Rice Growth Estimation and Yield Prediction by Combining the DSSAT Model and Remote Sensing Data Using the Monte Carlo Markov Chain Technique
Abstract
:1. Introduction
2. Result
2.1. Spectral Index for LAI and VNA Estimation
2.2. The Probability Distribution of Inverted Initial Parameters
2.3. Data Assimilation for LAI and PNA Estimation
2.4. Data Assimilation for LAI and PNA Prediction and Uncertainty Analysis
2.5. Data Assimilation for Yield Prediction and Uncertainty Analysis
3. Materials and Methods
3.1. Experimental Design
3.2. Data Acquisition
3.2.1. Measurement of Canopy Spectral Reflectance
3.2.2. Plant Sampling and Analysis
3.3. CERES-Rice Model
3.4. Integrating the MCMC Technique for Data Assimilation
3.5. Statistical Analysis
4. Discussion
4.1. Integration of Crop Model and Remote Sensing
4.2. MCMC Method
4.3. Uncertainty Analysis
4.4. Yield Prediction
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Treatments | Sowing Date (%) | Seeding Rate (%) | Nitrogen Amount (%) |
---|---|---|---|
E1N0 | −2 | −16.2 | / |
E1N1 | −1.33 | 3.2 | 7.2 |
E1N2 | 0 | 17.9 | −7.3 |
E1N3 | 0.67 | 27.2 | −11.9 |
E1N4 | 1.33 | 32.1 | 1.8 |
E2N0 | −1.6 | −12.4 | / |
E2N1 | −1.4 | 4.3 | −5.2 |
E2N2 | −0.7 | 13.6 | 8.4 |
E2N3 | 1 | 23.2 | −9.3 |
E2N4 | 1.4 | 29.4 | 7.5 |
E3N0 | −2.2 | −15.2 | / |
E3N1 | −1.3 | −1 | 8.53 |
E3N2 | −0.4 | 12.7 | −3.7 |
E3N3 | 1.3 | 24.1 | 5.8 |
E3N4 | 1.2 | 32.5 | 11.7 |
Experiment No | Site | Cultivar | N Rate (Kg ha−1) | Planting Date | Soil Parameters |
---|---|---|---|---|---|
Experiment 1 2015 | Deqing | Yongyou538 | N0(0) N1(70) N2(140) N3(210) N4(280) | 28-May | Soil organic matter: 22 g kg−1 Total N: 1.37 g kg−1 P2O5: 32.59 mg kg−1 K2O: 98.96 mg kg−1 |
Experiment 2 2016 | Deqing | Xiushui134 | N0(0) N1(70) N2(140) N3(210) N4(280) | 28-May | Soil organic matter: 21.1 g kg−1 Total N: 1.27 g kg−1 P2O5: 38.12 mg kg−1 K2O: 90.23 mg kg−1 |
Experiment 3 2017 | Deqing | Yongyou1540 | N0(0) N1(70) N2(140) N3(210) N4(280) | 30-May | Soil organic matter: 22.4 g kg−1 Total N: 1.24 g kg−1 P2O5: 40.14 mg kg−1 K2O: 94.56 mg kg−1 |
LAI | PNA | |||||||
---|---|---|---|---|---|---|---|---|
Tilling | Jointing | Booting | Flowering | Tilling | Jointing | Booting | Flowering | |
N0 | 0.17 | 0.10 | 0.16 | 0.15 | 5.30 | 3.28 | 2.92 | 3.07 |
N1 | 0.26 | 0.11 | 0.17 | 0.17 | 6.02 | 4.25 | 3.66 | 3.93 |
N2 | 0.23 | 0.12 | 0.16 | 0.16 | 5.06 | 3.98 | 3.59 | 3.92 |
N3 | 0.22 | 0.13 | 0.16 | 0.13 | 4.85 | 4.12 | 3.64 | 4.03 |
N4 | 0.15 | 0.13 | 0.13 | 0.14 | 3.84 | 3.85 | 3.61 | 3.78 |
Yield | |||
---|---|---|---|
R2 | RMSE (kg/ha) | RMSD (kg/ha) | |
E1 | 0.86 | 960 | 678 |
E2 | 0.83 | 338 | 764 |
E3 | 0.7 | 685 | 792 |
Total | 0.79 | 661 | 745 |
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Chen, Y.; Wang, S.; Xue, Z.; Hu, J.; Chen, S.; Lv, Z. Rice Growth Estimation and Yield Prediction by Combining the DSSAT Model and Remote Sensing Data Using the Monte Carlo Markov Chain Technique. Plants 2025, 14, 1206. https://doi.org/10.3390/plants14081206
Chen Y, Wang S, Xue Z, Hu J, Chen S, Lv Z. Rice Growth Estimation and Yield Prediction by Combining the DSSAT Model and Remote Sensing Data Using the Monte Carlo Markov Chain Technique. Plants. 2025; 14(8):1206. https://doi.org/10.3390/plants14081206
Chicago/Turabian StyleChen, Yingbo, Siyu Wang, Zhankui Xue, Jijie Hu, Shaojie Chen, and Zunfu Lv. 2025. "Rice Growth Estimation and Yield Prediction by Combining the DSSAT Model and Remote Sensing Data Using the Monte Carlo Markov Chain Technique" Plants 14, no. 8: 1206. https://doi.org/10.3390/plants14081206
APA StyleChen, Y., Wang, S., Xue, Z., Hu, J., Chen, S., & Lv, Z. (2025). Rice Growth Estimation and Yield Prediction by Combining the DSSAT Model and Remote Sensing Data Using the Monte Carlo Markov Chain Technique. Plants, 14(8), 1206. https://doi.org/10.3390/plants14081206