The process-based crop growth simulation model DSSAT is a powerful tool for modern agricultural system research. It can quantitatively describe the crop growth and development and yield formation processes and their relationship with climatic factors, soil environment, variety types and technical measures, and provide solutions for different conditions. Quantitative tools are provided for crop growth and yield prediction, cultivation management, environmental assessment, and future climate change assessment. However, when crop growth models develop from single-point research to regional-scale applications, the non-uniformity of the surface and near-surface environments that arises due to the increase in spatial scale leads to problems in the acquisition of some macroscopic data and the regionalization of parameters in the model. There are many difficulties, and model simulation results will also have great uncertainties. Remote sensing information can help crop growth models overcome these shortcomings to a large extent.
Remote sensing data from domestic satellites (such as HJ, GF, ZY), MODIS, Landsat, Sentinel-2 and other remote sensing data are effective means of monitoring crop growth status in a large area; crop growth models can use environmental factors to simulate the crop growth process and reveal the characteristics of crop growth and development. Cause and essence. Driven by the development of science and technology and the demand for agricultural applications, data assimilation methods combine remote sensing data with crop growth models to monitor crop growth and predict crop yields. This is one of the important contents and development trends of current agricultural information technology application research. The combination of the two can not only provide macroscopic monitoring information, but also dynamically reflect the crop growth and development process, which is conducive to realizing complementary advantages and enhancing application potential.
At present, there is no mature commercial software available for crop yield estimation based on data assimilation method coupling remote sensing and crop model. This course aims to help students master the basic knowledge of remote sensing and crop model assimilation, and the differences between it and traditional crop remote sensing monitoring methods. Contact us, using a combination of "theoretical explanation + case practice + hands-on practice + discussion and interaction" to peel off the cocoons and analyze in simple terms the experience and programming skills that need to be mastered when applying data assimilation methods in crop growth monitoring and yield estimation, in order to solve agricultural problems. Relevant scientific issues in production research.
It mainly involves the remote sensing data, PROSAIL model, DSSAT model, parameter sensitivity analysis, data assimilation algorithm, model coupling, accuracy verification and other main links in the assimilation modeling of remote sensing data and crop models.
1 : Basic theoretical knowledge of remote sensing
- Remote sensing platforms (such as drones) and sensors, major domestic and foreign land satellites (such as Landsat, SPOT, HJ, GF)
- Basic principles of remote sensing, spectral response function, remote sensing data processing flow
- Application of remote sensing in terrestrial ecosystem monitoring
2 : Research progress at home and abroad on crop growth monitoring and yield estimation
- A review of domestic and foreign research
- Research case analysis
Three : Fortran programming language
Software installation (using xp/win7/win8/win10 professional version notebook)
Project file creation and basic syntax operations
IV : Basic principles of remote sensing inversion of crop parameters
- Remote sensing inversion crop parameter types
- Biochemical components
(Chlorophyll, nitrogen, dry matter, leaf moisture content, anthocyanins)
- biophysical parameters
(LAI, LAD, plant height, biomass)
- physiological and ecological parameters
（FPAR、ET）
- Crop parameter remote sensing inversion model
- empirical model
- linear model
- exponential model
- Logarithmic model
- …
- physical model
- Radiative transfer model
- geometric optics model
- Mixed model
- computer simulation model
Comparative analysis of different methods
- Mixed model
- computer simulation model
Comparative analysis of different methods
(LAI, LAD, plant height, biomass)
- physiological and ecological parameters
（FPAR、ET）
- Crop parameter remote sensing inversion model
- empirical model
- linear model
- exponential model
- Logarithmic model
- …
- physical model
- Radiative transfer model
- geometric optics model
- Mixed model
- computer simulation model
Comparative analysis of different methods
Five : PROSAIL model
- Input parameters: LAI/LAD/chlorophyll/anthocyanin/dry matter/carotenoids/moisture content/…
- Output parameter: Vegetation canopy reflectance
- Taking FORTRAN code as an example to simulate the reflectivity simulation process on a computer
- Simulate leaf reflectivity and transmittance
- Simulated vegetation canopy 400-2500 nm hyperspectral reflectance curve
Simulate multispectral reflectance data from remote sensing sensors such as Landsat OLI and MODIS
Six : Parameter sensitivity analysis
- Parameter selection to be optimized
- local sensitivity analysis
- global sensitivity analysis
- Introduction to EFAST sensitivity analysis method
- SIMLAB software operation process
Global sensitivity analysis of PROSAIL model parameters
7. Cost function solution problems in the process of remote sensing inversion
- Cost function construction
- Inversion method
- Inversion parameters
- "Sick" problem
- Prior Knowledge
- function extreme value problem
- Introduction to inversion algorithm
- Optimization technology
- lookup table
- Neural Networks
- simulated annealing
- …
Application case analysis
Eight remote sensing inversions of crop parameters based on lookup table method + PROSAIL model
- Lookup table principle
- Lookup table implementation
Remote sensing inversion of crop parameters based on lookup table and PROSAIL model
9. Remote sensing inversion of crop parameters based on optimization algorithm + PROSAIL model
- Cost/objective function extreme value solution
- Test function extreme value solution
- Optimization algorithm to solve PROSAIL model parameters
Extraction of optimal values of crop parameters to be solved
Programmed expression and operation of ten crop models
- Model classification
- empirical model
- Semi-mechanical model
- Mechanism model
- Model selection principles
- Model debugging
- Model calibration
- Model comparative analysis
- Application case analysis
- Model operation (taking DSSAT crop model as an example, FORTRAN source code)
- Time series vegetation parameter (such as leaf area index) evolution simulation
Crop parameter (such as LAI) time series changes and yield simulation process
Eleven crop model and remote sensing data assimilation modeling principle
- The necessity of coupling crop models and remote sensing observations
- Crop model advantages and disadvantages
- Advantages and Disadvantages of Remote Sensing Observation
- Necessity of coupling
- coupling method
- driving method
- principle
- Program implementation process
- Applications
- data assimilation method
- development path
- Introduction to data assimilation algorithms
- Comparative analysis of methods
- Sensitivity analysis of crop model parameters
- Parameter selection to be optimized
- local sensitivity analysis
- global sensitivity analysis
- Crop model and remote sensing data assimilation
- Assimilate remote sensing inversion results (such as LAI remote sensing products)
Assimilated remote sensing observation reflectance
Programmed implementation of twelve-crop model and remote sensing inversion value assimilation modeling (first method)
- Forttan operating platform
- Remote sensing inversion results (such as leaf area index)
- crop model
- variational algorithm
- Cost function construction
- Iterative solution
Output : time series changes of key crop parameters , yield estimation results , regional mapping
Programmed implementation of thirteen crop models and remote sensing reflectivity assimilation modeling (second method)
Crop model and remote sensing reflectance assimilation modeling framework
- Forttan operating platform
- Remote sensing reflectivity
- crop model
- Vegetation canopy reflectance model
- PROSAIL forward model reflectivity simulation
- Coupled model construction (crop model + canopy reflectance model)
- variational algorithm
- Cost function construction
- Iterative solution
Output : time series changes of key crop parameters , yield estimation results , regional mapping