao, X.; Ma, H.; Huang, H.; Su, W.; Meng, J.; Li, Y.; Chen, H.; Yin, D. Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model. Remote Sens. 2020, 12, 2896. [Google Scholar] [CrossRef]
- Kepert, J.D. Covariance localisation and balance in an ensemble Kalman filter. Q. J. R. Meteorol. Soc. 2009, 135, 1157–1176. [Google Scholar] [CrossRef]
- Mitchell, H.L.; Houtekamer, P.L.; Pellegrin, G. Ensemble size, balance, and model-error representation in an ensemble Kalman filter. Mon. Weather Rev. 2002, 130, 2791–2808. [Google Scholar] [CrossRef]
- Yin, J.; Zhan, X.; Zheng, Y.; Hain, C.R.; Liu, J.; Fang, L. Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation. Geophys. Res. Lett. 2015, 42, 6710–6715. [Google Scholar] [CrossRef] [Green Version]
- Daley, R. The effect of serially correlated observation and model error on atmospheric data assimilation. Mon. Weather Rev. 1992, 120, 164–177. [Google Scholar] [CrossRef]
- Tandeo, P.; Ailliot, P.; Bocquet, M.; Carrassi, A.; Miyoshi, T.; Pulido, M.; Zhen, Y. A review of innovation-based methods to jointly estimate model and observation error covariance matrices in ensemble data assimilation. Q. J. R. Meteorol. Soc. 2018, 148, 3973–3994. [Google Scholar] [CrossRef]
- Tian, J.; Qin, J.; Yang, K.; Zhao, L.; Chen, Y.; Lu, H.; Li, X.; Shi, J. Improving surface soil moisture retrievals through a novel assimilation algorithm to estimate both model and observation errors. Remote Sens. Environ. 2022, 269, 112802. [Google Scholar] [CrossRef]
- Wu, Y.; Xu, W.; Huang, H.; Huang, J. Bayesian Posterior-Based Winter Wheat Yield Estimation at the Field Scale through Assimilation of Sentinel-2 Data into WOFOST Model. Remote Sens. 2022, 14, 3727. [Google Scholar] [CrossRef]
- Huang, H.; Huang, J.; Wu, Y.; Zhuo, W.; Song, J.; Li, X.; Li, L.; Su, W.; Ma, H.; Liang, S. The improved winter wheat yield estimation by assimilating GLASS LAI into a crop growth model with the proposed Bayesian posterior-based ensemble Kalman filter. IEEE Trans. Geosci. Remote Sens. 2023. [Google Scholar] [CrossRef]
- Houtekamer, P.L.; Mitchell, H.L. A sequential ensemble Kalman filter for atmospheric data assimilation. Mon. Weather Rev. 2001, 129, 123–137. [Google Scholar] [CrossRef]
Assimilation Strategy | R2 (500 m) | RMSE (500 m) (kg/ha) | R2 (10 m) | RMSE (10 m) (kg/ha) |
---|---|---|---|---|
Without DA | 0.19 | 533 | 0.21 | 1330 |
DA with SM | 0.32 | 694 | 0.35 | 934 |
DA with LAI | 0.39 | 829 | 0.40 | 879 |
DA with LAI + SM | 0.42 | 736 | 0.46 | 756 |
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Zhuo, W.; Huang, H.; Gao, X.; Li, X.; Huang, J. An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model. Remote Sens. 2023, 15, 1825. https://doi.org/10.3390/rs15071825
Zhuo W, Huang H, Gao X, Li X, Huang J. An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model. Remote Sensing. 2023; 15(7):1825. https://doi.org/10.3390/rs15071825
Chicago/Turabian StyleZhuo, Wen, Hai Huang, Xinran Gao, Xuecao Li, and Jianxi Huang. 2023. "An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model" Remote Sensing 15, no. 7: 1825. https://doi.org/10.3390/rs15071825