Vegetation is one of the most important components of terrestrial ecosystems and the most sensitive to climate change. It plays an important role in the process of global change and can indicate changes in the atmosphere, water, soil and other components in the natural environment. , and its interannual and seasonal variations can be used as an important indicator of Earth's climate change. In addition, due to factors such as ecological engineering protection construction and natural vegetation growth, China's terrestrial ecosystems have played an important role in carbon sinks. Therefore, quantitatively assessing the temporal and spatial dynamic changes of vegetation is an important prerequisite for formulating sustainable development goals of ecosystems and measuring the carbon sequestration potential of ecosystems. The ecological parameter products derived from satellite remote sensing data provide important data for the study of long-term global and regional vegetation temporal and spatial changes. source. At present, many long-term biophysical parameter products have been retrieved from satellite remote sensing data, such as GIMMS3g NDVI/LAI/FAPAR, MODIS NDVI/LAI/FAPAR/GPP, GLASS LAI/FVC/GPP, etc., and have been widely used in the world Or regional scale vegetation change trend and pattern analysis.
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Topic 1. Application of long-term remote sensing products in global change
/vegetation greening/vegetation phenology Science/Nature/PNAS and other related articles
Long time series remote sensing data product introduction
Analysis Method of Long Time Series Remote Sensing Data Products
Quality Evaluation of Long Time Series Remote Sensing Data Products
Topic 2. MODIS remote sensing data product preprocessing
HDF image mosaic/sub-region interception/format conversion based on MODIS TOOL
Automatic Batch Processing Program of Long Time Series Massive Remote Sensing Data Based on MODIS TOOL
Python-based reading of remote sensing product values
Python-based product quality control (QC) layer reading and meaning interpretation
After QC, the maximum value/mean value/median value of the product is synthesized
Topic 3. Long-sequence MODIS remote sensing data product time series reconstruction
remote sensing data outliers/outliers detection method
Intra-year time series remote sensing data reconstruction to remove noise points (filtering, polynomial fitting, ...)
Batch calculation of annual average/maximum value, monthly average/maximum value, seasonal average/maximum value of long-term remote sensing products year by year
Calculation of anomaly and coefficient of variation
The Influence of Weather (such as Cloud) on the Analysis of Long Time Series Remote Sensing Data
Topic 4: Building Longer Time Series Remote Sensing Data Based on GIMMS 3g and MODIS NDVI
Correlation Analysis of GIMMS 3g and MODIS NDVI Products
Convergence of GIMMS 3g and MODIS NDVI products in overlapping time period
Generation of longer time series products based on GIMMS 3g and MODIS NDVI products
Topic 5. Practical Application of Vegetation Phenology Extraction and Analysis
Reconstruction Method of Intra-year Time Series Remote Sensing Data
Implementation of multiple vegetation phenology extraction methods: threshold/logistic/derivative/…
Growing season start/length/end date extraction
Vegetation SOS/LOS/EOS Mapping
Analysis of interannual vegetation phenological change trend
Topic 6 Practical application of vegetation greening trend analysis
Analysis method of long-term interannual vegetation change trend
Vegetation Greening/Yellowing Trend Judgment Criteria
Vegetation Change Trend Judgment Based on Univariate Linear Regression
Vegetation Change Test Based on Manner-Kendall(MK)
Vegetation Change Stability Analysis Based on Coefficient of Variation (CV)
Map display of regional results and analysis of spatial pattern
Topic 7. Consistency Analysis of Vegetation Greening and Ecosystem Carbon Sequestration
Does greener vegetation mean enhanced carbon sequestration in ecosystems? -Enlightenment from long time series remote sensing products
Long-term NDVI change trend analysis
Long-term LAI change trend analysis
Long-term GPP change trend analysis
Comprehensive study and judgment of long-term NDVI/LAI/GPP change trends
Topic 8. Key parameters of grassland growth/biomass remote sensing estimation and trend analysis
Grassland LAI/coverage/biomass remote sensing estimation principle
Application of PROSAIL Radiative Transfer Model
Sensitivity Analysis of PROSAIL Model Parameters
Remote Sensing Retrieval of Key Parameters of Grassland Based on PROSAIL Model
Analysis of long-term grassland growth trend
