text stringlengths 0 820 |
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SMimaginar y |
VODreal |
VODimaginar y |
2011 2012 2013 2014 2015 2016 201700.020.040.060.08–0.8–0.40.40.8 |
0 |
Corr (ENSO4, SM) |
ENSO4 --> SM |
(a) (b) |
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100 |
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2021FIGURE 9. (continued ) (c) Convergent cross mapping. An example of the unbiased CCM application used in [88] to derive causal rela - |
tionships among the variables accounting for photosynthesis [the gross primary productivity (GPP) from FLUXCOM initiative (www |
.fluxcom.org)], temperature (T air from ERA Interim), and SM (from the European Space Agency’s climate change initiative [(v 2.0)]. Data |
cubes at 0.5° and eight-day spatial and temporal resolutions, respectively, spanning 2001–2002 were used. Reasonable spatial-causal |
patterns are observed for SM and T air on GPP; GPP drives T air mostly in cold ecosystems (probably due to changes in land surface albe - |
do, such as snow/ice to vegetation changes); SM is mostly controlled by T air, which partially drives evaporation in water-limited regions; |
and GPP dominates SM. (d) The additive noise model. Structural equation models in the form of an additive noise model using the |
kernels in [21] for hypothesis testing. Assessing cause-and-effect relationships is also possible when time is not involved. Here we rely |
on a look-up table generated by radiative transfer model (RTM), which gives the right direction of causation: state vectors (parameters) |
cause radiances. The algorithms accurately detect this from pairs of data and can be used for retrieval model-data intercomparison and |
RTM assessment. |
1 |
0.8 |
0.6 |
0.4 |
0.2 |
0True Positive Rate |
0 0.2 0.4 0.6 0.8 1 |
False-Positive-Rate |
(d)C (0.7667)" |
Cs(0.7953)" |
Photosynthesis |
Strength of Forcing Over SM |
50 |
–50 |
–150 –100 –50 05 0100 150Latitude (°)0GPP |
Temperature |
Longitude (°)Strength of Forcing Over Air Temperature |
50 |
–50 |
–150 –100–50 05 0100 150Latitude (°)0GPP |
SM |
Longitude (°) |
Strength of Forcing Over GPP |
50 |
–50 |
–150 –100 –50 05 0100 150Latitude (°)0Temperature |
SM |
Longitude (°)Air Temperature SM |
(c) |
Authorized licensed use limited to: ASU Library. Downloaded on March 07,2024 at 22:07:36 UTC from IEEE Xplore. Restrictions apply. |
101 |
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINEwidely used approach in Earth and climate sciences to |
quantitatively identify relationships between time series. It |
tests whether including past states of a variable, X, improves |
the prediction of an output variable, Y, more than consider - |
ing other covariates. GC is a linear test, but nonlinear (ker - |
nel) versions have also been proposed [ 85]. In [ 86], a gen - |
eralized kernel GC is presented, able to discover footprints |
of El Niño-Southern Oscillation (ENSO) on soil moisture |
(SM) and vegetation optical depth records [see Figure 9 (a)]. |
However, GC approaches have problems in nonstationary, |
nonlinear, and deterministic relationships, especially in |
dynamic systems with weak-to-moderate coupling. |
The second family considers nonlinear state-space meth - |
ods, such as convergent cross mapping (CCM) [ 87]. CCM |
attempts to address GC problems by reconstructing the |
variable’s state spaces, ( Mx, My), using time embeddings |
and concludes, based on XY", whether points on Mx can |
be predicted more accurately using nearest neighbors in My |
as more points are used for prediction. However, CCM is |
very sensitive to noise and time-series length. Recent works |
have included bootstrap resampling to alleviate such prob - |
lems and shown good results in identifying causal links |
in long global records of carbon and water fluxes [ 88] [see |
Figure 9 (b)]. |
The third family, collectively known as causal network |
learning algorithms , relies heavily on conditional-inde - |
pendence tests. Its methods iteratively remove the links |
between pairs of variables, ( X, Y), if they are found to be |
independently conditioned on any subset of the other vari - |
ables. The PC algorithm (named after its inventors Peter |
and Clark) allows us to identify parents and can be flexibly |
implemented with different kinds of conditional-indepen - |
dence tests, which can handle nonlinear dependencies and |
variables that are discrete or continuous and are univariate |
or multivariate. Finally, structural causal models (SCMs) |
are used when time is not involved or the sampling frequen - |
cy is too low. SCMs search for the causal direction within |
Markov-equivalent classes by exploiting the asymmetries |
between cause and effect. Additive noise models rely on the |
principle of independence between the cause and the gen - |
erating mechanism and have recently shown good results |
in remote sensing and geosciences in cases where time is |
not involved and only two variables are observed [ 21]. |
PERSPECTIVES |
Although the field of machine and deep learning has tra - |
ditionally progressed very rapidly, we observe that this is |
not the case in tackling the challenge of learning causal |
relationships from Earth observation data. The role that |
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