The CASA model is a process-based remote-sensing model (Potter et al, 1993; Potter et al, 1994), which couples ecosystem productivity and soil carbon and nitrogen fluxes, and is composed of gridded global climate, radiation, soil and remote-sensing vegetation indices Dataset driven. Models include soil organic matter, trace gas fluxes, nutrient availability, soil moisture, temperature, soil structure, and microbial cycling. The model simulates seasonal changes in carbon uptake, nutrient distribution, litter fall, soil nutrient mineralization, and carbon dioxide release with a monthly time resolution. Potter and Klooster considered the land cover change caused by human activities, and made some adjustments to the CASA model and some parameters to improve the calculation of soil carbon cycle and total ecosystem nitrogen availability related to plant uptake requirements
The first CASA model introduction (explain + case practice)
1.1 Introduction to carbon cycle models
1.2 Principle of CASA model
1.3 CASA download and installation
1.4 CASA Notes
The second CASA preliminary operation
2.1 ENVI interface introduction
2.2 ENVI data and format
2.3 CASA simulation based on ENVI
2.4 Analysis of CASA results
Third CASA data preparation (1)
3.1 Remote Sensing and GIS Technology in Data Preparation
3.2 ArcGIS software interface
3.3 Coordinate system and coordinate transformation
3.4 Processing of regional data
3.5 CASA Network Data Resources and Download
3.6 Processing of CASA network data
Fourth CASA Data Preparation (2)
4.1 Introduction to Remote Sensing Technology
4.2 Remote sensing image acquisition and display
4.3 Remote sensing image processing
4.4 Remote sensing interpretation of land use
Fifth CASA Data Preparation (3)
5.1 Introduction to Quantitative Remote Sensing
5.2 Radiometric calibration of remote sensing images
5.3 FLASSH Atmospheric Correction
5.4 NDVI Calculation
Sixth CASA Data Preparation (4)
6.1 Space display of meteorological point data
6.2 Download and processing of weather station data
6.3 Geostatistical spatial interpolation of meteorological data
6.4 Design of sampling points based on fishnet tool
6.5 CASA static parameter setting
The seventh CASA simulation under land use change
7.1 Land use change and carbon emissions
7.2 Analysis of land use change based on transfer matrix
7.3 Scenario analysis of land use change
7.4 Future land use forecast
7.5 CASA simulation under land use change
Eighth CASA simulation under climate change
8.1 Introduction to CMIP6 data
8.2 CMIP6 data download
8.3 CMIP6 data display
8.4 Convert CMIP6 data to CASA meteorological data
8.5 CASA simulation under future climate change
Ninth Simulation of carbon source and carbon sink based on CASA model
9.1 Introduction to carbon source and carbon sink analysis
9.2 Calculation of Respiration Consumption Rh of Heterotrophs
9.3 Calculation of Net Ecosystem Productivity NEP
9.4 Analysis of carbon sources and carbon sinks
Tenth CASA Case Analysis
Spatial-temporal dynamic simulation of carbon source/sink in ecosystem based on CASA
Practice of "Python+" Multi-Technology Fusion in Estimation of Evapotranspiration and Vegetation Total Primary Productivity
Familiar with evapotranspiration ET and its components (vegetation transpiration Ec, soil evaporation Es, canopy interception Ei), the concept of vegetation gross primary productivity GPP and the basic principles of carbon-water coupling; master the use of Python and ArcGIS tools for course-related operations; proficiency Master the internationally popular Penman-Monteith model, and be able to apply this model to calculate canopy conductance and evapotranspiration components on various vegetation types; master visualization methods and mapping methods for single-station and regional results. The scope of application of the course is related to industries related to ecology and hydrology, and industries related to double carbon.
Two evapotranspiration and photosynthesis impedance & Python practice
1. Evapotranspiration and photosynthesis impedance
Evapotranspiration and photosynthetic impedance are important concepts in plant physiology. Evapotranspiration is closely related to plant water balance, growth and metabolism; while photosynthesis resistance reflects the diffusion resistance formed by plants to maintain photosynthesis under the condition of limiting water evapotranspiration. Studying the principles of evapotranspiration and photosynthesis impedance is helpful to understand the photosynthesis efficiency, growth rate and ecological adaptability of plants, and provide scientific basis and decision support for agricultural production, forestry management and environmental protection.
2. Python instructions
2.1. Installation of Jupyter Notebook editor + Anaconda manager
Python is an easy-to-learn, powerful programming language with rich standard library and extensive third-party library support, suitable for many fields such as big data processing, artificial intelligence, and web development.
2.2 Installation and configuration of virtual environment
Virtual environments allow the creation of multiple independent Python environments on the same machine, each of which can have its own version of Python and third-party libraries installed. Different projects can use different Python versions and dependent libraries, avoiding version conflicts and dependency conflicts.
2.3 Common library learning This section includes the basic syntax of Python and the use of common scientific computing (Numpy), data processing (Pandas) and data visualization (Matplotlib) library functions .
2.4 Data processing
Common data problems in Python include data duplication, data exception, text type, data missing, data invalid, etc., corresponding to operations such as outlier processing, text conversion, and blank value filling.
Three ArcGIS practical applications
3. ArcGIS practice
3.1 Basic Operation
The basic operations of ArcGIS include creating and opening map documents, loading data, saving documents, layer operations, data frame coordinate system definition, feature attribute query, etc.
3.2 Data format conversion
In ArcGIS, the mutual conversion of different data formats can be realized, such as the mutual conversion between EXCEL data and Shapefile data, TXT data and Shapefile data, etc.
3.3 Extract raster values
Value extraction to points can be achieved in ArcGIS or batch processing of raster datasets can be achieved using ArcPy.
3.4 Data pruning
The clipping function in ArcGIS is used to clip the layer or raster dataset according to the specified boundary range. Clipping allows you to remove data that is not of interest or to limit it to a specific area for better analysis and visualization.
3.5 Cartography Map cartography mainly includes the main elements of map layout design, map rendering methods and other content.
4. Data processing practice 4. Data download and processing 4.1 Site data download and processing FLUXNET2015 is a global-scale carbon, water and energy flux observation dataset, which brings together data from more than 200 observation sites. The dataset provides in situ observations of multiple observed variables such as carbon, water, and energy fluxes, stored and shared in a standardized format .
Data download Open the URL https://fluxnet.org/data/fluxnet2015-dataset/ , click Download FLUXNET2015 Dataset, log in with the user name and account password, you can choose a site according to your needs, and fill in the application requirements to complete the download.
Ø Data processing According to research needs, the downloaded data is processed, including variable selection, processing of outliers, and filling of vacant values. 4.2 Regional data download and processing GLASS is a global land surface remote sensing dataset, which provides high-resolution vegetation leaf area index (LAI) data with a spatial resolution of 250m/500m/0.05° and a temporal resolution of 8 days.
Ø Data download Open the website http://www.glass.umd.edu/index.html , select the LAI dataset with the corresponding resolution according to the research needs, and use DownThemAll! to download the data in batches.
Ø Data processing The downloaded data is in hdf format. According to the research needs, the downloaded data is processed, including data format conversion, definition projection, corresponding grid value extraction, data summary, etc.
Five-canopy conductance and water and carbon flux spatial simulation case analysis practice 5. Application case 1: Calculation of evapotranspiration, soil evaporation, and vegetation transpiration at the site scale On the site scale, use leaf area index, net radiation, etc. to calculate The effective energy of the canopy and the effective energy of the soil are obtained, and the soil evaporation is obtained according to the accumulated precipitation and the equilibrium evaporation rate of the soil surface for a certain period of time, and then the vegetation transpiration and canopy conductance are calculated. The specific operation is as follows: 1. Station value extraction and interpolation data format conversion of leaf area index definition projection station value extraction data interpolation 2. Soil evaporation calculation canopy effective energy and soil effective energy calculation soil evaporation fraction calculation soil balance evaporation calculation 3 , Vegetation transpiration Calculation of psychrometer constant Calculation of saturation water vapor pressure and temperature relationship curve slope calculation Aerodynamic conductance calculation 4. Canopy conductance calculation Numerical calculation results visualization
Case 2: Regional Data Download, Processing and Display of Evapotranspiration and Vegetation Gross Primary Productivity
Download, process, display and count data of regional surface evapotranspiration and its components (soil evaporation, vegetation transpiration, canopy interception evaporation), and total primary productivity of vegetation.
