View the original text>>> Practical technical application of remote sensing data and crop model assimilation
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.
[Brief description of content]:
This article 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. The setting of the outline mainly focuses on the above-mentioned links to design relevant basic theoretical knowledge and hands-on operation steps. Through step-by-step explanation and practical operation, the set goals can be achieved.
Topic 1: Basic theoretical knowledge of remote sensing [Brief description of content]:
This content 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.
Topic 1: Basic theoretical knowledge of remote sensing
Remote sensing platforms (such as UAVs) and sensors,
basic principles of remote sensing from major domestic and foreign land satellites (such as Landsat, SPOT, HJ, GF), spectral response functions, and remote sensing data processing procedures.
Application of remote sensing in terrestrial ecosystem monitoring
Topic 2: Research progress at home and abroad on crop growth monitoring and yield estimation
Domestic and foreign research review
and research case analysis
Topic 3: Fortran Programming Language
Software installation
(using xp/win7/win8/win10 professional notebook)
project file creation and basic syntax operations
Topic 4: Basic principles of remote sensing inversion of crop parameters
Types of crop parameters retrieved by remote sensing
: Biochemical components (chlorophyll, nitrogen, dry matter, leaf moisture content, anthocyanins)
Biophysical parameters (LAI, LAD, plant height, biomass)
Physiological and ecological parameters (FPAR, ET)
Remote sensing of crop parameters Inversion model: empirical model, linear model, exponential model, logarithmic model
Physical model: radiative transfer model, geometric optics model, hybrid model, computer simulation model
Comparative analysis of different methods
Topic 5: PROSAIL Model
Input parameters: LAI/LAD/chlorophyll/anthocyanin/dry matter/carotenoids/moisture content/…
Output parameters: vegetation canopy reflectance.
Take the FORTRAN code as an example to simulate the reflectance simulation process on the computer
to simulate leaf reflectance and transmission. Rate
Simulates the 400-2500 nm hyperspectral reflectance curve of the vegetation canopy
Simulates the multispectral reflectance data of remote sensing sensors such as Landsat OLI and MODIS
Topic 6: Parameter sensitivity analysis
Selection of parameters 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
Topic 7 : Cost function solution in remote sensing inversion process
Cost function construction: inversion method, inversion parameters, "ill-conditioned" problems, prior knowledge, function extreme value problem
Introduction to inversion algorithm: optimization technology lookup table, neural network, simulated annealing
Application case analysis
Topic 8: Remote sensing inversion of crop parameters based on lookup table method + PROSAIL model
Principle of lookup table,
implementation of lookup table,
remote sensing inversion of crop parameters based on lookup table and PROSAIL model
Topic 9: Remote sensing inversion of crop parameters based on optimization algorithm + PROSAIL model
Solve the extreme value of the cost/objective function Solve
the extreme value of the test function
Solve the PROSAIL model parameters with the optimization algorithm Extract
the optimal value of the crop parameters to be solved
Topic 10: Programmed Expression and Operation of Crop Models
Model classification: empirical model, semi-mechanical model, mechanistic model. Model selection
principles. Model
debugging
. Comparative analysis of model calibration.
Application case analysis of
model operation (taking DSSAT crop model as an example, FORTRAN source code): Evolution of time series vegetation parameters (such as leaf area index) Simulation, time series changes of crop parameters (such as LAI) and yield simulation process
Topic 11: Crop model and remote sensing data assimilation modeling principles
The necessity of coupling crop models and remote sensing observations: advantages and disadvantages of crop models, advantages and disadvantages of remote sensing observations, necessity of coupling Coupling
method: driving method, principle, program implementation process, application examples
Data assimilation method: development history, introduction to data assimilation algorithm, method Comparative analysis
Sensitivity analysis of crop model parameters: parameter selection to be optimized, local sensitivity analysis, global sensitivity analysis
Crop model and remote sensing data assimilation: assimilation of remote sensing inversion results (such as LAI remote sensing products), assimilation of remote sensing observation reflectance
Topic 12 Programmed implementation of crop model and remote sensing inversion value assimilation modeling (first method)
Fortrtan 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
Topic 13 Programmed implementation of crop model and remote sensing reflectance assimilation modeling (second method)
Fortrtan operating platform
Remote sensing observation reflectance Crop
model
Vegetation canopy reflectance model
PROSAIL forward model reflectance 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
[Other related recommendations]:
Practical technical application of WOFOST model and PCSE model