Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Granger Causality
2.2. Vegetation Products and Environmental Land Variables
2.3. Global Analysis with Google Earth Engine and Python Geospatial Libraries
2.4. Biome-Specific Analysis
3. Results
3.1. Global GC Maps
3.2. Köppen–Geiger Biome Specific Analysis
3.3. Main ELV Factors Causing Vegetation Dynamics
4. Discussion
4.1. Water (SM and P)-Caused Vegetation Anomalies
4.2. Energy (T and R)-Caused Vegetation Anomalies
4.3. Limitations and Opportunities for Future Improvements
5. Conclusions
- Water availability (i.e., SM and P) is a strong driver in arid areas, especially for the LAI, which is highly sensitive (0.43 for SM→LAI and 0.41 P→LAI cover a fraction of G-Caused pixel arid biomes).
- SM also causes the LAI on cold and polar biomes with fractions of 0.44 and 0.5, respectively.
- Ecosystems at higher latitudes with cold and polar biomes are driven mainly by R, although R is set to cause the melting of snow, driving soil moisture dynamics. Both on cold and polar biomes, G-Caused areas cover more than 40% of the biomes’ areas.
- T causality is evenly distributed amongst all biomes and VPs, with cover fractions of ∼0.1–0.2.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
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Analysis Product | Spatial Resolution | Temporal Granularity | Algorithm/Retrieval Approach | Sensor | Unit |
---|---|---|---|---|---|
LAI/FAPAR | 300 m | 10 Day | Neural networks trained with reflectance data | S-3 OLCI /PROBA-V | LAI: (m2/m2)/FAPAR: (−) |
NDVI | 300 m | 10 Day | BRDF-normalized, atmospherically corrected reflectances Further corrections for Sun-sensor geometry differences | S-3 OLCI /PROBA-V | (−) |
TROPOMI SIF(TROPOSIF) | 7 × 3.5 km | Daily | Infilling of Fraunhofer lines at 743–758 and 735–758 nm with fluorescence radiance | S5P | mW m sr |
Surface Variable | Definition | Unit |
---|---|---|
Soil Moisture (SM) | Volume of water in soil layer 2 (7–28 cm) of the ECMWF Integrated Forecasting System. | 1 (volume fraction) |
Precipitation (P) | Total daily precipitation sum. Accumulated liquid and frozen water, including rain and snow, that falls to the Earth’s surface. | meter (m) |
Temperature (T) | Temperature of air at 2 m above the surface. 2 m temperature is calculated by interpolating between the lowest model level and the Earth’s surface, taking into account the atmospheric conditions. | Kelvin (K) |
Shortwave solar radiation (R) | Amount of accumulated shortwave solar radiation (0.2–4 µm direct and diffuse) reaching the surface of the Earth. and the Earth’s surface, taking into account the atmospheric conditions. | J/m |
Time Window | 1 Year | 2 Year | 3 Year |
---|---|---|---|
GC significant p-value pixels | 4094 | 7713 | 8243 |
Increment compared to previous year | - | 88% | 7% |
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Kovács, D.D.; Amin, E.; Berger, K.; Reyes-Muñoz, P.; Verrelst, J. Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method. Remote Sens. 2023, 15, 4956. https://doi.org/10.3390/rs15204956
Kovács DD, Amin E, Berger K, Reyes-Muñoz P, Verrelst J. Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method. Remote Sensing. 2023; 15(20):4956. https://doi.org/10.3390/rs15204956
Chicago/Turabian StyleKovács, Dávid D., Eatidal Amin, Katja Berger, Pablo Reyes-Muñoz, and Jochem Verrelst. 2023. "Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method" Remote Sensing 15, no. 20: 4956. https://doi.org/10.3390/rs15204956
APA StyleKovács, D. D., Amin, E., Berger, K., Reyes-Muñoz, P., & Verrelst, J. (2023). Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method. Remote Sensing, 15(20), 4956. https://doi.org/10.3390/rs15204956