Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects
Abstract
:1. Introduction
- The choice of emerging RS platform DA: in the face of an endless stream of new generation RS data (such as satellite constellation, UAV, new RS platform, high space-time resolution, etc.), how to make use of the characteristics of multi-source data, consider application scenarios and cost-effectiveness and other factors, select and effectively integrate these data, and further solve the data source problem of DA is an urgent need for the current research.
- In-depth understanding of the internal mechanism of DA model: there is a lack of discussion on parameter selection and optimization, uncertainty quantification, multi-model fusion, and fine management of the DA model in existing precision agriculture research, which restricts the further improvement of DA model performance and the expansion of agricultural application.
- The applicability of new challenges to DA: under the background of extreme climate change, precision agriculture, and the rise of the agriculture 4.0 digital era, challenges such as the processing of new RS sources of DA, multi-dimensional application scenarios, robust models, and new requirements for high-precision DA have not been fully studied and addressed.
2. Materials and Methods
2.1. Assimilation of Agricultural Data Sources by Remote Sensing
2.2. A New Multi-Source Remote Sensing Platform DA
2.3. High Spatio-Temporal Resolution Remote Sensing DA
2.4. The Mechanism and Framework of DA
3. Results
3.1. Forcing Method
3.2. Model Parameter Optimization DA
- represents the mode prediction operator from 0 to i, and has , and the forward prediction mode can be expressed as the product of the intermediate prediction mode, that is, .
- Under the tangent linearity hypothesis, the pattern operator and the observation operator are linearized, that is, , where and are the tangent operators of the pattern operator and the observation operator, respectively, and the minimum value of J is solved by gradient descent or conjugate gradient method. The obtained is the optimal solution of the initial state.
- Explore the advantages of optimization algorithms such as conjugate gradient method and quasi-Newton method in solving gradient problems. Global optimization techniques such as multiple startup, simulated annealing, and genetic algorithm were used to improve the initialization and optimization strategy of the algorithm and improve the convergence and stability of the algorithm [113]. Try to calibrate the sensitivity error of the initial conditions based on the model.
- Search for nonlinear framework: To solve nonlinear uncertainty, explore and develop nonlinear variational assimilation frameworks, such as manifold embedding variational method (ME-Var), four-dimensional variational filtering (4D-VARF), etc. For example, the anisotropic background error and time-window four-dimensional variation algorithm constructed by Wu et al. provides us with valuable reference experience [141].
- Focusing on uncertainty characterization and quantification. Introducing set members to model uncertainty: set Kalman filtering idea, and developing assimilation methods such as set four-dimensional variational (En-4D-Var) [142]. In terms of physical and chemical parameters, we need to pay attention to the quantification of the uncertainty, reduce the uncertainty through the integration of assimilation methods, and improve the adaptability and scalability of assimilation algorithms.
3.3. Model Status Update DA
- Forecast
- Update
3.4. Bayesian Normal Form DA
4. Discussion
4.1. Challenges Faced by DA in Agricultural Application
4.1.1. Deep Fusion and Uncertainty Management of Multi-Source Heterogeneous Data
4.1.2. Refinement and Dynamic Updating of Complex Crop Growth Models
4.1.3. From Univariate Assimilation to Multivariable and Multiscale Assimilation
4.2. The Future Development Prospect of DA
4.2.1. Intelligent and Integrated DA
4.2.2. DA Applicability of Decision Support for Precision Agriculture
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
References
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Model Name | Application of Agricultural Assimilation | References |
---|---|---|
APSIM | Simulate crop growth, soil water, and nutrient dynamics and crop management practices (suitable for crops such as wheat, corn, and soybeans) | [33] |
Agromet Shell | Simulated crop growth and yield (Prediction of crop growth and yield changes based on meteorological data) | [34] |
Aquacrop | Response of simulated crops to water stress and yield prediction (Suitable for arid and semi-arid areas) | [35] |
CERES-wheat | Simulate the growth and yield of wheat (Taking into account a variety of environmental and regulatory factors) | [36] |
CROPGRO-Soybean | Simulate crop growth and yield, such as soybeans (The physiological and ecological processes of crops, soil conditions, and management measures were considered.) | [37] |
Cropsyst | Simulate the growth, development, and yield of a variety of crops (Suitable for different climate and soil conditions) | [38] |
DAISY | Simulate the interaction between soil moisture, nutrients, and crop growth (Suitable for farmland and paddy field management) | [39] |
DSSAT | Simulate the growth, yield, and soil dynamics of many crops (Taking into account the impact of climate change and management practices on crops) | [40] |
EPIC | Simulate crop growth, soil erosion, nutrient cycling (Sustainable management of agro-ecosystem) | [34] |
GLAM | Simulated crop growth and yield are affected by global climate change (The effects of climate change on crop physiological and ecological processes were considered) | [41] |
HERMES | Simulation of crop growth and water balance (It is especially suitable for crop management in the Mediterranean climate region) | [39] |
STICS | Simulate crop growth, soil moisture, and nutrient dynamics (Suitable for wheat, corn, and other crops and different soil types) | [42] |
SUCROS | Simulate the growth and yield of sugar crops, such as sugarcane (The physiological and ecological processes of crops and climatic factors were considered) | [43] |
SWAP | Simulation of water cycle and crop growth in farmland system (Suitable for arid and semi-arid areas) | [44] |
WOFOST | Simulation of crop growth, yield, and soil moisture dynamics (Crop physiological and ecological processes and climate change are considered) | [45] |
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Wang, J.; Wang, Y.; Qi, Z. Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects. Agronomy 2024, 14, 1920. https://doi.org/10.3390/agronomy14091920
Wang J, Wang Y, Qi Z. Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects. Agronomy. 2024; 14(9):1920. https://doi.org/10.3390/agronomy14091920
Chicago/Turabian StyleWang, Jun, Yanlong Wang, and Zhengyuan Qi. 2024. "Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects" Agronomy 14, no. 9: 1920. https://doi.org/10.3390/agronomy14091920